APPLICATION OF CONNECTED DOMINATING SET FOR ROUTING...

APPLICATION OF CONNECTED DOMINATING

SET FOR ROUTING IN MOBILE AD HOC AND

WIRELESS SENSOR NETWORKS

THESIS

Submitted by

RAMALAKSHMI R(Register No: 200907210)

in fulfillment for the award of the degree

of

DOCTOR OF PHILOSOPHY

DEPARTMENT OF COMPUTER SCIENCE ANDENGINEERING

KALASALINGAM UNIVERSITYANAND NAGAR, KRISHNANKOIL – 626 126

TAMILNADU, INDIA

AUGUST 2015

ABSTRACT

Wireless ad hoc networks are infra-structureless multi-hop networks

consists of mobile or wireless devices, which include Mobile Ad Hoc Networks

(MANETs), Mobile Opportunistic Networks (MON), Wireless Sensor Networks

(WSNs) and UnderWater Acoustic Sensor Networks (UWASN). The important

characteristics of these networks are: limited bandwidth and dynamic topology. In

these networks, energy is a precious resource since each node has a limited power

source. To extend the lifetime of these networks, it is essential to have an efficient

routing scheme.

A Connected Dominating Set (CDS) or Virtual Backbone (VB) is a subset

of nodes that is able to perform data communication tasks and to serve nodes that

are not in the backbone. A CDS can be selected as a communication layer, and

only the nodes in the CDS transmit data. It is greatly reducing the transmission

of redundant information, simplifying the topology of the network, saving the

energy for information gathering and filtering, routing and forwarding information

required.

In MANETs, the mobility of nodes may cause two problems: the source

node might have used the path that does not exist and the topology might have

changed during the forwarding of the packet. This results in failure of packet

delivery. Therefore, a stable path is needed for routing. In the first study, the design

of Stability based Energy-efficient Link-state Hybrid Routing (S-ELHR) protocol

is presented. Here, a stability metric is proposed and a localized algorithm is

implemented to construct a CDS, which provides a stable and sustainable topology

for routing. The proposed S-ELHR is a hybrid routing protocol, which uses relay

based broadcasting to discover the topology and source routing for data packet

transmission. It uses a route recovery mechanism to adopt to changing network

topology. The performance of S-ELHR is compared with OLSR and EE-OLSR

in terms of packet delivery ratio, end-to-end delay, control overhead and energy

consumption.

Disasters create emergency situations and a MANET can be deployed

for rescue operations. In the second study, the design of Weighted

Connected Dominating Set based Routing (Weighted-CDSR) protocol for ad hoc

communications in emergency and rescue scenarios is described. It is a reactive

routing protocol and the route discovery is operated over the CDS. A weight

metric is proposed, which uses stability, mobility and energy of nodes. A localized

algorithm is implemented for Maximum Weight Minimum Connected Dominating

Set (MWMCDS) construction. The performance of Weighed-CDSR is compared

with DSR, AODV, DYMO and Wu(degree)-CDSR in terms of packet delivery ratio,

end-to-end delay, control message overhead, and energy consumption.

v

In MON, mobile nodes are enabled with carry and forward mechanism to

communicate with each other even if there is no connecting path exist between

them. In the third study, the design of Ego-centrality Contact-duration based

Backbone Routing Protocol (BRP) is explained. It is an on-demand routing

protocol and message transmission is multi-hop through the CDS. The ego-

centrality metric identifies the important nodes and the contact-duration metric

selects the nodes that have more contact with other nodes in the network. A

localized algorithm is implemented based on accumulated node coverage condition

for backbone construction. The backbone nodes are enabled to buffer the message

when the network is disconnected. The performance of BRP is compared with

Adaptive-Routing, PRoPHET and CoMANDR in terms of packet delivery ratio,

end-to-end delay and number of forwarded messages.

A CDS based topology control in WSNs is a kind of hierarchical method to

ensure 1-coverage while reducing redundant connections. For applications related

to security and reliability, it is necessary to construct a fault-tolerant CDS that

continues to function during node or link failures. In the fourth study, the design of

k−Coverage Connected Dominating Set (k−CCDS) for connected area monitoring

is described. A Weighted Coverage Cost (WCC) is proposed and distributed

algorithms are implemented for k−CCDS construction. The concept of k−CCDS

is used to provide fault tolerance and routing flexibility, where non-dominating

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nodes are covered even if k− 1 dominating nodes are dead. The performance of

k−CCDS is compared with A3Cov in terms of CDS size, CDS lifetime, number of

uncovered nodes, coverage area and residual energy.

Energy efficiency becomes more critical and challenging in UWASN

because of the much higher transmission and receiving power consumption

of acoustic channel. In the fifth study, the design of CDS based Energy-

efficient Pressure(depth)-aware Routing Protocol for UWASN, named CDS-EPRP,

is explained. An Ant Colony Optimization (ACO) is applied to form CDS, based

on energy and depth. CDS-EPRP is an on-demand routing protocol, neither path

maintenance nor recovery is required. In order to save energy, the data forwarding

is multi-hop and the connectivity of CDS is also maintained to adapt to dynamic

network topology. The performance of CDS-EPRP is compared with VBF and

DBR in terms of packet delivery ratio, energy consumption, and end-to-end delay.

This thesis focuses on application of CDS for routing in different ad hoc

and wireless networks. Here, a communication network is modeled as a graph and

a weight metric is assigned to each node and/or communication link. This thesis

proposes localized and distributed algorithms for CDS construction based on the

weight metric. The proposed protocols show better performance in packet delivery

ratio, end-to-end delay, control overhead and energy consumption.

vii

ACKNOWLEDGEMENT

First of all, I thank Almighty God for His abundant blessings.

I express my heartfelt gratitude to Shri.K.Sridharan, Chancellor,

Kalasalingam University, for providing me with all the necessary facilities

for the research. I place on record, my sincere thanks to respected

Dr.S.Saravanasankar, Vice-Chancellor and Dr.V.Vasudevan, Registrar, for their

continuous encouragement. I wish to express my thanks to Dr.D.Devaraj,

Dean(Academics) and Head of the Department Dr.P.Deepalakshmi, Associate

Professor, for their guidance throughout this work.

I express my sincere thanks to my research supervisor,

Dr.S.Radhakrishnan, Senior Professor, Department of CSE for the constant

encouragement and constructive ideas he provided, without which this research

would never have been possible. I would like to thank him for encouraging my

research and for guiding me to grow as a research scientist. His advice on both

research as well as on my career have been invaluable. My special thanks are due

to Dr.S.Arumugam, Director, n-CARDMATH, for his help in understanding the

concepts of graph theory.

I would like to express my deepest gratitude to my mother, my husband, and

my kids for their understandings, sacrifices and supports. I also thank everyone for

their direct or indirect support to complete this research work.

R. Ramalakshmi

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TABLE OF CONTENTS

CHAPTER NO. TITLE PAGE NO.

ABSTRACT iv

LIST OF FIGURES xv

LIST OF TABLES xviii

LIST OF ABBREVIATIONS xix

LIST OF SYMBOLS xxiv

1 INTRODUCTION 1

1.1 MOBILE AD HOC NETWORKS (MANETS) 1

1.1.1 Routing in MANETs 3

1.2 WIRELESS SENSOR NETWORKS (WSN) 5

1.2.1 Routing in WSN 7

1.3 CONNECTED DOMINATING SET (CDS) 8

1.3.1 Definitions 8

1.3.2 Algorithms for CDS construction 10

1.3.3 Applications of CDS 11

1.4 MOTIVATION 12

1.5 OBJECTIVES 14

1.6 CONTRIBUTIONS OF THIS THESIS 16

1.7 ORGNAIZATION OF THE THESIS 18

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2 LITERATURE SURVEY 22

2.1 ROUTING PROTOCOLS IN MANET 22

2.2 CDS BASED BROADCASTING AND ROUTING IN MANETS 28

2.3 STABILITY AND ENERGY EFFICIENT ROUTING IN MANETS 37

2.4 ROUTING IN MOBILE OPPORTUNISTIC NETWORKS 42

2.5 CDS BASED ROUTING AND SCHEDULING IN WSN 50

2.6 CDS BASED COVERAGE IN WSN 54

2.7 ROUTING IN UWASN 60

2.8 OPTIMIZATION TECHNIQUES FOR CDS 65

2.9 SUMMARY 67

3 DESIGN OF STABILITY BASED ENERGY EFFICIENT LINK

STATE HYBRID ROUTING PROTOCOL FOR MOBILE AD

HOC NETWORKS 68

3.1 INTRODUCTION 68

3.1.1 Stability based Routing in MANETs 69

3.2 STABILITY BASED ENERGY-EFFICIENT LINK-STATE

HYBRID ROUTING (S-ELHR) 70

3.2.1 Network Model and Problem Statement 71

3.2.2 Stability Metric (SM) Calculation 72

3.2.2.1 Computation of Willingness 72

3.2.2.2 Link Connectivity Index (LCI) Metric 73

3.2.2.3 Energy Weight (EW) Metric 74

3.2.2.4 Degree Weight (DW) Metric 74

3.2.3 Algorithm for Stable CDS Construction 74

3.2.4 Routing in S-ELHR 76

3.2.4.1 Topology Discovery 76

x

3.2.4.2 Route Computation and Route Recovery 77

3.3 SIMULATION STUDY 78

3.3.1 Simulation Parameters 78

3.3.2 Protocols used for comparison 80

3.3.3 Results and Discussion 81

3.4 CHAPTER SUMMARY 91

4 DESIGN OF WEIGHTED DOMINATING SET BASED ROUTING

PROTOCOL FOR AD HOC COMMUNICATIONS IN

EMERGENCY AND RESCUE SCENARIOS 92

4.1 INTRODUCTION 92

4.2 WEIGHTED CDS BASED ROUTING (WEIGHTED-CDSR) 93

4.2.1 Network Model and Problem Statement 94

4.2.2 Weight Calculation 94

4.2.2.1 Link Stability Metric 95

4.2.2.2 Mobility Metric 95

4.2.2.3 Energy Metric 96

4.2.3 Algorithm for Maximum Weighted CDS Construction 96

4.2.4 Routing in Weighted-CDSR 99

4.2.4.1 Route discovery and Maintenance 99






xi

5 DESIGN OF AN EGO CENTRALITY AND CONTACT

DURATION BASED BACKBONE ROUTING PROTOCOL FOR

MOBILE OPPORTUNISTIC NETWORKS 117

5.1 INTRODUCTION 117

5.1.1 Social-aware Routing in Mobile Opportunistic Networks 119

5.2 BACKBONE BASED ROUTING PROTOCOL (BRP) 121

5.2.1 Network model and Problem Statement 121

5.2.2 Ego-Centrality and Contact-Duration based Connected

Dominating Set (ECCDS) 122

5.2.2.1 Ego Centrality Calculation 122

5.2.2.2 Calculation of Average Inter-Contact time 123

5.2.2.3 Algorithm for ECCDS Construction 123

5.2.3 Routing in BRP 125

5.2.3.1 Message Forwarding 125

5.2.3.2 Forwarding of Buffered Messages 127






6 DESIGN OF CONNECTED K−COVERAGE TOPOLOGY

CONTROL FOR AREA MONITORING IN WIRELESS SENSOR

NETWORKS 143


6.1.1 CDS for Topology Control in WSN 144

6.2 k−Coverage Connected Dominating Set (k−CCDS) 145

xii


6.2.2 Weighted Coverage Cost Calculation 146

6.2.3 Distributed k−CCDS Construction Algorithm 148

6.2.3.1 CDS Construction 148

6.2.3.2 k−CCDS Construction 152






7 DESIGN OF CONNECTED DOMINATING SET BASED

ENERGY EFFICIENT PRESSURE AWARE ROUTING FOR

UNDERWATER ACOUSTIC SENSOR NETWORKS 163


7.1.1 Energy Efficient Routing in UWASN 165

7.2 CDS BASED ENERGY-EFFICIENT PRESSURE-AWARE

ROUTING PROTOCOL (CDS-EPRP) 167


7.2.2 ACO based CDS Construction 169

7.2.3 Routing in CDS-EPRP 171

7.2.3.1 Data Packet forwarding 171

7.2.3.2 Connectivity Maintenance of CDS 172





xiii


8 CONCLUSION AND FUTURE WORK 186

8.1 SUMMARY OF CONTRIBUTIONS 186

8.2 CONCLUSION 189

8.3 SCOPE FOR FUTURE WORK 190

REFERENCES 191

LIST OF PUBLICATIONS 210

VITAE 212

xiv

LIST OF FIGURES

3.1 No. of Nodes vs Percentage of Relay Nodes 82

3.2 No. of Nodes vs Average Path Length in Hops 83

3.3 No. of Nodes vs Topology Message Overhead 84

3.4 Mobility vs Topology Message Overhead 84

3.5 No. of Nodes vs Average Energy Consumption 85

3.6 Mobility vs Average Energy Consumption 86

3.7 No. of Nodes vs Control Overhead per Data Packet 87

3.8 Mobility vs Control Overhead per Data Packet 87

3.9 No. of Nodes vs Packet Delivery Ratio 88

3.10 Mobility vs Packet Delivery Ratio 89

3.11 No. of Nodes vs End-to-End Delay 90

3.12 Mobility vs End-to-End Delay 90

4.1 No. of Nodes vs CDS Size 107

4.2 No. of Nodes vs Average Route Length 108

4.3 Mobility vs Routing Overhead 108

4.4 Mobility vs Packet Delivery Ratio 109

4.5 Mobility vs Energy Consumption 110

4.6 No. of Nodes vs Routing Overhead 111



4.9 No.of Nodes vs Energy Consumption 114

4.10 No. of Traffic Sources vs Packet Delivery Ratio 115

xv

5.1 Accumulated Node Coverage Condition 124

5.2 No. of Nodes vs Message Delivery Ratio 132

5.3 Mobility vs Message Delivery Ratio 132

5.4 Message Lifetime vs Message Delivery Ratio 133


5.6 Mobility vs End-to-End Delay 135

5.7 Message Lifetime vs End-to-End Delay 136

5.8 No. of Nodes vs Hop Count 137

5.9 Mobility vs Hop Count 137

5.10 Message Lifetime vs Hop Count 138

5.11 No. of Nodes vs No. of Forwarded Messages 139

5.12 Mobility vs No. of Forwarded Messages 140

5.13 Message Lifetime vs No. of Forwarded Messages 140

5.14 No. of Messages vs No. of Forwarded Messages 141

6.1 Sensor’s Radius and Coverage Redundancy 147

6.2 Weighted Coverage Cost 148

6.3 No. of Nodes vs Average CDS Size 155

6.4 Communication Range vs Average CDS size 156

6.5 No. of Nodes vs CDS Lifetime 157

6.6 No. of Nodes vs Uncovered Nodes 157

6.7 No. of Nodes vs Residual Energy 158

6.8 No. of Nodes vs Coverage Area 159

6.9 No. of Nodes vs Convergence Time 160

6.10 Communication Range vs Convergence Time 161

7.1 CDS-EPRP for UWASN 167

7.2 No. of Nodes vs No. of Dominating Nodes with dth 178

xvi

7.3 No. of Nodes vs Packet Delivery Ratio with varying Sinks 178

7.4 No. of Nodes vs End-to-End Delay with varying Sinks 179



7.7 No. of Nodes vs Energy consumption 182

7.8 Offered Load vs Packet Delivery Ratio 183

7.9 Offered Load vs End-to-end Delay 183

7.10 Offered Load vs Energy Consumption 184

xvii

LIST OF TABLES

3.1 Willingness Calculation in S-ELHR 73

3.2 S-ELHR Simulation Parameters 79

4.1 Route Discovery Packet Formats 99

4.2 Weighted-CDSR Simulation Parameters 103

5.1 BRP Simulation Parameters 128

6.1 k−CCDS Simulation Parameters 153

7.1 CDS-EPRP Simulation Parameters 175

xviii

LIST OF ABBREVIATIONS

ABP - Adaptive Backbone Protocol

ABPL - Average Backbone Path Length

ACO - Ant Colony Optimization

ACO-TS - ACO with Tournament Selection

AHH-VBF - Adaptive Hop-by-Hop Vector-based Forwarding

ANCC - Accumulated Node Coverage Condition

AoA - Angle of Arrival

AODV - Ad-hoc On-demand Distance Vector

ASBIT - Adaptive Spraying Based Inter-contact Time

BDMST - Bounded Diameter Minimum Spanning Tree

BRP - Backbone Routing Protocol

CADS - Connected Area Dominating Set

CBR - Constant Bit Rate

CCDS - Coverage/Clique Connected Dominating Set

CDMA - Code Division Multiple Access

CDP - Connected Domatic Problem

CDS - Connected Dominating Set

CDS-EPRP - CDS based Energy-Efficient Pressure-Aware Routing Protocol

CEDAR - Core Extraction Distributed Ad-hoc Routing

CH - Cluster Head

CLaB - Cluster-Label-based Backbones

CMFDS - Connected Message Ferry Dominating Set

CNM - Color Notification Message

xix

CoMANDR - Combined MANET-DTN Routing

CR - Coverage Redundancy

DBR - Depth Based Routing

DCDS - Degree-Constrained minimum-weight CDS

DP - Domatic Partition

DS - Dominating Set

DSDV - Destination Sequenced Distance Vector

DSR - Dynamic Source Routing

DSSS - Direct Sequence Spread Spectrum

DTLST - Delay Tolerant Link State Routing

DTN - Delay Tolerant Network

DTP - Dominating Tree Problem

DUCS - Distributed Underwater Clustering Scheme

DYMO - Dynamic MANET On-demand

E-PULRP - Energy optimized Path Unaware Layered Routing Protocol

EAP - Expected Allocation Probability

ECCDS - Ego-Centrality Contact-Duration Connected Dominating Set

ECSS - Energy Conservation Self Scheduling

EEDTC - Energy Efficient Dominating Tree Construction

EE-OLSR - Energy Efficient Optimized Link State Routing

ELDT - Expected Link Duration Time

FDDS - Fast Distributed Dominating Set

FND - Fidelity of NoDes

GPS - Global Positioning System

H2-DAB - Hop-by-Hop-Dynamic Addressing Based

HH-VBF - Hop-by-hop Vector Based Forwarding

ICT - Intermittently Connected Network

xx

IETF - Internet Engineering Task Force

IS - Independent Set

L2-ABF - Layer by layer Angle-Based Flooding

LBCDS - Load Balanced Connected Dominating Set

LBVB - Load Balanced Virtual Backbone

LCI - Link Connectivity Index

LET - Link Expiration Time

MAC - Medium Access Control

MACA - Mobility Adaptive Clustering Algorithm

MANET - Mobile Ad-hoc Network

MCDS - Minimum Connected Dominating Set

MDMIS - MinMax Degree Maximal Independent Set

MDS - Minimal Dominating Set

MEMCDS - Maximum Energy Minimum CDS

MEWR - Modified Energy Weight Routing

MIS - Maximal Independent Set

MLBS - Maximum Lifetime Backbone Scheduling

MON - Mobile Opportunistic Network

MPR - Multi Point Relay

MP-OLSR - MultiPath Optimized Link-state Routing

MSE-CDS - Maximum Spectral-efficient Connected Dominating Set

MVBA - MinMax Valid-degree non-Backbone Allocation

MWDS - Minimum Weighted Dominating Set

MWMCDS - Maximum Weighted Minimum Connected Dominating Set

NCR - Neighbor-aware Contention Resolution

NS-2 - Network Simulator version 2

OLSR - Optimized Link State Routing

xxi

PMAR - Power and Mobility Aware Routing

PROPHET - Probabilistic Routing Protocol using History of Encounters & Transitivity

QASP - QoS Aware Stable Path

QELAR - Q-learning-based Routing

QoS - Quality of Service

RAPLF - Routing with Adaptive Path and Limited Flooding

RDS-MPR - Realistic Dominating Set MPR

RERR - Route Error

RET - Route Expiration Time

RETC - Energy Efficient Topology Control

RF - Radio Frequency

RMQR - Reliable Multi-path QoS Routing

RREP - Route Reply

RREQ - Route Request

RSEA - Route Stability and Energy Aware

RRP - Route Reach Packet

RSP - Route Search Packet

RSQR - Route Stability based QoS Routing

RSS - Received Signal Strength

S-ELHR - Stability based Energy Efficient Hybrid Routing

SLABR - Social Link Awareness Based Routing

SM - Stability Metric

SND - Stability of NoDes

SOB-T - Self Organized Backbone Tree

SOB-M - Self Organized Backbone Marking

SONR - Social Opportunistic Network Routing

ST-OLSR - Stability Optimized Link State Routing

xxii

SWORP - Stable Weight-based On-demand Routing Protocol

TC - Topology Control

TCDS - Two-hop Connected Dominating Set

TDMA - Time Division Multiple Access

THP - Three-hop Horizon Pruning

TMPO - Topology Management by Priority Ordering

TTL - Time To Live

UWASN - UnderWater Acoustic Sensor Network

VAPR - Void-aware Pressure Routing

VB - Virtual Backbone

VBF - Vector Based Forwarding

UBG - Unit Ball Graph

VBS - Virtual Backbone Scheduling

VGA - Virtual Grid Architecture

WCC - Weighted Coverage Cost

WSN - Wireless Sensor Network

xxiii

LIST OF SYMBOLS

V - Set of nodes in a network

E - Set of communication links in a network

|V | - Size of set V

Nu1 - One-hop neighbors set of node u (Nu

1 = {v | (u,v) ∈ E})

Nu2 - Two-hop neighbors set of node u (Nu

2 = {w | v ∈ Nu1 ∧w /∈ Nu

1 ∧ (v,w) ∈ E})

Nt1(u) - One-hop neighbors set of u, Nu

1 at time t

Nt+11 (u) - One-hop neighbors set of u, Nu

1 at time t +1

|Nu1 | - Number of one-hop neighbors of u

Nuw - One-hop neighbors set of node u with Willingness DEFAULT or HIGH

Relay(u) - Subset of Nu1 chosen to relay

NC(u) - Subset of Nu2 not covered by nodes in Relay(u)

Euinit - Initial energy of node u

Eurm - Residual energy of node u

LCI(i, j) - Link connectivity index between the two nodes i and j

SM(i, j) - Stability metric of a node i with its neighbor j ( j ∈ Ni1)

γMOBmin - Minimum mobility factor with value 0.01

Ruv - Received signal strength (RSS) between node u and v

∆Ruv - Variation of RSS between node u and v

γLSu - Link stability metric of a node u with its neighbors

γMOBu - Mobility metric of a node u

γENu - Energy metric of a Node u

WT u - Weight of a node u

ϑu - Ego centrality value of node u

xxiv

(u,v) - Communication link between node u and v ((u,v) ∈ E)

∆uv - Inter-contact time of node u with v during an encounter

∆′uv - The sum of the inter-contact time of node u to v before last encounter

σuv - Sum of inter-contact times between the nodes u and v

χuv - Number of encounters between the nodes u and v

ξuv - The average inter-contact time of the link (u,v)

σPi - Sum of the average inter-contact time of path Pi

ϖuv - Average of inter-contact time of paths between nodes u and v

tbuv - The start time of contact of node u with v

teuv - The end time of contact of node u with v

Rs - Sensing radius

Rc - Communication radius

A(si) - Sensing area of sensor si, is a disk of radius Rs, centered at location of si

d(u,v) - Euclidean distance between sensor nodes u and v

Nsi1 - Communication neighbors of sensor node si, Nsi

1 = {s j|d(si,s j)≤ Rc}

CR(si) - Coverage redundancy of sensor node si, CR(si) = {s j|d(si,s j)≤ Rs}

Esiinit - Initial energy of sensor node si

Esir - Residual energy of sensor node si

Esitot - Total energy level of the sensing neighbors of sensor node si

WCC(si) - Weighted Coverage Cost of sensor node si

Eth - Energy threshold

RSSud - Distance of u based on RSS

dth - Depth threshold

du - Depth of node u

d p(u,v) - Difference of depths between node u and node v, ( d p(u,v) = du−dv )

Ns - Number of surface sinks

Na - Number of ants

xxv

τu - Pheromone at node u

τ0 - Initial pheromone

ηu - Energy weight of node u

Nuf - Feasible neighborhood of node u (Nu

f = {v | d p(u,v)> dth,∀v ∈ Nu1})

NuD - Dominating neighbors of node u (Nu

D = {x | dominating(x) = true,∀x ∈ Nu1})

NuE - Dominatee neighbors of node u (Nu

E = {x | dominating(x) = f alse,∀x∈Nu1})

xxvi

CHAPTER 1

INTRODUCTION

1.1 MOBILE AD HOC NETWORKS (MANETS)

Mobile Ad-hoc networks (MANETs) are self-organizing and self-

configuring multi-hop wireless networks, where the structure of the network

changes dynamically (Macker et al. [97]). It consists of wireless devices that

interact among each other by means of wireless communications. The main

objective of MANET is to enable communication between senders and receivers

in a network where nodes are mobile and may not be within direct wireless

transmission range of each other. This type of networks does not relay on a fixed

infrastructure and any node can act as traffic source, destination or forwarder.

MANET is very flexible and resilient to node failures due to distributed nature.

Hence, MANETs are well suited to applications where rapid deployment and

dynamic reconfiguration are necessary. Some of the potential applications for

MANETs are [72, 98]:

- Military application: MANETs satisfy the needs of military applications

like battlefield survivability. Here, the MANET is deployed with wireless

electronic devices carried in soldiers, tanks, airplane and other military

equipment, to support communication among them in order to collaboratively

achieve military goals, since there is no any pre-defined infrastructure and

connectivity in battlefield environments.

1

2

- Emergency services: Each year natural disasters destroy people’s lives around

the world. As network applications will become increasingly important for

emergency services, it will be important to find ways to enable the operations

of networks even when infrastructure elements have been disabled as part

of the effects of a disaster. For disaster recovery, field agents wish to

communicate their findings regarding, for example, environmental hazards

or survivors to other field agents as well as to a command post.

Delay Tolerant Networks (DTNs) were developed to allow communication

in scenarios where fixed infrastructure is not available and existing IP and

GSM/UMTS network protocols are unsuitable. Mobile Opportunistic Networks

(MONs) or Intermittently Connected Networks (ICT) are kind of DTN and highly

dynamic. In these networks, when nodes move away or turn off their power to

conserve energy, links may be disrupted or shut down periodically. These events

result in intermittent connectivity [77]. In such scenarios, where nodes often create

sparse network topologies and the contacts between them are intermittent, MONs

use a store-carry-forward strategy to allow communication when a path through the

network is not reliable, due to frequent disconnections. A node receiving a packet

from one of its contacts can buffer the message, carry it while moving, and forward

it to the encountered nodes which are at least as useful as itself in terms of delivery

[106].

Each node in MON after receiving a message, exploits local knowledge to

decide which is the best next hop, among its current neighbors, for the message to

reach the eventual packet destination. When no forwarding opportunity exists (no

other nodes are in the transmission range, etc), the nodes store the message and

waits for further contact opportunity with other devices to forward the information

[114]. Applications of MON include:

3

- Vehicular networks: provide network and Internet connectivity to mobile

users in vehicles. The network is constituted by hot spots that are placed

along the roads providing thus intermittent connectivity to the users that can

connect within proximity.

- Mobile sensor networks: for environmental monitoring, e.g, Zebranet, which

is a wireless networking architecture designed to support wildlife tracking for

biology research. In ZebraNet, the network is constituted by sensor collars

that are attached to zebras, which log movement patterns of the zebras, and

by researchers base stations that are mounted on cars which move around

sporadically. When two zebras meet, the corresponding sensors exchange

collected data for a potential data delivery back to researchers base-stations.

1.1.1 Routing in MANETs

Routing is a fundamental issue for any network and routing protocols are

considered to be in charge of discovering and maintaining the routes. It is a

challenging task to find and maintain routing path in MANET with sudden topology

changes due to nodes mobility. Several routing protocols have been proposed

for MANETs, they are classified into proactive, reactive and hybrid according to

routing strategy. A survey of routing protocols in ad hoc networks was given by

Boukerche et al. [22].

Proactive routing protocols [33, 40] attempt to keep the freshest route

information from the whole network. These protocols use several tables to store

the messages and periodically update the tables, in order to maintain fresh route

information throughout the entire network. A different approach from proactive

routing is the reactive routing protocols, or on-demand protocols [73, 116].

Besides local links, these protocols initiate a flooding route discovery when

4

requiring sending data to a specific destination and do not maintain the route

information periodically. They usually have two mechanisms, route discovery

and route maintenance, to create and maintain a route efficiently to prevent highly

overloading the whole network. Unlike proactive routing protocols, these protocols

can save the resource (e.g, node’s battery and network bandwidth) but not always

transmit data immediately. The last is hybrid routing which incorporates merits of

proactive and reactive routing. These protocols are designed to increase scalability

by allowing nodes with close proximity to work together. They can be formed by

some particular backbone to reduce the route discovery overheads and also by a

single point failure. Hybrid routing protocols can exhibit a better performance than

proactive and reactive schemes can. However, the memory requirement is greater

and the path to destination may be suboptimal.

In MONs, popular ad hoc routing protocols such as AODV [116] and DSR

[73] fail to establish routes. This is due to these protocols trying to first establish

a complete route and then, after the route has been established, forward the actual

data. However, when instantaneous end-to-end paths are difficult or impossible

to establish, routing protocols must take a “store and forward” approach, where

data is incrementally moved and stored throughout the network in hope that it

will eventually reach its destination. The routing in these networks are classified

into: Flooding based routing, History or prediction based routing and Sociality-

aware routing, based on whether the future movement and connection status of the

network is known or predictable [23, 106].

The basic concept of flooding based routing [159] is to flood the packets,

a node copies its message to all the nodes that come in contact with it, provided

the recipient node does not have a copy of it already. Several methods have been

proposed to control the flooding. Most of the routing strategies were designed with

5

the aim to avoid flooding [28, 136, 148]. Even when flooding is adopted, care has

been taken to conserve the resources. Some approaches also take care to free the

buffer, after the message has been delivered. History or prediction based routing,

utilizes the history of encounters between nodes, to make a more informed routing

decision. Intuitively, a node that has encountered the destination many times, is

likely to encounter the destination again. This is the principle behind history based

routing protocols [21, 91, 94, 126]. Sociality-aware routing [29, 36, 95, 136],

works on two important observations from society: people with closer relationship

tend to reside in communities and people within a community may have different

popularity. As such, the increasingly “popular” or “central” nodes are more

probably chosen as carriers to relay messages between disconnected communities,

until a node belonging to the same community with the destination is reached.

1.2 WIRELESS SENSOR NETWORKS (WSN)

Wireless Sensor Networks (WSN) contain a number of sensor nodes

dispersed randomly onto a target field [4]. With the advance in microelectronic

technology, sensor nodes are developed with small size, low cost, and low power

consumption, communicate via radio frequency over short distances. Each sensor

might be deployed to collect one kind of data. It is capable of collecting the data

and to broadcast the selected data to the next hop or to the closest base station. The

particular property that differentiates a WSN from a MANET is the convergecast

(many-to-one) service mode. Therefore, the communication protocol developed

for WSNs much be energy-efficient. The main goal in WSN research is to find

a topology for a WSN which can both save energy usage to prolong a network

lifetime and enhance the data transfer. WSNs are coming into wider use today and

in the future. Some of the applications of WSN are:

6

- Homeland Security: Motion sensors detect and report the possible incursions

on our border areas.

- Military: Robots are configured as a sensor network to scan the whole battle

filed.

- Healthcare: Patients can carry a small sensor which reports to the doctor via

a WSN.

- Environmental Monitoring: WSN helps to gather data for some species

studies or report weather in a dangerous area, such as near a volcano.

UnderWater Acoustic Sensor Networks (UWASN) consist of underwater

sensors that are deployed to perform collaborative monitoring tasks over a given

area [146, 179]. UWASN shares many properties with terrestrial sensor networks

such as the large number of nodes and energy issues, still these are different in many

aspects from terrestrial sensor technology. Communications in UWASN have to be

done through acoustic channels, because electromagnetic radio signals attenuate

quickly in water. The speed of sound in water is five-order slower than the speed

of light, which brings long propagation and end-to-end delay. The bandwidth of an

acoustic channel is low and the error rate is high. Most underwater sensor nodes,

except some fixed nodes equipped on surface level buoys have low or medium

mobility (move up to 1-3 m/sec) owing to water currents and other underwater

activities [2, 63]. UWASN can be used in a wide spectrum of aquatic applications,

such as oceanographic data collection, pollution monitoring, offshore exploration,

disaster prevention and coastline surveillance [3].

7

1.2.1 Routing in WSN

The main task of sensor networks is to monitor and detect events, perform

quick local data processing and then transmit data to a base station. Based on

this main task, power consumption can be further divided into three domains:

Coverage, Communication and Data Processing.

The coverage problem is important in WSN. The goal is to have each

location within the sensing range of at least one sensor. The coverage problem also

reflects the quality of service that can be provided by a particular sensor network.

With the energy constraint, devising a method to prolong network lifetime while

successfully handling the coverage tasks becomes very important. In WSN, sensor

nodes are deployed densely. Therefore, it is possible to turn some sensor off while

the network is still able to handle its tasks [9, 10, 124, 129, 130, 156, 189, 198].

Communication protocols find an energy efficient routes based on the available

power in the nodes or the energy required for transmission in the links along the

nodes. The route that consumes minimum energy to transmit the data packets

between base station and the sensor nodes is preferred. To use this technique, one

must design an algorithm to find a minimum energy consumption route [83, 135].

In UWASN, because of the high attenuation of long range communication,

multi-hop relay is a common scheme to reduce energy consumption in the data

transmission process. When a node has a packet to transmit to the sink, this

packet will be routed through some intermediate nodes in the multi-hop relay

process. Because, the underwater sensor nodes will move due to the water current,

the network topology changes frequently. The routing protocols designed for

terrestrial wireless sensor network are not suitable for UWASN because the node

mobility is not considered in these protocols. These protocols usually require

8

the processes of path construction, maintenance and recovery. However, these

processes are very expensive in high dynamic UWASN [14, 53]. The underwater

routing protocols flood the packets to the sink with geographic information

[6, 13, 32, 42, 55, 68, 110, 111, 119, 161, 180, 183].

1.3 CONNECTED DOMINATING SET (CDS)

A simple graph G = (V,E) can be used to represent a MANET or WSN,

where V represents a set of mobile or sensor nodes and E represents a set of

communication links between the nodes. An edge (u,v) indicates that in a

particular time, both nodes u and v are within their transmission range, hence,

connections of nodes are based on geographic distances among them. The topology

of this type of graphs vary over time due to node mobility.

1.3.1 Definitions

This section provides some preliminary definitions that are relevant to the

understanding of the rest of the chapters.

Definition 1.1. Graph: It is an ordered pair G = (V,E) comprising a set V of

vertices or nodes together with a set E of edges or links, which are 2-element

subsets of V .

Definition 1.2. Undirected Graph: A graph G = (V,E) is an undirected graph in

which edges have no orientation. The edge (a,b) is identical to the edge (b,a), i.e.,

they are not ordered pairs.

Definition 1.3. Connected Graph: In an undirected graph G = (V,E), two vertices

u and v are called connected if G contains a path from u to v. Otherwise, they are

9

called disconnected. A graph is called connected if every pair of distinct vertices

in the graph is connected; otherwise, it is called disconnected.

Definition 1.4. Weighted Graph: A graph G = (V,E) is a weighted graph if a

number (weight) is assigned to each edge and/or vertices. Such weights might

represent, for example, costs, lengths or capacities, etc. depending on the problem

at hand.

Definition 1.5. k-connected Graph : A graph G = (V,E) is said to be k−vertex

connected or k−connected if for each pair of vertices there exists at least k mutually

independent paths connecting them. In other words, G is still connected even after

the removal of any k−1 vertices from G.

Definition 1.6. Dominating Set (DS): For a given graph G = (V,E), a DS is a

subset D⊆V , such that for every vertex v ∈V , either v ∈ D, or v has a neighbor in

D.

Definition 1.7. Connected Dominating Set (CDS): For a given graph G = (V,E), a

CDS is a subset D⊆V such that D is a DS and the graph induced by D is connected.

The nodes in a CDS are called dominators, the others are called dominatees.

Definition 1.8. Maximal Independent Set (MIS): For a given graph G = (V,E), an

Independent Set (IS) is a subset of nodes U ⊆ V , such that no two nodes in U are

adjacent (ie., ∀(x,y) ∈U | (x,y) /∈ E). An IS is maximal, if no node can be added

without violating independence.

Definition 1.9. Multi-Point Relay(MPR): For a given a graph G = (V,E) and a

node v ∈ V , let Nv1 and Nv

2 represent the set of 1-hop and 2-hop neighbors of v,

respectively. MPR asks for a minimum size subset of Nv1 such that Nv

2 is covered

by MPR (i.e., MPR(u) = {v|v ∈ Nu1} such that Nu

2 =⋃

v∈MPR(u)Nv1).

10

Definition 1.10. Maximum Degree ∆: Let G = (V,E) be a graph. For a node v∈V ,

d(v) denotes the degree of v and Nv1 denotes the neighbor set of v. Nv

1 = {u | (u,v)∈

E} and d(v) =| Nv1 |. The maximum node degree of G is, ∆ = max{d(v) | v ∈V}.

The problem of finding the MCDS of a given graph is known to be NP-

complete. Therefore, only approximation algorithms running in polynomial-time

are practical for wireless ad hoc networks.

1.3.2 Algorithms for CDS construction

The existing CDS construction techniques for wireless ad hoc networks

can be classified into three categories, based on the network information they use:

centralized algorithms, distributed algorithms and localized algorithms. The CDS

construction algorithms are summarized in [20, 190].

Centralized algorithms [37, 45, 56, 81, 131], determine a CDS based on

global information. These algorithms usually have the best performance guarantee

and the minimal average CDS size. The major drawback are high overhead and

slow convergence. Collecting global information incurs large message cost and

high delay, which make centralized algorithms less attractive in ad hoc networks

that do not have centralized control.

Distributed algorithms [8, 24, 30, 71, 89, 132, 133, 151, 152, 162, 171,

193], do not need any geometric or topological information. Nodes exchange

hello messages to identify their neighbors. The computation is partitioned into

rounds, where the nodes receive the messages sent in the previous round, execute

local computations and send messages to the neighbors in the next round. All the

distributed algorithms for CDS construction require only local information and a

11

constant number of iterative rounds of message exchanges among the neighboring

hosts. These algorithms are either prune-based or MIS based. In prune-based

algorithms, all nodes are CDS nodes at first, and then nodes become dominatee

according to some rules. The idea behind MIS-based algorithms is to find a

Maximal Independent Set (MIS) first, then find connectors to form a CDS. The

MIS-based algorithms are further can be divided into single initiator and multiple

initiator. For the single initiator algorithms, a unique initiator is elected and become

a CDS node firstly. Then CDS nodes are picked up according to their position

in this tree. Actually, in this kind of algorithms, a tree is built implicitly or

explicitly. In multiple initiator algorithms, many nodes claim themselves CDS

nodes simultaneously at first based on their local information.

Localized Algorithms [1, 34, 35, 59, 92, 107, 123], are distributed

algorithms, where each node determines its status based on its h−hop information

only. A localized algorithm converges in O(1) rounds, which makes it very robust

in dynamic networks such as MANET and WSN.

1.3.3 Applications of CDS

The communication methods and network topology maintenance are

challenging in MANETs and WSN, as there is no fixed or pre-defined

infrastructure. Inspired by the physical backbone in wired networks, many

researchers have been working on creating an effective virtual backbone in these

networks. It is possible to construct a virtual backbone (VB) by using a CDS. A

VB is a subset of nodes that is able to perform data communication tasks and to

serve nodes that are not in the backbone. It has been applied for the following

applications:

12

Multicasting/broadcasting [12, 16–18, 87, 93, 105, 107, 123, 135, 141,

147, 152, 174]: Broadcasting is frequently used in on demand routing protocols

for route discovery. By using only CDS nodes to forward broadcast packets, full

delivery is guaranteed while the excessive broadcast redundancy can be avoided.

Routing [5, 16, 38, 39, 48, 95, 143, 144, 172, 186]: By using only nodes in

the CDS as routers, non-CDS nodes do not maintain a routing table. With the help

of CDS, routing is easier and can adapt quickly to network topology changes. In

addition, using the CDS can reduce the traffic during communication and simplify

the connectivity management.

Energy Efficient Scheduling [170, 172, 174, 198]: By making non-CDS

nodes into periodical sleep mode, the energy consumption is greatly reduced while

network connectivity will be maintained by CDS nodes.

Topology Control [26, 125, 129, 156, 167]: In densely deployed sensor

networks, the node coverage of a CDS is a good approximation to provide reduced

topology for area coverage. The deployment area is within the sensing range of

CDS nodes with high probability.

1.4 MOTIVATION

The mobility of nodes cause two problems in MANET: the source node

might use the links that do not exist and topology might change during the

forwarding of the packet. This will affect both the quality of the selected paths

and their durability. Thus, the route selection process should also consider the link

stability criterion (i.e. links durability), which allows to maintain the characteristics

of the selected paths.

13

When a natural disaster like an earthquake or tsunami hits a region, it

frequently destroys the existing communications infrastructure. As the relief

agencies move onto the region, the services provided must be coordinated quickly

via a communication network. MANETs are suited for ubiquitous communication

during emergency rescue operations. Energy efficiency, quick response time and

stability are equally important for routing in emergency MANETs, since mobile

nodes have homogeneous lifetimes. The presence of dynamic and adaptive routing

protocols will enable ad hoc networks to be formed quickly, and then it ensures

efficient communications during the rescue operations.

Message transmission in MON is based on message replication or using

MANET routing protocol with DTN mechanism and social-aware routing. The

network overhead is high in MON when a flooding or replication based message

transmission is applied. The DTN extension on MANET routing routing protocol

needs a convergence layer to carry the bundle and social-aware routing needs global

network topology. Instead of allowing every node in the network to forward the

messages, it is better to choose a subset of nodes to do it. When only the nodes in

the subset forward data, the routing in opportunistic networks is achieved with the

optimal performance in terms of the expected end-to-end delay and delivery ratio.

This subset of nodes should be capable of delivering the packets to the destination.

As nodes in WSN prone to failures, nodes may have mobility and are

turned on and off frequently, fault tolerance and routing flexibility are necessary

for routing. Therefore, it is important to maintain a certain degree of redundancy

in CDS. Unfortunately, a CDS can preserve 1-connectivity and it is therefore very

vulnerable. The k−coverage of WSNs studies a methodology to ensure that every

point in a target area is covered by at least k different working nodes. The set of

redundant nodes, then, can sleep until one of the working nodes fails. As a result,

14

the k−covered network can extend the network lifetime without loss of sensing

reliability.

The location or depth based flooding in UWASN increases the energy

consumption of network due to redundant packet transmissions, which in turn

increases an end-to-end delay in dense networks and reduces packet delivery in

sparse networks. Therefore, an energy-efficient routing with reduced overhead is

needed to increase the network performance.

1.5 OBJECTIVES

The high level objectives of this thesis work are:

1. To design a stable CDS and implement a Link-State Hybrid Routing Protocol

for Mobile Ad Hoc Networks.

2. To design a weighted CDS and implement an Energy Efficient Reactive

Routing Protocol for Ad Hoc Communications during Emergency and Rescue

Scenarios.

3. To design an Ego-centrality, Contact-duration based CDS and implement a

Reactive Routing Protocol for Mobile Opportunistic Networks.

4. To design a k−coverage CDS and implement a Fault Tolerant Topology

Control for Area Monitoring in Wireless Sensor Networks.

5. To design an Ant Colony optimization (ACO) based energy, pressure-aware

CDS and implement an Energy-Efficient Routing Protocol for UnderWater

Acoustic Sensor Networks.

15

The first objective of this thesis is considering the expected connectivity

time of the communication links, energy and degree of nodes. These metrics help to

measure the stability of a link and to select nodes (dominating) which can provide

stable path for routing. The dissemination of link state information is restricted to

the dominating nodes, which will reduce the number of broadcast messages. It also

helps to find a routing path from the topology information and the end-to-end delay

will be reduced if no route calculation is done at intermediate nodes.

The second objective is also considers the stability of routing path that

accounts the link stability, mobility and energy of nodes. These metrics are used to

identify backbone nodes which can provide long lasting routing path. The nodes

which are responsible for routing are limited to the nodes in the backbone, which

will greatly reduce routing overhead and energy consumption.

The third objective focuses on selecting the most central nodes that have

more chances to meet the destination. Centrality is a mathematical measure

proposed by social network analysts to capture the structural properties of social

relationship. Here, an ego-centrality and the average duration of contact with other

nodes are considered to select the important nodes that meet more nodes. The

message transmission is restricted only to these selected nodes.

The fourth objective considers the requirement of k−coverage, to take care

of fault tolerance and robustness of dominatees, which ensure that every dominatee

has at least k adjacent dominator neighbors.

The fifth objective addresses the energy efficient communication

mechanism for UWASN. It focuses on providing a communication environment,

where the energy consumption is less and network performance is high. The energy

level and the depth of nodes are used to select the dominating nodes.

16

1.6 CONTRIBUTIONS OF THIS THESIS

The contributions of this research work, which are elaborated throughout this

thesis, are summarized here,

1. Stability based Energy-Efficient Link-State Hybrid Routing (S-ELHR)

Protocol for Mobile Ad Hoc Networks

Proposes a hybrid routing protocol, S-ELHR for MANET and a localized

algorithm for CDS construction. The major contributions of this work are the

following:

- Introduces a weight based willingness calculation, which is computed as a

ratio between actual and initial energy.

- Proposes a stability metric, which takes into account the link connectivity

time, energy and degree of nodes.

- Develop a localized algorithm for CDS construction based on stability

metric.

- Implement a relay based broadcasting for topology discovery and source

routing for data transmission over CDS.

- Apply route recovery mechanism to adapt to changes in network topology.

- Evaluate the performance of S-ELHR and compare it with the existing

protocols.

2. Weighted Connected Dominating Set based Routing (Weighted-CDSR)

Protocol for Ad hoc Communications in Emergency and Rescue Scenarios

Proposes a reactive routing protocol Weighted-CDSR and a distributed

algorithm for weighted CDS construction. The main contributions of this work

are the following:

17

- Proposes a weight metric, which considers link stability, mobility and

energy of nodes.

- Develop a distributed algorithm for Maximum Weight Minimum CDS

(MWMCDS) construction based on weight metric.

- Implement a route discovery and maintenance mechanism over

MWMCDS.

- Evaluate the performance of Weighted-CDSR and compare it with the

existing protocols.

3. Backbone Routing Protocol (BRP) for Mobile Opportunistic Networks

Proposes an on-demand routing protocol and a localized algorithm for CDS

construction. The main contributions of this work are the following:

- Define and Formulate Ego-Centrality Contact-Duration based CDS

(ECCDS).

- Design a localized algorithm for ECCDS construction.

- Develop a message forwarding mechanism over ECCDS.

- Implement a buffering mechanism to be used when the network is

partitioned.

- Evaluate the performance of BRP and compare it with the existing

protocols.

4. k−Coverge CDS for Fault Tolerant Topology Control in WSN

Proposes a k−coverage protocol and distributed algorithms for CDS


- Define and formulate k−Coverage Connected Dominating Set (k−CCDS).

18

- Proposes a Weighted Coverage Cost (WCC), based on the coverage

redundancy of sensing area of sensors.

- Develop distributed algorithms for MIS and k−CCDS constructions.

- Evaluate the performance of k-CCDS and compare it with the existing

protocols.

5. CDS based Energy-Efficient Pressure-Aware Routing Protocol (CDS-

EPRP) for UWASN

Proposes an on-demand routing protocol and a distributed algorithm for CDS


- Application of ACO for CDS construction using energy and depth of

underwater sensors.

- Design of data forwarding over CDS.

- Implement a connectivity mechanism to maintain the CDS structure.

- Evaluate the performance of CDS-EPRP and compare it with the existing

protocols.

1.7 ORGNAIZATION OF THE THESIS

The thesis is organized in eight chapters.

Chapter 1, “Introduction” provides the required introductory concepts to

MANETs, WSN and CDS. The routing challenging in MANETs and WSN are

discussed in detail. The definition of CDS, algorithms for CDS construction and

the applications of CDS are also explained. This chapter also outlines motivation,

objectives, contributions and organization of thesis.

19

Chapter 2, “Literature Survey” reviews related works on routing protocols

in MANET, WSN, MON and UWASN. It also presents the application of CDS

in MANET and WSN. Finally, CDS construction with optimization techniques is

discussed.

Chapter 3, “Design of Stability based Energy-Efficient Link-State Hybrid

Routing Protocol for Mobile Ad Hoc Networks” describes in detail the proposed

protocol S-ELHR, a stability based energy-efficient link-state hybrid routing

protocol. It gives an introduction to stability-based routing in MANETs. The

network model and problem statement are provided for S-ELHR. This chapter

also describes how the stability metric is calculated as combined metric from link

connectivity time, energy and degree. The design of localized algorithm for CDS

construction using stability metric is also explained. This chapter also elaborates

the process involved in discovering the topology, source route calculation and

the route recovery mechanism. The performance of S-ELHR in terms of packet

delivery ratio, end-to-end delay, control overhead and energy consumption is

discussed.

Chapter 4, “Design of Weighted Dominating Set based Routing Protocol

for Ad Hoc Communications in Emergency and Rescue Scenarios” describes

the proposed protocol Weighted-CDSR, a reactive routing protocol for ad

hoc communications during emergency and rescue scenarios. It provides an

introduction to energy-efficient routing in emergency communications. A model

of the network and problem statement are explained. This chapter explains the

computation of weight with link stability, mobility and energy metrics. It also

describes the design of a distributed algorithm for maximum weight connected

dominating set construction. The route discovery and maintenance are also

described. It also presents the simulation parameters and the protocols used for

20

comparison. At the end, the performance of Weighted-CDSR is discussed in terms

of packet delivery ratio, control message overhead, end-to-end delay and energy

consumption.

Chapter 5, “Design of an Ego-Centrality and Contact-Duration based

Backbone Routing Protocol for Mobile Opportunistic Networks” presents the BRP,

a backbone based routing protocol using ego-centrality and contact-duration. An

introduction of social-aware routing in MON is given. The model of network and

the problem statement for BRP are explained. It also explains the computaion of the

ego-centrality, average contact-time calculation and a localized algorithm for CDS

construction. It also presents how the message forwarding is done over CDS. At

the end of the chapter, the simulation parameters, protocols used for performance

comparison and performance results of BRP in terms of packet delivery ratio, end-

to-end delay and routing overhead are presented.

Chapter 6, “Design of Connected k−Coverage Topology Control for Area

Monitoring in Wireless Sensor Networks” presents the k−CCDS, a coverage and

connectivity protocol for WSN based on k−CDS. An introduction of CDS based

coverage in WSN is discussed. It gives the network model and the problem

statement for k−CCDS. The computation of weighted coverage cost and distributed

algorithms for k−CCDS construction are described. The simulation parameters,

protocols used for comparison and the performance of k−CCDS in terms of CDS

size, coverage, energy consumption and lifetime of the network are presented at

the end of the chapter.

Chapter 7, “Design of Connected Dominating Set based Energy-Efficient

Pressure-Aware Routing for Underwater Acoustic Sensor Networks”, describes

the CDS-EPRP, a CDS based energy-efficient pressure-aware routing protocol for

underwater acoustic sensor network. First, an introduction of energy-efficient

21

routing in UWASN is discussed. It presents the network model and the problem

statement. It also explains the ACO based CDS construction algorithm, data

forwarding and connectivity maintenance of CDS. The simulation parameters,

protocols used for comparison and the performance results of CDS-EPRP in

terms of packet delivery ratio, energy consumption and end-to-end delay are also

discussed.

Chapter 8, “CONCLUSION AND FUTURE WORK ” summarizes the

contributions of the research work and outlines the scope for future research.

CHAPTER 2

LITERATURE SURVEY

This chapter reviews on various works carried out for providing routing in

mobile ad hoc and wireless sensor networks. The literature survey begins with

routing protocols in MANETs and WSN. The survey continues by describing

CDS based routing and energy efficient routing protocols in MANETs. Also,

routing protocols in MON, CDS based coverage in WSN and routing in UWASN

are discussed from various literature collections. The application of optimization

techniques for CDS construction is also described in this survey.

2.1 ROUTING PROTOCOLS IN MANET

Dynamic Source Routing (DSR) proposed by Johnson et al. [73], is a

reactive routing protocol, uses a concept of source routing. The sender knows

the complete hop-by-hop route to the destination. These routes are stored in a

route cache. The data packets carry the source route in the packet header. When a

node in the network attempts to send a data packet to a destination and it does not

know the route, it uses route discovery process to dynamically determine a route.

The route discovery process works by flooding the network with Route Request

(RREQ) packets. Each node receiving a RREQ rebroadcasts it, unless it is the

destination or it has route to the destination in its route cache. Such a node replies

to the RREQ with a Route Reply (RREP) packet that is routed back to the original

source. RREQ and RREP packets are also source routed. The route carried back

22

23

by the RREP packet is cached at the source for future use. If any link on a source

route is broken, the source node is notified using a Route Error (RERR) packet. The

source removes any route using this link from its cache and a new route discovery

process is initiated if this route is still needed.

Ad hoc On-Demand Vector (AODV) proposed by Perkins et al. [116] is

a reactive routing protocol, also discovers routes on demand, using a similar route

discovery process like DSR. However, AODV adopts a very different mechanism to

maintain routing information. It uses routing tables with one entry per destination.

It relies on routing table entries to propagate a RREP back to the source and to

route data packets to the destination. It uses sequence numbers at each destination

to determine freshness of routing information and to prevent routing loops. These

sequence numbers are carried by all routing packets. Upon receiving the RREQ

packet, each intermediate node checks whether it has a valid route to the requested

destination. If the sequence number of the stored route is greater than the sequence

number in the RREQ packet, it notifies the source about the valid route by sending

RREP. If an intermediate node does not have a valid route to the destination, it

checks whether it has already forwarded a RREQ packet with the same sequence

number. If not, an intermediate node records its receipt of the RREQ packet and

broadcasts the packet to its neighbors. The destination node, upon receiving the

RREQ packets, chooses the desired route and notifies the selected route through

a RREP packet to the source. An important feature of AODV is the maintenance

of timer-based states in each node regarding utilization of individual routing table

entries. A routing table entry expires if it is not used recently. A set of predecessor

nodes is maintained for each routing table entry, indicating the set of neighboring

nodes that uses the entry to route data packets. These nodes are notified with RERR

packets when the next hop link breaks.

24

Dynamic MANET On-demand (DYMO) proposed by Chakeres et al. [27]

is a reactive routing protocol, uses the same route discovery mechanism used in

AODV to construct the routing tables. During route discovery, the originating node

multicasts a RREQ to find a route toward some target destination. Using a hop-

by-hop retransmission algorithm, each node receiving the RREQ message records

a route toward the originator. When the target destination receives the RREQ, it

records a route and generates a RREP. Each node that receives the RREP stores a

route toward the target, and again unicasts the RREP toward the originator. When

the originator receives the RREP, routes have then been established between the

source and destination nodes, in both directions. Route maintenance consists of

two operations. In order to maintain active routes, DYMO routers extend route

lifetimes upon successfully forwarding a packet. When a data packet is received to

be forwarded downstream but there is no valid route for the destination, then the

DYMO router of the source of the packet is notified via a RERR message. Each

upstream router that receives the RERR marks the route as broken. Before such an

upstream DYMO router could forward a packet to the same destination, it would

have to perform route discovery again for that destination.

Optimized Link State Routing (OLSR) developed by Clausen et al. [33], is

a proactive routing protocol for mobile ad hoc networks. OLSR constructs and

maintains routing tables by diffusing partial link state information to all nodes

in the network, with the help of MPR. There are two types of control traffic in

OLSR: HELLO and TC packets. Each node collects the 2-hop neighborhood

information using HELLO packets. HELLO packets are sent periodically and are

never forwarded by any node. Each node maintains a MPR selector list, including

the nodes, which have elected this node as its MPR. Upon receiving a HELLO

message, a node examines the list of addresses. If its own address is included in the

25

MPR list, the sender is added to the MPR selector list. A node upon receiving the

traffic, checks whether the sender is in the MPR selector list. If so, it will forward

the traffic. TC packets are also sent periodically. The purpose of this packet is to

transmit partial link state information on the network. A TC packet is generated by

a MPR node and it contains the MPR selector list. Upon receiving a TC packet, a

node knows that the sender is the next-hop node to reach all nodes listed in the TC

packet. Once the topology is constructed, shortest path algorithm is run to create

routing tables. All routing is done through MPR nodes .

Energy-Efficient OLSR (EE-OLSR) proposed by De et al. [40] is an

extension of OLSR for proactive routing. They investigated the effects of

applying energy-aware routing to the OLSR protocol, to evaluate the influence

of overhearing and idle activity on the energy consumption in a network using

the IEEE 802.11 technology and to check if these considerations could affect the

performance of a protocol that ensures a good QoS in terms of end-to-end delay. In,

EE-OLSR, the MPR selection mechanism was based on the Willingness concept, to

prolong the network lifetime without losses of performance in terms of throughput,

end-to-end delay or overhead. They tested the performance of EE-OLSR with

different well-known energy aware metrics and noticed that Minimum Drain Rate

(MDR) based EE-OLSR outperforms classical OLSR, and MDR confirms to be the

most performing metric to save battery energy in a dense mobile network with high

traffic loads.

Guo et al. [57, 58] developed a multi-objective routing decision-making

mechanism within OLSR, named Multi-Objective OLSR (OLSR MO). This

routing mechanism considered three objectives: minimizing average end-to-end

delay, maximizing network energy lifetime, and maximizing packet delivery ratio.

Therefore, they focused on three routing metrics: queuing delay, energy cost

26

and link stability cost. First, OLSR MO predicts multiple dynamic networking

metrics in each measurement interval including mean local queuing delay, local

energy consumption, and residual link lifetime. Second, it combines the metrics

into a multi-objective routing metric of each known node-link pair based on the

predicted values. Third, it enables flexible settings for the relative importance of

the different routing objectives in the composite metric. This was achieved by a

normalized weighted additive utility function. To minimize the additional overhead

to the routing protocol, each node independently measured all the metrics locally

without any extra information exchange with the other nodes. These local metrics

were used to predict the corresponding future values, which are disseminated to

its neighboring nodes through normal periodical routing broadcasts. Each node

receives the predicted metric values of neighboring nodes and creates its routing

table using an extended version of Dijkstra’s algorithm. This algorithm calculates

the lowest-cost route to each known node using the composite metric as the link

cost.

Yi et al. [187] developed a hybrid multi-path routing protocol called

MultiPath OLSR (MP-OLSR). MP-OLSR works in two phases: topology sensing

and route computation. The topology sensing phase is used to make the nodes

aware of the topology information of the network. This part have used MPRs

like OLSR. The route computation phase used the Multi-path Dijkstra Algorithm

to calculate the multi-path, based on the information obtained from the topology

sensing. They applied two cost functions to generate node-disjoint or link-disjoint

paths. In MP-OLSR, the proactive behavior of OLSR is changed for an on-demand

computation. It becomes a source routing protocol. To support to the frequent

topology changes of the network, auxiliary functions, route recovery and loop

check, were implemented in MP-OLSR. The source route is saved in the header

27

of the data packets. They also implemented route recovery and loop detection in

MP-OLSR to improve quality of service regarding OLSR. The contributions were

quantified in terms of quality of service parameters and compared with OLSR.

A Reliable Multi-path QoS routing protocol (RMQR) proposed by Wang

et al. [165], is an on-demand routing scheme. RMQR is a reliable multi-path QoS

routing protocol with mobility prediction for MANETs. It includes route discovery,

route reservation, and route maintenance. RMQR considered multiple QoS paths

from a source node to a destination node and the routes must also satisfy certain

bandwidth requirements. To calculate the path, the authors proposed two metrics:

Route Expiration Time (RET) and the number of hops, to select a routing path with

low latency and high stability. The RET is the minimum of the link expiration time

of the links that constitute the path. They considered a path as best path when the

ratio of RET to hop count is maximum. They used global positioning system (GPS)

to determine the RET between two connected mobile nodes.

Tan et al. [155] proposed an on-demand routing protocol, named Power

and Mobility Aware Routing (PMAR) using node location information. PMAR

is designed for choosing a route based on maximizing the minimum node

battery power and minimizing the total transmission power required to reach

the destination. PMAR was able to restrict control packet flooding during

route discovery and pre-empt link breakages because of node mobility. They

first formulated a power and mobility aware optimization problem. Then, they

presented a heuristic schmeme, PMAR protocol. They verified the performance of

PMAR in static and mobile networks.

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2.2 CDS BASED BROADCASTING AND ROUTING INMANETS

Wu et al. [170] proposed a method of calculating power-aware CDS,

based on a dynamic selection process. Specifically, in the selection process of a

gateway node, preference was given to a node with a higher energy level. The

CDS construction was based on the algorithm proposed by Wu et al. [171]. The

algorithm works by finding a CDS and then pruning certain redundant nodes from

the CDS. Initially, each of the nodes exchanges one-hop neighborhood information

with all its neighbors. First, every node determines if two of its neighbors are

mutually adjacent or not. All the nodes, which have two unconnected neighbors,

include themselves in the CDS. In the next step, some redundant nodes were

discarded from the CDS, thereby reducing the CDS size. The pruning rule states

that any node u in the CDS is considered as redundant and should be pruned if it

has either a neighbor in CDS with a larger ID, which dominates all other neighbors

of u or two adjacent neighbors with larger IDs that together, dominate all other

neighbors of u. The pruning rules were extended based on node degree and energy

level associated with each node.

The routing process proposed by Wu et al. [172], was based on a power-

aware connected dominating set in [170, 171]. A dominating node is also called

as a gateway host. The proposed dominating-set-based routing consists of three

steps: 1) If the source was not a gateway host, it forwarded the packets to a source

gateway, which is one of the adjacent gateway hosts. 2) This source gateway

acted as a new source to route the packets in the induced graph generated from

the CDS. 3) Eventually, the packets reached a destination gateway, which is either

the destination host itself or a gateway of the destination host. In the latter case, the

29

destination gateway forwarded the packets directly to the destination host. Each

gateway host maintained gateway domain membership list and gateway routing

table. Gateway domain membership list is a list of non-gateway hosts which are

adjacent to gateway hosts. Gateway routing table includes one entry for each

gateway host, together with its domain membership list.

Stojmenovic et al. [151, 152] proposed a neighbor elimination based

broadcasting scheme with dominating sets. A localized algorithm is applied,

where cluster heads were selected to form a dominating set and border nodes

were identified to connect the cluster heads. They used highest key = (degree,x,y)

in selecting internal nodes, and retransmission after negative acknowledgements

scheme. The degree represents the number of neighbours and (x,y) represents

the position of a node. Internal node maintenance was incorporated into location

updates between neighboring nodes if GPS or another location method is available

to all the nodes in the network.

To minimize flooding traffic, Yen et al. [186] proposed a protocol named

routing with adaptive path and limited flooding (RAPLF). In the RAPLF, the

mobile hosts initially exchange their node sets of one-hop neighbor by the hello

message. Then, each mobile host selects a subset (MPR) of its one-hop neighbor

nodes in such a way that the subset can cover all the two-hop neighbor nodes when

forwarding its broadcast traffic. In this process, each mobile host builds a minimum

spanning tree consisting of all the neighbor nodes in its two-hop list. RAPLF

initiates the route discovery procedure, when the source host wants to transmit

a datagram to a destination. The source host first checks its two-hop list. If the

destination host is in its two-hop list, then the datagram is transmitted by following

the routing table’s path. If the destination host is not in its two-hop list, the source

host broadcasts the Route Search Packet (RSP) to the MPR-set. When the MPR-set

30

receives this RSP packet, it also checks their two-hop list. If the destination host is

in their two-hop list, then the MRP-set forwards directly the RSP to the destination

host. The destination host replies with a Route Reach Packet (RRP) which follows

the RSP return path to the source host. If the destination host is not in their two-

hop list, then it modifies the sequence-number and hop-count, and re-broadcasts

this RSP. The process is repeated until it finds the destination host.

An efficient virtual-backbone routing proposed by Al-Karaki et al. [5] was

based on virtual grid architecture (VGA) clustering. They created a simple and

stable rectilinear virtual topology on which the routing and network management

functions were performed easily and efficiently. The clustering approach consists

of two major steps: network zoning and CH election inside zones. The zoning

strategy starts by dividing the network area into disjoint, adjacent, fixed size, and

regular (symmetric) shape zones. To create a simple rectilinear virtual topology,

they selected the zones to be square in shape with possible extension to other virtual

topologies like hexagon, line, or triangle. After the zoning operation was finished,

a CH election algorithm was executed in each zone.

Bao et al. [16] introduced a topology management by priority ordering

(TMPO) to solve the CDS problem. The priority computation integrates multiple

factors (energy and mobility) into a single metric for cluster election decisions.

TMPO applied the neighbor-aware contention resolution (NCR) algorithm to

provide fast convergence and load balancing with regard to the battery life and

mobility of mobile nodes. Based on NCR, TMPO assigned randomized priorities

to mobile stations, and elects a minimal dominating set (MDS) and the CDS of

an ad hoc network according to these priorities. TMPO requires only two-hop

neighbor information for the MDS elections. The dynamic priorities assigned to

nodes are derived from the node identifiers and their “willingness” to participate in

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the backbone formations. The willingness of a node is a function of the mobility

and battery life of the node.

Hassan et al. [60] developed a new distributed connected-dominated-set

clustering algorithm called Ring Clustering Algorithm (RCA). RCA is a heuristic

algorithm that had three phases: ring-formation phase, members-joining phase and

CDS-nodes selection phase. In the ring-formation phase, each ring consists of

three ring-nodes. The ring was formed if it has the highest priority. The priority of

the ring was based on the total ring-degree rather than the individual node-degree.

The degree of a ring is the number of neighbors that the three ring-nodes have all

together without repetition. Nodes that cannot form rings join neighboring rings

as members in the members-joining phase. In the CDS nodes selection phase, the

decision was made for a node to remain or leave the CDS.

Moulahi et al. [107] mentioned that the methods proposed to minimize

broadcast storm problem, such as MPR or DS-MPR (Connected Dominating Sets

with MPR) stipulated that a packet is correctly received if the receiver node is in

the transmission radius of the sender node. They suggested that this fact is not

always true, due to many factors like signal attenuation, noise and existence of

obstacles. They proposed a broadcasting mechanism based on DS-MPR, where

CDS is constructed from MPR based on weight of nodes. The weight is a function

of node remaining energy and node degree. They proposed realistic weight and

extended weight taking into account the reception probability according to the log-

normal mode. They also introduced a modification of DS-MPR, named Realistic

DS-MPR (RDS-MPR), to be applicable with a realistic physical layer.

Spohn et al. [147] introduced a three-hop horizon pruning (THP) algorithm

to make broadcast operations more efficient in ad hoc networks. THP builds a

two-hop connected dominating set (TCDS) of the network, which is a set of nodes

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such that every node in the network is within two hops from some node in the

dominating set. They adapted a virtual radio range (VR), shorter than the physical

radio range (RR), and considered as one-hop neighbors only those nodes within

VR. The gap between VR and RR works as a buffer zone, in which nodes can

move without loss of connectivity. Every node provides its one-hop neighbors with

a list specifying one or more tuples, each with the identifier of a one-hop neighbor

and a bit indicating if that neighbor dominates any two-hop neighbor. To forward a

broadcast packet, a node tries to obtain the smallest subset of forwarders, which are

one-hop neighbors that use some of the nodes two-hop neighbors to reach any node

that is three hops away. After such a selection of forwarders, the node broadcasts

its packet with a header specifying its forwarder list, and each forwarder in turn

repeats the process.

Duresi et al. [47] proposed a Adaptive Backbone Protocol (ABP) using

CDS. They derived a geometric based probabilistic model that described the

expected coverage of a one-hop broadcast as a function of range, sending rate and

density. They used an analytic model to predict the optimal range for maximizing

1-hop broadcast coverage using information like network density and node sending

rate. The selection of backbone was based on the extended covering problem.

In order to maximize the life span of all nodes, ABP periodically selects nearly

disjoint subset of nodes to form CDS.

Li et al. [88] presented a distributed mechanism called Cluster-Label-based

mechanism for Backbones (CLaB) on mobile ad hoc networks. The proposed

mechanism provided a distributed topology control and consists of three parts:

the part creating a backbone, the routing part, and the maintenance part on the

backbone. CLaB used the clustering approach in the generation and maintenance

of a backbone. A unique and virtual ID was assigned to each cluster, which

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was called a cluster label. The second part adapted existing routing protocols on

the backbone. The routes were constructed on the basis of cluster labels rather

than node IDs. The third part maintained links on the backbone to minimize

the influence of node movements, and needs no rerouting mechanism. The

mechanism especially concentrated on maintenance by introducing constantly

connected backbone elements based on cluster labels.

Samuel et al. [118] proposed a super-node system architecture based on the

DTN framework, as a solution for providing a continuous connection to mobile

nodes that experience intermittent connections. They introduced the concept of

virtual network topology, which is adaptation of the network topology in a DTN

context. They presented a new approach for calculating the probability of future

contacts in DTN and developed a routing technique that was based on calculating

the dominating set for the virtual network topology.

Shaukat et al. [139] controlled the TC message transmission of OLSR with

centrality measure. They defined that a node in a network is central to the extent

that it falls on the shortest path between pairs of other nodes. Rather than sending

TC messages periodically, at each interval a node: (1) monitors betweenness of its

two-hop neighbourhood graph; (2) if the measure is in-control no message is sent,

otherwise a TC message is sent.

Polat et al. [117] defined a Connected Message Ferry Dominating Set

(CMFDS) problem and developed heuristics to find a minimum-size CMFDS,

given a model for the connectivity between nodes over time. In message ferrying

technique, one or more mobile nodes are tasked with storing and carrying data, to

forward data between sources and destinations. Message ferries may need to relay

data to each other, to achieve connectivity between all nodes. This technique is

particularly useful in intermittently connected networks, where traditional MANET

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routing protocols are not usable. They presented a non-adaptive algorithm that uses

a heuristic approach to determine a CMFDS and an adaptive algorithm that uses the

recent past node to node interactions to derive a near future CMFDS.

Montolio-Aranda et al. [105] analyzed the multi-point relaying (MPR)

flooding mechanism, used by OLSR protocol, to distribute topology control (TC)

messages among all the system nodes. They proposed a new flooding method,

based on the fusion of two key concepts: distance-enabled multi-point relaying

and connected dominating set (CDS). They generalized the multi-point relaying

approach by adding distance knowledge, to improve the selection of an optimized

subset of forwarders. They implemented source-independent flooding of broadcast

messages, handling the problem of delayed forwards.

Levin et al. [87] proposed a MCDS based broadcasting. They analyzed

their work to two types of network settings: centralized and distributed. In the

centralized network setting, they assumed that each node has full knowledge about

the topology of the network, including size, distance, and the IDs of all nodes.

In the distributed network settings, they assumed that each node has only partial

information about the network such as the number of neighbors it has or the total

number of nodes. To handle message efficiency, backbone of smaller size was

constructed. To handle time efficiency, a backbone with relatively short diameter

was produced, which decreases the total time of the scheduling algorithm and

ensures all rumors arrive to their destination as soon as possible.

Hong et al. [65] investigated the throughput of virtual backbone in wireless

networks. They showed that a path with higher spectral efficiency is with higher

throughput than a shortest hop. They proposed a maximum spectral-efficient

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connected dominating set (MSE-CDS) algorithm incorporating their spectral-

efficiency metric to obtain a virtual backbone with higher throughput. The spectral-

efficiency was measured based on signal-to-noise ratio. Given two nodes vi, v j,

and Pi j is the set of paths between the two nodes in the network. MSE-CDS is

constructed such that P∗i j is the maximum spectral-efficient path between vi and v j.

Smys et al. [145] proposed two distributed localized self-organized

algorithms called SOB-T and SOB-M to construct and to maintain the network

backbone. SOB-T uses a dual tree-based strategy to form the virtual backbone and

SOB-M uses a marking scheme. A self-organized backbone was formed with a

set of marked nodes that form a self-organized connected dominating set. They

proposed that self-organized backbone network works in two modes: selfish mode

and fusion mode. In selfish mode, each node involve in the routing process or route

discovery and the rest of the time the nodes do not hear the information channel

and go to sleep mode. This process mainly supports the low energy consumption of

each node; it is also used to improve the network life time. The next fusion mode

entirely resides on backbones, i.e. whenever network abnormalities (congestion,

link and node failures) occurs, backbones are stimulated by the normal nodes in

short time duration to rectify the concern issue.

Sivakumar et al. [144] presented core-extraction distributed ad hoc routing

(CEDAR), a routing protocol that dynamically establishes a core set for route set

up, QoS provisioning, routing data, and route maintenance. A greedy algorithm

is used to proactively create an approximate minimum dominating set, whereby

all hosts in the network are either members of the core or one-hop neighbors

of core hosts. Only core hosts maintain local topology information, participate

in the exchange of topology and available bandwidth information, and perform

route discovery, route maintenance, and call admission on behalf of these nodes.

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Two assumptions are made in CEDAR. The first one is that MAC/link layer can

estimate available link bandwidth and second one is that small-to-medium-size ad

hoc networks consist of tens to hundreds of nodes. Although there are no specific,

redundant, reserved routes, the existence of cores provides a proactive approach to

offering partially-cached core routes. This was enhanced by Sinha et al. [142] to

operate routing protocols DSR and AODV over cores.

An energy-efficient dominating tree construction (EEDTC), proposed by

Yu et al. [192] consists of two phases: marking phase and connecting phase. The

marking phase constructs a maximal independent set (MIS) using k-hop neighbor’s

information (k ∈ [1,DG], where DG is the diameter of graph G), and meanwhile

forms a forest consisting of trees rooted at several initiators. In the connecting

phase, the forest was connected to a dominating tree by connecting some adjacent

trees. Compared with other tree-based algorithms, EEDTC simplifies the execution

process by combining MIS construction and forest formation together which are

separated in other schemes.

The Mobility Adaptive Clustering Algorithm (MACA) by Basagni S. [17],

formed Weakly-CDS with slow moving nodes using clustering process. A Weakly-

CDS induces a weakly connected subgraph, which is the graph induced by the

dominating nodes and its neighbors. A cluster-head is a node that acts as a

coordinator for the associated neighbors. When nodes move randomly, a fast

moving cluster-head is likely to encounter another cluster-head sooner than a slow

moving one. The open neighbor sets of fast moving nodes will exhibit more change

than those of slow moving nodes. Therefore, their algorithm had selected slow

moving nodes, which are more likely to have stable links.

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2.3 STABILITY AND ENERGY EFFICIENT ROUTING INMANETS

Sarma et al. [134] developed a route stability based QoS-aware routing

(RSQR) protocol with throughput and delay constraints. They proposed a model

for computing link stability based on measurements of received signal strengths

of two successively received packets from a neighbor. The route stability was

calculated as a product of link stability of all the links which constitute the route

under consideration. RSQR incorporated the stability model in route discovery

process to find QoS routes with the highest stability. It is based on an enhancement

of AODV and some extra fields were included in route request/reply packets. It also

incorporated admission control during route discovery and QoS violation detection

and recovery.

Moussaoui et al. [108] proposed a QoS routing protocol based on link

stability called ST OLSR. This protocol used a new probability based mechanism

by considering the variation in signal strength as a main indicator of the node’s

mobility. They claimed that the use of such metric allows to select effectively the

best path in terms of stability. They presented two metrics: Stability of NoDes

(SND) and Fidelity of NoDes (FND), to assess the link stability between two

adjacent nodes. SND was based on statistics collected by a node on its neighbor to

estimate the durability of the connection. To estimate this stability, they proposed

a function based on Bienayme–Chebyshev inequality. The FND was the degree of

reachability and it was the degree of reachability with only the stable nodes. These

metrics were used to elect the most stable MPR nodes set in the network.

Wang et al. [166] proposed a stable weight-based on-demand routing

protocol (SWORP) for MANETs. The proposed scheme used the weight-based

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route strategy to select a stable route in order to enhance system performance. The

weight of a route is decided by three factors: the route expiration time (RET),

the error count, and the hop count. The link expiration time (LET) represents the

duration of time for a packet to travel between two nodes. Then the RET was equal

to the minimum LET for the feasible path. The error count was used to indicate

the number of link failures caused by a mobile node. Hop count was used to prefer

the route that reaches the destination node first. Route discovery usually first finds

multiple routes from the source node to the destination node. Then the path with

the largest weight value for routing was selected.

Srinivasan et al. [150] proposed a Route Stability and Energy Aware Ad hoc

On-demand Distance Vector (RSEA-AODV) protocol, which is an enhancement of

AODV protocol. RSEA-AODV computes the reliability factor based on stability

and residual energy of nodes. The route with the highest reliability factor value

was selected for data transmission. It predicts the probability of link failures using

signal strengths and mobility of nodes. It takes the product of the residual battery

of the intermediate nodes to select a path that has nodes with maximum residual

energy among the path that just meet the basic energy requirement. To reduce the

probability of link failure, the path with higher path stability value contains more

stable links was selected.

Wu et al. [169] introduced an effective link lifetime estimation mechanism

based on received signal strength. They analyzed the relationship between the

reliability of end-to-end connection and the number of paths with number of paths

was restricted to two. They proposed an adaptive path establishment mechanism to

set up multiple paths according to the current network topology and the estimated

link lifetime. On the basis of the network coding method, a reliable packet

transmitting mechanism was also proposed to enhance network performance. They

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applied the link stability-aware intelligent trigger scheme to reduce the redundant

packets transmission.

Joshi et al. [74] proposed a modified protocol, including multi-path and

energy aware technique in OLSR, named OLSRM. The neighbor selection in

OLSRM was based on residual battery energy of a node and traffic conditions

that influence the drain rate of the node in the network. The authors have

considered the multi-path and source routing concept for route selection and a route

recovery technique to tackle mobility issue efficiently. In OLSRM, the load was

distributed fairly with even utilization of energy resources in the network so as to

increase network lifetime as well as individual node lifetime in various dynamic

conditions. The multi-path source routing approach was used in association with

the min–max lifetime as an improvement over the conventional hop-by-hop routing

in the original OLSR protocol.

El-Hajj et al. [48] presented a dominating set based routing scheme, named

fast distributed connected dominating set (FDDS) routing. In this protocol, each

node knows its own ID, residual energy, and traffic load. A node calculates

its mobility by measuring its own displacement with respect to its neighbors at

different time periods. At time t1, node X measures the average distance D1avg

between itself and its neighbors. X repeats the same calculation at time t2 in order

to obtain D2avg. X can then estimate its mobility by calculating (D2avg - D1avg).

Node X estimates the distance to its neighbors by measuring their received signal

strengths (RSS). FDDS is divided into four steps. The first step uses a simple

neighbor discovery protocol and assigns a weight for each node. The second step

elects an initial set of cluster heads. The third step connects the cluster heads

together to form a CDS. The last step eliminates some redundant cluster heads.

40

Torkestani. [158] introduced a learning automata-based distributed

algorithm for constructing the most stable virtual backbone as a bounded diameter

minimum spanning tree (BDMST) problem. He stated that the duration of the

communication link, so called link duration time, is inversely proportional to

the relative mobility of the hosts that are connected by the link. The concept

of expected link duration time (ELDT) was defined to predict the real mobility

behavior of the host. A backbone diameter d was defined as the maximum number

of hops connecting every pair of hosts along the backbone and the network delay is

bounded by the backbone diameter d. BDMST algorithm constructed the most

stable delay bounded virtual backbone with the edge weight as ELDT of the

corresponding communication link and diameter d as the maximum backbone hop-

count.

Sheu et al. [140] proposed an efficient distributed algorithm to construct a

stable CDS based on received signal strength, by keeping a node with many weak

links from being selected as a member of CDS. They assumed that the transmitted

power strength of each mobile node is fixed and the same. Each node computes the

distances from its each neighbor since both the transmitted power strength and the

received power strength from its each neighbor are known. In their work, a link is

said to be weak if the strength of the beacon signals received on the link is below

a threshold. The nodes were considered in the decreasing order of the non-weak

links associated with that node. The proposed algorithm was based on the marking

process and rule k, operated in a distributed manner.

Wang et al. [163] proposed a localized backbone construction scheme,

namely connected maximal independent set with multiple initiators. The backbone

construction consists of two interleaved phases: forest construction and merging

on conflicts. Here, a node rank is defined as a tuple of stability, effective-degree,

41

and ID. They constructed a stable backbone using this rank. The stability metric

is used for measuring the mobility and effective degree is used for estimating the

coverage of a node. They assumed that there is usually temporal and spatial locality

in node movement. Based on this, the stability of each node was estimated using its

previous location information. They also defined that the stability of a node is the

reciprocal of the sum of the distance between its initial location and the locations

for 10 consecutive seconds.

An energy efficient and scalable routing protocol is proposed by Ramrekha

et al. [128] for emergency ad hoc communications. They have designed an energy

consumption model for MANET nodes and used a packet delivery delay model to

explain the scalability and energy efficiency. The proposed energy consumption

model aimed at reducing energy consumption due to data packet transmission and

processing at critical nodes that are frequently solicited for data forwarding. The

energy efficient mechanism only focused on fairly distributing the forwarding load

of data packets whenever possible. This model was integrated with OLSR and

AODV protocols.

Macone et al. [98] developed a reinforcement learning based proactive

routing protocol name mobile Q-Routing (MQ-Routing) for disaster relief

scenarios. The routing strategy have chosen the next-hop node based on residual

energy of nodes. MQ-Routing also takes into consideration the mobility of each

node in choosing the route towards a destination, in order to rapidly adapt to

network changes. Three different metrics were reported, taking into account the

link availability prediction, the residual energy of the nodes and the node mobility.

These metrics were combined together to yield a time-varying discount factor.

A link availability metric was computed by assuming that the nodes were GPS-

enabled, and was used to compute the link availability factor. The mobility and

42

residual energy metrics, lead to the computation of the mobility factor and of

the energy factor, respectively. Link availability and the mobility metrics provide

information on the link stability.

Xia et al. [178] proposed a new cluster based routing protocol FASTRoute

(FASTR) for highly mobile heterogeneous MANETs without group mobility. In

order to minimize the clustering delay, they applied pre-selection mechanism,

where all powerful nodes with multiple interfaces are selected as cluster heads.

Each cluster head will periodically gather the network topology of local cluster

and send out HEARTBEAT messages with the cluster topology embedded in the

messages. An ordinary node joins the cluster upon receiving the HEARTBEAT

messages. If the ordinary node receives multiple HEARTBEAT messages from

different cluster heads, it will attach to the cluster head with the minimum hop

distance. After joining a cluster, the node will increment the hop distance counter

in the HEARTBEAT message and re-broadcast the message to allow further nodes

to join the cluster.

2.4 ROUTING IN MOBILE OPPORTUNISTIC NETWORKS

Epidemic routing proposed by Vahdat et al. [159] works as follows.

The protocol relies upon the transitive distribution of messages through ad hoc

networks, with messages eventually reaching their destination. Each host maintains

a buffer consisting of messages that it has originated as well as messages that it is

buffering on behalf of other hosts. An epidemic protocol works by transferring its

data to each and every node it meets. As data is passed from node to node, it is

eventually passed to all nodes, including the target node. One of the advantages

of an epidemic protocol is that by trying every path, it is guaranteed to try the

43

best path. One of the disadvantages of an epidemic protocol is the extensive use

of resources with every node needing to carry every packet and the associated

transmission costs.

The Spray and Wait protocol proposed by Spyropoulos et al. [148], bounds

the total number of copies and transmissions per message without compromising

performance. It consists of two phases: spray and wait. During the spray phase,

L packet copies are “sprayed” to relays in the network. Then these relays enter

the wait phase until they meet the destination and the message is delivered. Spray

and Wait is classified into source and binary spray. With source spray, the source

replicates a message to the first L nodes contacted. In binary spray the source keeps

dL/2e copies and distributes the remaining copies to the first node encountered.

The relay carries dL/2e copies. This distribution continues recursively for each

encounter until each node is left with one copy.

Wu et al. [175] proposed a hop-limited Epidemic Routing protocol for

DTN routing. Their method achieved better performance through controlling the

message hop count. In this approach, because of the energy constraint or other

factors, each node may forward only limited times, that is, both the message hop

count and the forwarding times may be limited. They conducted simulations based

on both synthetic and real motion traces to show the accuracy of the framework.

They also explored the impact of many parameters (e.g., message hop count)

through extensive numerical results. The numerical results showed that both the

message hop count and the forwarding times can have certain impact on the routing

performance, but their impact is related with many other factors (e.g., the number

of nodes).

PROPHET, a Probabilistic Routing Protocol using History of Encounters

and Transitivity, proposed by Lindgren et al. [94] is an epidemic protocol with

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strict pruning. PRoPHET’s goal was to gain the advantages of an epidemic protocol

without paying the price in storage and communication resources incurred by the

basic epidemic protocol. Instead of doing blind epidemic replication of bundles

through the network, it applies ”probabilistic routing”. To accomplish this, a metric

called ”delivery predictability”, 0<=P (A,B)<= 1, is established at every node A

for each known destination B. This metric is calculated so that a node with a higher

value for a certain destination is estimated to be a better candidate for delivering a

bundle to that destination (i.e., if P (A,B)>P (C,B), bundles for destination B are

preferable to forward to A rather than C). It is later used when making forwarding

decisions. As routes in a DTN are likely to be asymmetric, the calculation of the

delivery predictability reflects this, and P (A,B) may be different from P (B,A).

According to this protocol, nodes exchange and update the delivery predictability

when they meet other nodes. Also, a node exchanges all messages to a node when

the other node has a higher delivery probability.

Adaptive-Routing scheme proposed by Lakkakorpi et al. [84] uses only

local information to transmit the messages from source to destination using either

AODV or DTN routing, depending on current node density, message size, and

path length to destination. The Adaptive-Routing approach is to choose in the

sending node whether to use DTN (e.g., epidemic or spray and wait) or AODV

for message delivery. The benefit of the approach is that both routing protocols

can remain untouched, and intermediate node need to support only pure DTN or

AODV functionality. The decision on which protocol to use for transmitting a given

message from source to destination is made on application level. To evaluate the

proposed approach they used a simulation model that closely follows real world

use cases. The simulation models the effect of wireless physical layer congestion.

They conducted simulations with with synthetic and real life mobility traces, that

45

model the proposed usage scenario. They confirmed that it is beneficial to integrate

MANET and DTN routing so that the method for the message delivery is chosen

for each message adaptively on a case-by-case basis when sending the message.

Raffelsberger et al. [126] developed a combined MANET/DTN Routing

(CoMANDR), works like a traditional routing protocol for MANETs when end-

to-end paths are available. It uses the routing table that is calculated by the

MANET protocol to route packets that can be reached instantly over a multi-hop

end-to-end path. To cope with disruptions, CoMANDR utilized two mechanisms

from delay/disruption-tolerant networking: packet buffering and utility-based

forwarding. If the routing table contains no valid entry for a packet’s destination,

CoMANDR buffers the packet instead of discarding it. There may be situations

where an end-to-end path between sender and receiver will never be available. To

handle such situations, CoMANDR may also forward packets to nodes that are

assumed to be closer to the destination. The decision to which node a buffered

packet should be forwarded is based on a utility function. CoMANDR used a

modified version of the PROPHET meeting probability calculation function to

calculate the utility of a node. In contrast to the PROPHET protocol, that only

considers when two nodes directly meet (i.e., there is a direct link between the

nodes), CoMANDR also considers multi-hop information from the routing table.

When a node i has a routing table entry for another node j (with a distance less than

infinite), CoMANDR considers node i and j to be in contact. This allows nodes to

exploit multi-hop paths to determine contacts with other nodes.

Pan et al. [113] proposed “SpecRouter”, a spray with prophet and epidemic

controlled routing protocol, where the transmission direction and the number of

the copies are dynamically controlled according to the information of the whole

distribution rate of the nodes. Similar to the process of the original PRoPHET

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routing, the delivery predictability was calculated. Addressing epidemic controlled

routing issue, each node recorded its location and context to a historical information

database. Nodes renew their routing passively and share their location and moving

information. Firstly, when a node encounters another node, it exchanges its

summary vector with the encountering node. If it finds out that there are some

new messages in the buffer of the encountering nodes, it then compares its delivery

predictability with that of the other, if its value of delivery predictability is lower

than that of its encountering node, then it enters the copy state, otherwise it enters

the forwarding state. Secondly, in the copy state, it compares the copy control

counter (CountC) of the message with that of threshold (NC), if CountC equals NC,

then CountC is set to 0, and the node copies the messages to others. If CountC is

less than NC, CountC will be added 1. The node does not exchange any messages

with other nodes until CountC equal NC. If it finds out that there isn’t any new

message for it, then it compares the delete control counter (CountD) with that of

the threshold (ND), if CountD equals ND, the node will delete the message in its

buffer, otherwise CountC is added 1 and the node waits until CountC reach ND.

Li et al. [91] stated that probabilistic forwarding with a higher delivery

utility enhances single-copy routing. They also described that the current

probabilistic forwarding methods only consider node contact frequency in

calculating the utility while neglecting the influence of contact duration on the

throughput, though both contact frequency and contact duration reflect the node

movement pattern in a social network. They theoretically proved that considering

both factors leads to higher throughput than considering only contact frequency.

To fully exploit a social network for high throughput and low routing delay, they

proposed a Social network oriented and duration utility-based distributed multi-

copy routing protocol (SEDUM) for DTNs. SEDUM has three distinguished

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features. First, it considered both contact frequency and duration in node movement

patterns of social networks. Second, it used multi-copy routing and discovered

the minimum number of copies of a message to achieve a desired routing delay.

Third, it used an effective buffer management mechanism to increase throughput

and decrease routing delay.

Luo et al. [96] presented a routing scheme for DTNs, called Adaptive

Spraying Based on the Inter-contact Time (ASBIT), based on inter-contact time

and degree centrality measure. They defined that a contact is a communication

opportunity in which a mobile node comes into communication range with another

node in DTNs. The inter-contact time between two nodes was defined as the

time elapsed between two successive contacts. They stated that a node with a

higher degree centrality maintains more contacts with other nodes in the network.

In this scheme each node dynamically chooses the right number of message

copies disseminated to respond to the current conditions of the network. When

forwarding, ASBIT selects the node with a higher centrality as the next hop, and

utilized a simple additive weighting algorithm for the division of the replication

number. They used three attributes: degree centrality, speed of current node and

free buffer. The weighted sum of these attributes were used to find the replication

number.

Wang et al. [164] proposed an improved routing algorithm based on social

link awareness (SLABR). In order to indicate the social relationship of the node’s

pair, two metrics were defined, social pressure metric and relative social pressure

metric. The social link of the node’s pair was calculated according to these two

metrics. Then the friendship community of the node was constructed based on its

social links. SLABR is composed of two parts, inter-community forwarding and

intra-community spreading. A single-copy based forwarding mechanism was used

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in the inter-community forwarding, where message is forwarded to the node with

stronger social link to the destination node. A multi-copy based binary forwarding

algorithm was executed in the intra-community spreading so that the message can

be diffused in the community quickly. According to whether the carrying node and

destination node are in the same friendship community, different algorithms were

implemented.

Miao et al. [101] presented the Community-based Adaptive Spray (CAS)

routing protocol for mobile delay tolerant networks. A community was defined as

a set of nodes which frequently co-exist and encounter. CAS is composed of two

major parts. First, a sub-protocol responsible for gathering mobility information

about nodes upon encountering each other. Second, a sub-protocol responsible for

the routing process. Routing is organized around the notion of gateways between

communities. Specifically, a gateway towards a community C, is the node in a

given community that has the highest probability to encounter any node in C. To

route a message towards a given destination node, the source of a message uses

the community topology to pre-compute multi-hop path that traverses the minimal

number of communities through their gateway nodes and that has the highest

delivery probability. Furthermore, once the routing process is engaged, the routing

protocol allocates a given number of message copies at each hop depending on the

remaining TTL of the message. The CAS protocol raises the number of message

copies in the network in proportion to the remaining TTL, in order to increase

the probability of message delivery before time runs out. This strategy keeps the

number of message copies in the network low while achieving a high delivery ratio.

Cheng et al. [29] proposed a social opportunistic networks routing (SONR)

which brings an adapted discrete Markov chain into node’s mobility model and

calculated the transition probability between successive status. The probability is

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defined as the occupation ratio of a node in steady of network state. In SONR, the

nodes are naturally divided into different communities. The purpose of community

detection is to improve the forwarding rate in condition of meeting forwarding

spending limitations. In order to improve routing performance in the opportunistic

networks, they used social characteristics such as centrality to assist message

forwarding. The degree centrality was for relay selections. The node degree

centrality is defined as the number of links incident upon a given node. A prediction

method of two node’s next transition probability is proposed.

Chen et al. [28] proposed a group aware cooperative routing protocol for

opportunistic networks called GAR, which aims to maximize the message delivery

probability under the resource constraints of both bandwidth and buffer space.

The proposed GAR protocol includes a cooperative message transfer scheme and

a buffer management strategy. In the cooperative message transfer scheme, the

limited bandwidth available for mobile nodes is considered and two encountering

nodes will exchange messages cooperatively to maximize the delivery probability.

In the buffer management strategy, they further considered the constraint of mobile

node’s buffer space, and proposed the cooperative message caching scheme, in

which the message dropping priorities were set to minimize the reduced delivery

probability. They also proposed an improved strategy to utilize the extra contact

duration of the encountering nodes to further improve the performance. They

adopted the quota-based routing scheme, in which each message initially has a

predefined number, which is denoted as k, of replicas in the network. A message is

considered as being successfully delivered when any replica of the message arrives

at the destination within the message’s time-to-live.

The Delay Tolerant Link State Routing (DTLSR) proposed by Demmer et

al. [41] was modeled on classic link state algorithms. As the network state changes,

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link state announcements are flooded throughout the network. Each node maintains

a graph representing its current view of the state of the network, and uses a shortest

path computation (e.g. Dijkstra) to find routes for messages. Each node in the

system was assigned to an administrative area, and a link state protocol instance

operates only within a single area. This helps to constrain the size of the network

graph and limits the scope of announcement messages, if required. Nodes that

have neighbors in other areas learn the set of endpoint identifiers reachable via

the other area and announce themselves as a gateway to those endpoint identifier.

The relay node selection was based on convergence layer. They implemented a

constrained flooding algorithm within the DTN bundle forwarding layer. Link state

announcement messages were sent as bundles.

2.5 CDS BASED ROUTING AND SCHEDULING IN WSN

He et al. [61] constructed a VB, based on the size and the load-

balance factors. They investigated three NP-hard problems namely, the MinMax

Degree Maximal Independent Set (MDMIS) problem, the Load-Balanced Virtual

Backbone (LBVB) problem, and the MinMax Valid-degree non-Backbone node

Allocation (MVBA) problem. They solved LBVB in two steps: First, they

proposed an approximation algorithm by using linear relaxation and random

rounding techniques to solve MDMIS problem. Subsequently, the minimal set

of nodes is found to make the MDMIS connected. They formulated MVBA as a

binary programming and presented a randomized approximation algorithm, which

produces a solution with the traffic load on each backbone.

He et al. [62] proposed a greedy algorithm for Load Balanced CDS

(LBCDS) construction based on dominator’s degree values. In LBAD problem,

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rather than allocating a dominatee to a dominator in a naive way, they introduced

a new term, Expected Allocation Probability (EAP), which represents, for any

connected dominatee and dominator pair, the expected probability that the

dominatee is allocated to the dominator. On the basis of the EAP value,

they formulated the LBAD problem into a constrained nonlinear programming

optimization problem. They also proposed a probability-based distributed

algorithm to dynamically allocate dominatees to dominators.

Zeng et al. [194] proposed an efficient distributed approximation algorithm

that computes a sub-optimal MCDS in polynomial time, for connectivity

maintenance of WSNs. The proposed algorithm was fully distributed, and the

constructed CDS had a small size, which reduced the overhead of maintaining

the backbone and the cost in communication. The constructed CDS achieved

load balancing, which extends the lifetime of the network. They also proposed

an energy conservation node self-scheduling algorithm (ECSS), for coverage

maintenance. Each sensor makes self-scheduling to separately control the states

of radio frequency and sensing unit based on dynamic coordinated reconstruction

mechanism. ECSS was based on a probabilistic sensing model, provided some

degree of redundancy according to application requirements. It considered the

residual energy and detection ability of nodes.

Zhao et al. [198] presented a sleep-scheduling technique called Virtual

Backbone Scheduling (VBS). VBS was designed for WSNs, has redundant sensor

nodes. VBS constructed multiple overlapping backbones which work alternatively

to prolong the network lifetime. In VBS, traffic was forwarded by backbone nodes,

and other sensor nodes turn off their radios to save energy. The energy consumption

of all sensor nodes was balanced with the rotation of multiple backbones, which

fully utilizes the energy and achieves a longer network lifetime. The scheduling

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problem of VBS was modeled as the Maximum Lifetime Backbone Scheduling

(MLBS) problem. They proposed approximation algorithms based on the schedule

transition graph and virtual scheduling graph, since the MLBS problem is NP-hard.

They also presented a distributed implementation of VBS using an iterative local

replacement scheme.

Kui et al. [83] investigated the problem of constructing an energy-balanced

CDS to effectively preserve the energy of nodes in order to extend the network

lifetime in data collection. An energy-balanced connected dominating set scheme

named DGA-EBCDS was proposed, and each node in the network can effectively

transmit its data to the sink through the virtual backbone. When constructing

the virtual backbone in DGA-EBCDS, they prioritized selecting those nodes with

higher energy and larger degree. This method makes the energy consumption

among nodes more balanced. Furthermore, the routing decision in DGA-EBCDS

considered both the path length and the remaining energy of nodes in the path to

further prolong the lifetime of nodes in the backbone.

Khedr et al. [79] proposed an algorithm to select a minimum number of

sensor nodes which can entirely cover a monitored region. The proposed algorithm

included the following four phases: initial, connectivity, finding minimum

connected cover, and mobility assistance. In the initial phase, the network was

organized into partitioned grids, where each grid contains a cluster head (CH)

and its members. The connectivity phase included the following operations: it

initializes setup phase by the CHs and finds the set of sensor nodes that can directly

communicate to each CH; determines the hop count for each sensor node; and

constructs the connected routing path for each sensor node within the network to

its CH. In finding a minimum connected cover phase, they used a greedy based

scheme and a sensor node selection method, to select a sensor node that has the

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largest benefit in terms of covers, the largest area with highest residual energy, and

minimum communications cost from the unselected sensor nodes.

Du et al. [46] devised an efficient algorithm that produces a CDS with

bounded CDS size and guaranteed routing cost in terms of routing path length.

The size of the resulting CDS can be slightly greater than that of the MCDS, but

the routing path length should have an upper bound. They proposed a centralized

algorithm, where MIS was constructed and nodes in the MIS were connected to

form a CDS. It was implemented with all pairs shortest paths sequential algorithm.

They also proposed a distributed algorithm with two stages: MIS construction and

connection, based on node ID. These algorithms produce a CDS D whose size |D |

is within a constant factor from that of the minimum CDS. For each node pair u

and v, there exists a routing path with all intermediate nodes in D and path length

at most 7.d(u,v), where d(u,v) is the length of the shortest path between u and v.

Yuanyuan et al. [193] proposed an energy efficient distributed connected

dominating set algorithm based on coordinated reconstruction mechanism to

prolong the network lifetime and balance energy consumption. The algorithm

consists of two phases. In the first phase, MIS was constructed and the second

phase of the algorithm chose a minimal number of nodes to make the DS connected,

i.e., a CDS. They considered dynamic reconstruction strategy to balance energy

consumption in the networks. Each time when a CDS is constructed as backbone,

the length of operating time of this CDS for this round was determined according to

the energy level of the CDS nodes. If the minimal residual energy of nodes in CDS

was cut down to a certain level (such as 50%) of the initial energy of current round,

the operating period of this round was due. When this operating period expires,

the next CDS operating round was computed. They assumed that each node u has

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a weight w(u) of being in the backbone. Here, w(u) was computed based on a

combination of its remaining battery power and its effective degree.

Yu et al. [191] addressed the domatic partition (DP) problem, which

partitions the set of nodes in networks into disjoint dominating sets. They stated

that through rotating each dominating set in the domatic partition periodically,

the energy consumption of nodes can be greatly balanced and the lifetime of the

network can be prolonged. In order to solve the domatic partition problem, they

presented a cell structure which was constructed as follows. Firstly, the network

was divided into clusters, and then a clique was constructed in each cluster. Based

on the cell structure, they proposed a distributed nucleus algorithm for DP using

the property of the skyline of uniform radius disks.

Torkestani et al. [157] designed a learning automata-based heuristic for

backbone formation in WSN, taking into account both energy consumption and

transmission delay issues. The proposed heuristic constructs the network backbone

by finding a near optimal solution to the proxy equivalent degree-constrained

connected dominating set problem. The degree-constrained minimum-weight CDS

problem was having the minimum expected weight subject to a given constraint on

the node degree. Then, a learning automata-based heuristic was proposed to find a

near optimal solution to the proxy equivalent CDS problem.

2.6 CDS BASED COVERAGE IN WSN

Pemmaaraju et al. [115] proposed three deterministic distributed algorithms

for k-domatic partition problem. The k-domatic partition problem seeks to partition

the network into maximum number of k-dominating sets. A k-dominating set is a

subset of nodes D such that every node in the network is at distance at most k

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from D. Their first algorithm works on Unit Ball Graph(UBG), assuming that all

nodes know their position in a global coordinate system. In the second algorithm,

it is assumed that pair wise distances between neighboring nodes are known. They

have applied the third algorithm on Growth-Bounded Graph, using the connectivity

information.

The Connected Domatic Problem (CDP), partitioning the nodes of the

graph G into node disjoint CDS, have been proposed by Misra et al. [102]. They

developed a distributed algorithm for CDP using MIS based heuristics which

depends on the connectivity information. They have shown that the size of a CDP

is at least b δ+1β (c+1)c− f , where δ is the maximum node degree; β ≤ 2 and c ≤ 11

is a constant for UDG; the expected value of f is εδ |V | where ε � 1 is a positive

constant and δ ≥ 48.

Misra et al. [103] proposed a distributed approximation algorithm for

MCDS problem with a known initiator. A new collaborative cover heuristic was

proposed using two principles: 1) domatic number of a connected graph is at

least two and 2) optimal substructure defined as subset of independent dominator

preferably with a common connector. This heuristic helped in identifying smaller

cardinality MIS of G as compared to ID-based or degree-based heuristics. A

Steiner tree was constructed in two phases: steiner nodes identified in the first

phase to drive the MIS construction by shifting independent set nodes to locate

the connectors in identifying Steiner nodes. The second phase becomes a post-

processing step of identifying the steiner nodes to construct the CDS tree satisfying

a standard bound. They have also shown that the CDS approach, when used for in-

network aggregation application, prolongs the network lifetime.

Yang et al. [184] addressed the k-(Connected) Coverage Set (k-CCS/k-CS)

problem using linear programming. They developed an approximation algorithm

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based on integer programming for the k-CS problem. They proposed two non-

global k-coverage solutions, one was quasi-local cluster-based with a deterministic

bound, the other was localized with a proven probabilistic bound. Two versions

of each solution were considered, one with connectivity for k-CCS and the other

without connectivity for k-CS.

Shang et al. [138] proposed three centralized approximation algorithms for

the minimum k-tuple dominating set problem and m-connected k-tuple dominating

set problem. They constructed (m,k)-CDS, the fault tolerant virtual backbone as

m-connected k-tuple dominating set. Every node in the network is dominated by

at least k backbone nodes. The backbone is m-connected if there are at least m

disjoint paths between each pairs of nodes.

Sausen et al. [135] proposed centralized and distributed solutions for

computing bounded-distance multi-coverage backbones in WSNs. This means that

any sensor node is covered by multiple backbone members within a bounded-

distance. To guarantee these properties, a (k,r)-CDS mechanism is employed

for computing a backbone. The multiple domination parameter, k, defines the

minimum number of backbone nodes covering any regular sensor node. The

bounded-distance parameter, r, defines the maximum distance to k backbone

nodes for any other sensor in the network. The centralized solution provides an

approximation to the optimum solution, and it is used as a lower bound when

evaluating the performance of the distributed solution. The distributed solution

is source-based in the sense that usually the base station (or sink) is the focus of

attention in a WSN. A broadcasting mechanism with dynamic power management

was applied on (k,r)-CDS.

Qureshi et al. [124] proposed a polygon based CDS formation for reliable

and energy-efficient topology. They found a polygenic backbone to turn-off the

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unnecessary nodes while keeping the network connected and covered. To achieve

energy efficiency, the protocol formed a CDS like polygenic network which in turn

provides reliability in the case of random link failures. Moreover, it adapted to

topological changes in the network based on the remaining energy of the nodes.

This allows topology maintenance among different set of nodes to increase the

network lifetime. They compared the CDS based topology control algorithms

in WSN [125]. They also proposed a clique based CDS (CCDS), where CDS

was formed according to the size of the network using single phase topology

construction process. The CCDS protocol formed cliques of size 2 based on first

come first serve basis. Since the CCDS does not select any node based on the

selection metric, the clique sets form a CDS backbone, which covers the whole

network.

Lee et al. [85] designed a distributed and reliable energy-efficient topology

control (RETC) algorithm for topology construction and maintenance in real

application environments. Particularly, many intermittent links and accidents may

result in packet loss. A reliable topology can ensure connectivity and energy

efficiency, prolonging network lifetime. Thus, in the topology construction phase,

a reliable topology was generated to increase network reachable probability. In

the topology maintenance phase, this work applied a novel dynamic topology

maintenance scheme to balance energy consumption using a multi-level energy

threshold. This topology maintenance scheme can trigger the topology construction

algorithm to build a new network topology with high reachable probability when

needed.

Carle et al. [26] developed a localized algorithm named Connected Area

Dominating Set (CADS) based on Surface Coverage Relays (SCR). SCR-CADS

algorithm is based on relay selection and self-decision. In SCR-CADS algorithm,

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each node computes a subset of its neighborhood, its relay set, which covers the

same surface as all the neighbors. Nodes apply a simple rule to decide whether

or not they should be active. This rule was based on a unique random priority

attributed to each sensor node of the network. Any node which has the highest

priority of its neighborhood or which has been selected as SCR relay by its neighbor

with the highest priority, will belong to CDS.

Li et al. [90] proposed three algorithms to construct kmCDS for general k

and m. The first one, CSAA, is a centralized sequentially augment algorithm which

is suitable for small wireless networks where the number of nodes in a network is

usually not large. DDA is a distributed deterministic algorithm. In DDA a kmCDS

grows from an inner core to the outer nodes. DPA is a distributed probabilistic

approach which is based on the fact that the whole network is surely k−connected

when the probability of the minimum degree being larger than k is almost 1. These

algorithms consist of two phases: CDS construction and MIS connection phase.

Anitha et al. [12] proposed a base station-controlled centralized algorithm

for static sensor networks and a distributed, weighted algorithm for dynamic sensor

networks. The solutions were based on a (k,r)-CDS, which were suitable for

cluster-based hierarchical routing. Every non-dominating node is dominated at

least by k dominating nodes within distance r in (k,r)−CDS. The cluster head

redundancy parameter k, improves reliability, the multi-hop parameter r, addresses

the scalability issue and the combined weight metric improves the network lifespan

and reduces the number of re-affiliations. To create a stable and efficient backbone

structure, the backbone sensor nodes are selected based on quality, which is

a function of the residual battery power, node degree, transmission range, and

mobility of the sensor nodes.

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Wu et al. [177] proposed one centralized algorithm CGA and one

distributed algorithm DDA, for k-connected m-dominating set. The main idea was

to construct an m-dominating set first and then augment this set for k-connectivity.

Firstly, nodes were sorted in non-increasing order based on tuple (Ni,ei, IDi).

Ni was given the highest preference because of the observation that the size of

C is smaller if nodes with larger degree are added first. Energy was another

consideration. Therefore, the nodes with more remaining energy were added to

the set instead of the ones with less remaining energy so that the total network

lifetime can be extended. Node ID was used to break ties. Initially, C is empty.

Then nodes were repeatedly added into C till C is an m−dominating set. After C

becomes an m−dominating set, check whether C is k−connected or not.

Wu et al. [176] presented a distributed kmCDS construction algorithm,

LDA, for general k and m. LDA is a totally distributed algorithm which is preferred

by WSNs, especially for large WSNs. It also has lower message complexity than

others. For small networks, centralized algorithms are more suitable since they may

have better results and may save communication cost compared with distributed

algorithms. Therefore, they proposed a centralized algorithm ICGA which is better

than CGA, since CGA cannot always guarantee obtaining a kmCDS.

Kim et al. [81] investigated the problem of constructing quality CDS

in terms of size, diameter, and Average Backbone Path Length (ABPL). They

presented two centralized algorithms having constant performance ratios for its

size and diameter of the constructed CDS. They gave its distributed version, which

not only can be implemented in real situation easily but also considers energy to

extend network lifetime. Both algorithms constructed CDS based on MIS.

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2.7 ROUTING IN UWASN

Depth-based-routing (DBR) proposed by Yan et al. [183], is a geographic

routing protocol, where each node knows the depth to the surface sink using

pressure sensors. On receiving a data packet, each node forwards it only if its

depth is less than that of the sender. Before forwarding the data packet, each node

calculates holding time for a packet that depends on the difference between its own

depth and that of the sender. In particular, the larger the depth, the smaller the

holding time, so that nodes that are closer to the surface sink are the first to forward

the data packet. While holding, a node if it overhears that the packet that it is about

to broadcast is transmitted by another node, then it drops the packet.

Void-aware Pressure Routing (VAPR) proposed by Noh et al. [111] is

a geographic routing protocol. VAPR uses surface reachability information to

set up each node’s next-hop direction toward the surface through which local

opportunistic directional forwarding can always be used for data packet delivery

even in the presence of voids. It builds a directional trail to the closest sonobuoy on

the surface. The idea of this protocol is similar to that of DBR: A node will forward

a packet only if other nodes closer to the sink cannot send it. VAPR neither requires

additional recovery path maintenance nor incurs any hop stretch caused by the

recovery fall-backs in existing solutions. They also provided a new framework of

attaining loop freedom using soft-state breadcrumb approach in mobile networks.

Domingo. [42] proposed a Distributed Underwater Clustering Scheme

(DUCS) based energy-aware routing protocol, for long-term non-time-critical

aquatic monitoring applications, with random node mobility and without global

positioning system support. This clustering protocol does not use flooding

techniques, minimizes the proactive routing message exchange and it uses data

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aggregation to eliminate redundant information before transmission to the sink.

He proposed TDMA and CDMA (Code Division Multiple Access) with DSSS

(Direct Sequence Spread Spectrum) using pseudo-orthogonal codes for intra-

cluster communication and only CDMA with DSSS using pseudo-orthogonal codes

for all other communications processes.

A vector based forwarding (VBF) proposed by Xie et al. [180] assumed

that the position information was calculated by measuring the angle of arrival

(AOA) and strength of the signal. In VBF, each packet carries the positions of

the sender, the target, and the forwarder. The forwarding path was specified by

the routing vector from the sender to the target. Upon receiving a packet, a node

computes its relative position to the forwarder. Recursively, all the nodes receiving

the packet compute their positions. If a node determines that it is sufficiently close

to the routing vector (e.g., less than a predefined distance threshold), it puts its own

computed position in the packet and continues forwarding the packet; otherwise,

it simply discards the packet. In this way, all the packet forwarders in the sensor

network form a “routing pipe”: the sensor nodes in this pipe are eligible for packet

forwarding, and those which are not close to the routing vector do not forward.

In the basic VBF protocol, all the nodes inside the routing pipe are qualified

to forward packets. In dense networks, too many nodes might be involved in the

data forwarding process. To save energy, Xie et al. [181] also proposed a self-

adaptation algorithm, based on the concept of desirableness factor, which estimates

the density of a node in its neighborhood using local information. This algorithm

aims to select the most desirable nodes as forwarders. In this algorithm, when a

node receives a packet, it first determines if it is close enough to the routing vector.

If yes, the node then holds the packet for a time period related to its desirableness

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factor. In other words, each qualified node delays forwarding the packet by a time

interval.

A Hop-by-hop Vector Based Forwarding (HH-VBF) proposed by Nicolaou

et al. [110] used the same concept of routing vector as VBF. However, instead of

using a single virtual pipe from the source to the sink, HH-VBF defined a different

virtual pipe around the per-hop vector from each forwarder to the sink. In this way,

each node can adaptively make packet forwarding decisions based on its current

location. This design directly brings the following benefits: (1) Since each node has

its own routing pipe, the maximum pipe radius is the transmission range. In other

words, there is no necessity to increase the pipe radius beyond the transmission

range in order to enhance routing performance; (2) In sparse networks, though the

number of eligible nodes may be small, HH-VBF finds a data delivery path as long

as there exists one in the network. Thus, HH-VBF enhances data delivery ratio in

sparse networks compared with VBF.

An adaptive hop-by-hop vector-based forwarding (AHH-VBF) proposed by

Yu et al. [188], is an extension of HH-VBF. In AHH-VBF, during the transmission

process, the radius of virtual pipeline was adaptively changed hop by hop to restrict

the forwarding range of packets, in order to guarantee the transmission reliability

in the sparse sensor region and to reduce duplicate packet transmissions in the

dense sensor region. They also adjusted the transmission power level hop by

hop in cross-layer fashion to improve energy-efficiency. The forwarding nodes

in AHH-VBF, were selected based on the distance from current node to destination

node so that the end-to-end delays is reduced effectively. They proposed two

metrics: propagation deviation factor and effective neighbor number, to evaluate

the network performance of AHH-VBF.

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A Hop-by-Hop-Dynamic Addressing Based (H2-DAB) routing protocol

proposed by Ayaz et al. [13], is a scalable and energy-efficient using multi-sink

architecture. They used surface buoys to collect the data at the surface and

anchored sensors at the bottom. Other sensors are deployed at different levels from

surface to bottom. Nodes near the surface sinks are assigned smaller addresses and

these addresses are increasing as the nodes go down towards the bottom. These

dynamic addresses are assigned using hello packets. Any node which collects the

information forwards it towards the upper layer nodes in a greedily fashion. They

have also used special nodes called courier nodes for better energy consumption

and to increase the reliability.

Pompili et al. [119] investigated the problem of data gathering by

considering the interactions between the routing functions and the characteristics

of the underwater acoustic channel. They proposed two bandwidth and energy-

efficient distributed geographical routing algorithms for delay-insensitive and

delay-sensitive applications in UWASN. In order to increase the efficiency of the

acoustic channel, the proposed algorithms allow a sender to transmit a short packets

back-to-back without releasing the channel. Specifically, the proposed routing

algorithms allowed each node to jointly select its best next hop, the optimal transmit

power, and the forward error correction rate for each packet, with the objective of

minimizing the energy consumption, while taking the condition of the underwater

channel and the application requirements into account.

Wahid et al. [161] proposed a reliable energy-efficient routing protocol

based on physical distance and residual energy (R-ERP2R) for UWASN. It is a

location-free routing protocol and considered into account multiple routing metrics.

The multiple routing metrics used in R-ERP2R are: physical distance, link quality

and residual energy. Physical distance is the distance of a sensor node from the

64

sink node. It was used to select the next forwarding node that is closer to the sink

than the sender of a packet. The link quality information was utilized to select the

next forwarding node with more reliable link among all the candidate nodes. The

residual energy of nodes was considered to balance energy consumption among the

sensor nodes. In R-ERP2R, each node computes these metrics and communicates

it to all the nodes. During the data forwarding, a node that is closer to the sink than

the sender, having high residual energy and having good link quality were selected

as a next forwarding node.

Ali et al. [6] proposed a novel routing protocol called Layer by layer Angle-

Based Flooding (L2-ABF) for UWASN to address the issues of continuous node

movements, end-to-end delays and energy consumption. It used an angle-based

flooding architecture in which multi-sinks were anchored on the water’s surface to

collect data packets. The ordinary nodes were deployed on different depth levels

from the surface to the bottom in the form of layers. Each node forwards sensed

data towards the upper layer nodes, using the angle-based zone. The data packet

received on one of the sinks on water’s surface, was considered to be delivered

successfully. In L2-ABF, every node calculated its flooding angle to forward

data packets toward the sinks without using any explicit configuration or location

information. L2-ABF completes its task in two phases. In the first phase, a Layer-

ID was assigned to the sensor nodes by the sink node in the network. In the second

phase, the nodes forward sensed data.

Alves et al. [7] presented a controlled flooding routing mechanism inspired

by the route establishment phase of the OLSR protocol, termed MPR. MPR used

periodic control messages to collect (recent) historical information on link quality

and topology status in order to find the best route towards the destination. For

the envisioned scenarios where nodes periodically broadcast telemetry and status

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information, the contents of the control packets are simply piggybacked in the

data packets, incurring in no additional transmissions. MPR does not assume any

preliminary information on the network, e.g., where the sink is, and it is designed

to work in both scenarios where a common collection point is present or where

data has to be exchanged between any pair of nodes in the network. MPR was

also designed to work in DTN scenarios storing the packets when a reliable path is

not available and trying to forward it when updated and more favorable topology

information is received.

2.8 OPTIMIZATION TECHNIQUES FOR CDS

Jovanovic et al. [75, 76] proposed the first meta-heuristic solution based

on ACO to the the Minimum Weighted Dominating Set (MWDS) Problem. The

given graph is converted into a complete graph with edges having a weight 0 if

they are not present in the original graph and 1 otherwise. The uncovered neighbor

is represented by a value 1. A heuristic used in this problem takes into account the

weights of vertices being covered. In each iteration, the node with the maximum

ratio of the sum of weights of its uncovered neighbors to its weight is added to the

MWDS. This is used to initialize the pheromone values of the nodes in the ACO

algorithm. The state transition rule for an ant k to choose node i is determined

by the probability pki . It is calculated as pk

i =τiη

β

i

∑r∈Akτrη

βr

, where Ak represents the

set of nodes that are not in the dominating set and ηβr the heuristic component.

The global pheromone update rule used by the algorithm is τi = (1−ω)τi +ω∆τi,

where ω is the pheromone evaporation rate and ∆τi =1

∑ j∈D w( j) , D being the best

dominating set constructed by an ant in that iteration. In addition, the algorithm

also used a local pheromone update rule as τi = (1−φ)τi +φτ0, where φ ∈ (0,1)

and τ0 is the initial pheromone.

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A minimum weight dominating set construction algorithm proposed by

Potluri et al. [121] is based on ACO without heuristic. The state transition rule

for an ant is based solely on the pheromone values of the nodes not yet in the

dominating set. A local search mechanism is applied to reduce the weight of the

dominating set by removing any redundant nodes from the solution generated by

an ant. A dominating set where the number of dominated nodes assigned to each

dominating node does not exceed the capacity of the corresponding dominating

node, is called the capacitate dominating set. They also proposed meta heuristic

algorithms for minimum capacitate dominating set problem, where the capacity

represents the maximum number of nodes that a node can service at most [122].

Ho et al. [64] introduced a new way for encouraging the construction of

diverse solutions. This was achieved by having the ants not follow the standard

transition mechanism all the time. Rather, based on a specified probability, an ant

will first randomly select a set of allowable solution components, and then from

this set, select the most desirable one. This strategy was known as tournament

selection. Even though the proposed strategy used additional randomization as

an extension of pure random selection, they showed that the tournament selection

approach gives better performance than pure random selection. They justified the

proposed enhanced ACO meta-heuristic (ACO-TS) by comparing its performance

with the original ACO meta-heuristic, a standard genetic algorithm, and an ACO

that uses pure random selection for diversity control.

Sundar et al. [154] presented a heuristic, an artificial bee colony (ABC)

algorithm and an ant colony optimization (ACO) algorithm to solve the dominating

tree problem (DTP). The algorithm for DTP using ACO was referred as ACO DT.

In ACO DT, pheromone was laid on the vertices of the graph. This is due to the fact

that the choice of vertices plays more important role in the construction of good

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solutions than the choice of edges. Once they had a dominating tree containing

certain vertices, then a dominating tree of minimum cost was found, comprising

only these vertices by running Prim’s algorithm on the subgraph induced by these

vertices.

Chizari et al. [31] modeled the problem of MPR selection as set covering

problem. They proposed a new fitness function for the optimization techniques:

genetic algorithm, tabu search and simulated annealing. They also proposed a

new heuristic based MPR selection algorithm named EF-MPR, based on the hill

climbing method without local optima escaping function. In EF-MPR, the MPR

selection was similar to the the original MPR selection with two modifications. i)

At each step, the best operation that reduces the cost function was selected and ii)

MPR size was reduced by using the fitness function.

2.9 SUMMARY

From the literature, it is observed that a CDS based routing is an energy-

efficient mechanism for communication in MANETs and WSN. It is also found that

the message forwardings in MON are based on number of message copies and relay

selections. Also, it is found that the CDS based coverage protocols use the degree

or ID of nodes in its communication range, to increase the connectivity among

dominators or dominatees, in order to provide redundant coverage. Also, it is

observed that the routing protocols in UWASN, use flooding based communication

to increase the packet delivery, which leads to more energy consumption. The

survey also shows that the application of an ACO technique for CDS construction,

produces good results on CDS size.

CHAPTER 3

DESIGN OF STABILITY BASED ENERGY EFFICIENT

LINK STATE HYBRID ROUTING PROTOCOL FOR

MOBILE AD HOC NETWORKS

3.1 INTRODUCTION

A MANET consists of wireless nodes that can self organize into arbitrary

and temporary network topologies by themselves. Due to the nature of wireless

network, the transmission in these networks is basically a one-hop broadcast, in

which a message transmitted by a node reaches all the nodes in its transmission

range. Two nodes not within the transmission range communicate through

intermediate nodes as relays. Based on the route discovery principle, routing

protocols are classified into either proactive or reactive. Proactive protocols

update routes for every pair of nodes at regular intervals. The reactive or on-

demand protocols, determine route only when there is a need using a broadcasting

procedure.

The network-wide broadcasting methods are classified into probability-

based, area-based and neighbor-knowledge-based (Montolio-Aranda et al. [105]).

They are highly resource consuming approaches and it is used in almost all routing

protocols like AODV (Perkins et al. [116]), DSR (Johnson et al. [73]) and OLSR

(Clausen et al. [33]). Among these protocols, OLSR uses neighbor-knowledge

based method MPR, where each node selects a subset of its one-hop neighbors as

68

69

forwarding nodes to reduce redundant broadcasting. These MPR nodes guarantee

that all two-hop neighbors receive a copy of the broadcast packets and therefore

all nodes in the network can be covered without retransmission by every node

[123, 152, 186]. These MPRs form a CDS [92, 173]. Forwarding the data through

the backbone or CDS is a cost-efficient alternative to broadcasting in which the

backbone nodes are responsible for routing only.

The biggest challenge in MANETs is providing a stable route for packet

delivery [109]. Most of routing protocols use hop count as a selection metric

and found that the routes discovered are not stable. The node’s mobility may

clearly affect both the quality of the selected paths and their durability. Thus,

the route selection process should also consider the link stability criterion (i.e.

links’ durability), which allows to maintain the characteristics of the selected paths.

Recently, there is a growing interest in the research towards applying CDS to

support various network functions such as multi-hop communications [156, 172].

In MANET, link failures occur frequently due to node mobility, formation of a

long-lasting backbone or CDS significantly improves the network performance.

This study proposes an efficient way to form a stable CDS based on

stability metric, which avoids selecting a node with many links of low stability

as a dominator. It also implements a hybrid link-state routing protocol operating

over stable CDS, named stability based energy-efficient link-state hybrid routing

(S-ELHR).

3.1.1 Stability based Routing in MANETs

Since each node in a MANET is mobile, the topology of the MANET may

change dynamically. From the viewpoint of routing, communication between two

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nodes which are far away from each other may fail because of the link failure

between any two intermediate nodes which are adjacent. Thus, the provision

of stable links in a MANET becomes essential. Some routing protocols have

emphasized the need to find routing paths consisting of links with higher stability

[74, 108, 134, 150, 163, 169].

Basically, most of them rely on the received signal strength to estimate

the stability of a link. A link with greater received signal strength is referred

to as having a higher stability. Because each node in a MANET is mobile, the

CDS topology may change dynamically. Therefore, like the routing protocols

mentioned above, CDS-based routing protocols must also deal with the issue of

link stability. To be more specific, in order to reduce CDS’s maintenance overheads

and to provide a more stable CDS for other algorithms, the CDS stability should

be taken seriously. A CDS is more stable if it can hold for a longer period of time

during which no dominating node needs to update its routing table. The stable CDS

constructions were addressed in [48, 99, 140, 158, 163]

3.2 STABILITY BASED ENERGY-EFFICIENT LINK-STATEHYBRID ROUTING (S-ELHR)

The S-ELHR is a hybrid routing protocol using relay-based broadcasting

to discover the topology and on-demand source routing to send data packets. It

constructs CDS first and then performs routing over CDS. The CDS construction

uses a stability metric (SM), which is a function on link connectivity index, energy

and degree weights. The nodes with the greatest SM are selected as relays. The

topology discovery or broadcasting of S-ELHR uses the CDS nodes to disseminate

the topology information. The routing is done through the CDS nodes, where the

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routing path is established on-demand and each data packet carries a complete

routing path. The following sections explain the stability metric calculation, CDS

formation and routing in S-ELHR.

3.2.1 Network Model and Problem Statement

A MANET is modeled as a graph G(V,E), where V is the set of nodes in

the network and E is the communication links among the nodes. A homogeneous

network where nodes have same transmission range is assumed. An edge exists

between the two nodes if the distance between them is less than the transmission

range. A node learns about its own location through location service schemes such

as global positioning system or any other scheme. A node learns the velocity and

direction of movement of its neighbors through the beacon messages periodically

broadcast by the nodes in one-hop. Every node selects a relay set as in definition

3.1.

Definition 3.1. Relay(u): Given a graph G = (V,E), for a node u ∈ V,Relay(u) =

{v|v ∈ Nu1} such that Nu

2 =⋃

v∈Relay(u)Nv1 .

The problem can be modeled as finding a CDS C for an edge-weighted,

connected and an undirected graph G = (V,E,W ) with edge weight function W :

E → R+, where V = {v1,v2, ...,vn} denotes the set of mobile nodes of MANET,

E = {(vi,v j) | i ≤ n, j ≤ n} ⊆ V × V denotes the communication links between

the nodes, and W = {w(i, j) | ∀(vi,v j) ∈ E} denotes the set of weights (stability

metric) associated with the communication links. The objective of the problem is

to find C such that it can work for the longest time.

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3.2.2 Stability Metric (SM) Calculation

To estimate the stability of a link, a parameter called SM is proposed, which

takes into account the three different cost metrics, link connectivity index (LCI as

defined in section 3.2.2.2), energy weight (EW as defined in section 3.2.2.3) and

degree weight (DW as defined in section 3.2.2.4). The LCI metric computes the

predicted link expiration time between the two nodes. It is used to find more stable

nodes which can provide a long lasting route. The energy metric is used to increase

the lifetime of CDS, because nodes with more energy need to be selected. The

size of CDS will be smaller when nodes with more neighbors are selected. So,

the stability metric uses these three metrics to form a smaller CDS with increased

lifetime, which can work for the longest time. Every node in the network calculates

their energy and degree weights. This information is communicated to its one-hop

neighbors. Each node after receiving these informations, calculates the stability

metric to each of its one-hop neighbor. The SM of a node u to its neighbor v can

be defined as follows,

SM(u,v) = α ∗LCI(u,v)+β ∗EW (v)+ γ ∗DW (v) (3.1)

where α +β + γ = 1.

3.2.2.1 Computation of Willingness

Each node has a variable Willingness, specifies how willing a node is to be

forwarding traffic on behalf of other nodes. A node may dynamically change its

willingness as its conditions change. In S-ELHR, each node uses weight which is

a ratio between actual and initial energy, to set its Willingness. In this protocol,

a node declares a WILL LOW if the weight is less than 10%. If the weight is in

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the range 10% to 50%, Willingness is set to WILL DEFAULT. Otherwise, a node

declares WILL HIGH as its willingness. The weight based Willingness selection

is shown in Table 3.1.

Table 3.1: Willingness Calculation in S-ELHR

Weight < 10% ≥ 10% and < 50% ≥ 50%Willingness WILL LOW WILL DEFAULT WILL HIGH

A weight based selection of Willingness introduces an improvement in the

relay selection, allowing nodes to declare a willingness values of WILL HIGH, a

high willingness to act as relay for its neighbors or WILL LOW to signal a low

willingness to act as relay. In this protocol, the CDS selection will never include

a node with WILL LOW. The Willingness field in the HELLO message is used to

hold the weight value of the issuing node.

3.2.2.2 Link Connectivity Index (LCI) Metric

The LCI metric uses the concept of predicted link lifetime of two nodes i

and j as defined by Su et al. [153]. It is calculated as follows. Let the co-ordinates

of i and j be (Xi,Yi) and (X j,Yj) respectively. The two nodes are moving with

velocities vi and v j in directions θi and θ j. Let R be the transmission range of

the nodes. Then, the amount of time LCI(i, j), the mobile nodes i and j will stay

connected is

LCI(i, j) =−(ab+ cd)+

√(a2 + c2)R2− (ad−bc)2

(a2 + c2)(3.2)

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where a, b, c, d can be calculated by Eqs. (3.3–3.6), respectively.

a = vi ∗ cosθi− v j ∗ cosθ j (3.3)

b = Xi−X j (3.4)

c = vi ∗ sinθi− v j ∗ sinθ j (3.5)

d = Yi−Yj (3.6)

3.2.2.3 Energy Weight (EW) Metric

The EW of a node u is the remaining energy in u divided by the maximum

energy of nodes in Nu1 .

EW (u) =Eu

rm

Max{E irm;∀i ∈ Nu

1}(3.7)

3.2.2.4 Degree Weight (DW) Metric

The DW of a node u is the number of neighbors of u divided by the

maximum degree of nodes in Nu1 .

DW (u) =|Nu

1 |Max{|Ni

1|;∀i ∈ Nu1}

(3.8)

3.2.3 Algorithm for Stable CDS Construction

The proposed CDS construction algorithm does not need any knowledge of

the global network topology to generate a CDS. Every node in the network needs

to know the ID of one-hop and two-hop neighbors. All these information’s are

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piggybacked into HELLO messages and they are sent periodically by every node.

The CDS selection process can be summarized as follows: First, each node chooses

a set of one-hop neighbors as its relay nodes with respect to stability metric. Each

node then communicates their relay set to its one-hop neighbors. The pseudo-code

for CDS selection is given in Algorithm 3.1. Initially, each node sets its dominating

flag to f alse. A node v selects a one-hop neighbor say x as it relay, if x is the only

node to reach some of its neighbors (line 4). Otherwise, it chooses a one-hop

neighbor with the greatest SM value. This process is repeated until all the two-hop

neighbors are covered (lines 6 to 9). Finally, the dominating flag is set to true for

all nodes in the relay set (line 10). All the dominating nodes form a CDS for the

network.

Algorithm 3.1: Dominating Set Construction

1. Nvw = { Nv

1 with Willingness = DEFAULT or HIGH }

2. Relay(v) = φ

3. NC(v) = Nv2

4. Relay(v) = x, where x ∈ Nv1 are the only nodes to reach some nodes in

Nv2

5. NC(v) = NC(v)−NRelay(v)1

6. while NC(v)<> φ do

7. Choose x ∈ Nvw with maximum SM(v,x)

8. Relay(v) = x

9. NC(v) = NC(v)−Nx1

10. Mark Dominating(x)← true, ∀x ∈ Relay(v)

Algorithm Complexity

Let ∆ be the maximum degree of a node. It is assumed that O(∆) time is

needed to find out all one-hop neighbors that solely cover some two-hop nodes. The

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algorithm iteratively calculates the remaining one-hop neighbors until all two-hop

nodes are covered. This process needs O(∆) for each round and since the iteration

process takes M rounds to complete, the second step needs at most O(M∆) time in

total to finish. Therefore the overall time complexity is O(M∆).

Let n be the number of nodes in the network. Each node needs to send

HELLO messages to its one-hop neighbors to inform its one-hop neighborhood

information. After the relay selection, each node also sends out a message to inform

one-hop nodes that have been selected as relays. Therefore each node only sends

a constant number of messages during the CDS selection process and hence, the

message complexity is O(n).

3.2.4 Routing in S-ELHR

3.2.4.1 Topology Discovery

Each node maintains a local information base and topology information

base. The local link information base stores information about links with

neighbors. Each node maintains topology information about the network in the

topology information base. Each node, which has been selected as dominating,

regularly broadcasts Relay-Update messages to inform the network of its list of

nodes which has elected it as relay. Only the dominating nodes are involved in the

processing and the redistribution of the Relay-Update messages. These message

transmission help the dominating nodes to create and maintain the partial topology

information. The Relay-Update message forwarding in S-ELHR is according to

the following steps.

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(1) The node that originates the Replay-Update message sends it to all its

neighbors.

(2) A relay node which receives the message forwards it to all its neighbors when

the node from which it receives the message for the first time, belongs to the

list of Relay-Selector. Otherwise, the message will not be forwarded.

(3) Repeat (2), until no more forwards are needed for the message.

3.2.4.2 Route Computation and Route Recovery

A route computation is done when a node wants to send a packet. Nodes

do not maintain routing tables in S-ELHR. To construct a path, a shortest path

algorithm is executed for the specified destination address from the topology

information base. The dominating nodes are the only intermediate nodes in the

established path. To improve routing performance, source routing is applied in S-

ELHR for data retransmission instead of hop-by-hop routing. The whole route

is determined by the source node and all the intermediate nodes will only act

as routers to store and retransmit the packet. Every packet carries the complete

information of the route, from the source node to the destination node, including

all the intermediate nodes. So the intermediate nodes need not to maintain the

routing information and the routing nodes need not to calculate the next hop.

As the network is dynamic, there is a possibility of link breakages. The

S-ELHR also implements a route recovery scheme to adopt the changes in the

network topology. Algorithm 3.2 explains this process. As each packet in S-ELHR

carries the routing path, intermediate nodes check whether the next hop in the

source route of the packet is one of its neighbors. If so, it forwards the packet

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(lines 5 to 6). Otherwise, route re-computation is done to find a new route to the

destination and then forward the packets through the new route (lines 7 to 10).

Algorithm 3.2: DATA PACKET ProcessingInput: Node n receives a data packet p with destination d

1. if n == d then

2. receive(p)

3. else

4. nextHop = nextNodeAddress(p)

5. if neighborTable.lookU p(nextHop) 6= 0 then

6. f orward(p,nextHop)

7. newPath = routeRecovery(d)

8. p.sourceRoute = NewPath

9. nextHop = nextNodeAddress(p)

10. f orward(p,nextHop)

3.3 SIMULATION STUDY

3.3.1 Simulation Parameters

To evaluate the proposed protocol, the simulations are performed with NS-

2. CBR packets with 512 bytes are transmitted. The source and destination pairs

are selected randomly. The parameters of the simulation are summarized in Table

3.2.

The results are taken from 30 trails and the average of the results is

presented in error graphs with 95% confidence interval. Since the proposed work is

using CDS nodes for network-wide broadcasting and routing, the performance of

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Table 3.2: S-ELHR Simulation Parameters

Parameter Value

Simulation Time 600s

Traffic Type Constant Bit Rate(CBR)

Number of Connections 10

Packet Size 512 bytes

Packet Sending Rate 5 packets /second

MAC Protocol IEEE 802.11

Propagation Model Two-ray Ground

Transmission Range 250m

Bandwidth 2 Mbps

Queue Size 50 packets

Area Size 1000m x 1000m

Number of Nodes 20,30,40,50,60,70,80,90,100 (default:50)

Mobility Model Random Waypoint

Maximum Speed 5m/s to 35m/s in steps 5m/s (default:25m/s)

Transmission Power 0.666 W

Reception Power 0.395 W

Idle Power 0.1 W

S-ELHR is evaluated with the following performance metrics and is also compared

to OLSR and EE-OLSR.

- Number of Relay nodes: It measures the number of nodes in the CDS.

- Average path length: It defines the average length of path between source and

destination pairs.

- Number of Topology Message Forwarding: It measures the number of Relay-

Update packets forwarded by each node.

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- Average Energy Consumption: It defines the total energy consumed for the

routing operations during the simulation time.

- Control Packets per Data Packet: It is the ratio between the number of data

packets received and the total control packets.

- Packet Delivery Ratio: It is the ratio between number of data packets

successfully received and total data packets generated.

- Average End-to-End Delay: It is the amount of time to forward a data packet

from source node to destination node.

3.3.2 Protocols used for comparison

Since S-ELHR is based on link-state information, Optimized Link State

Routing (OLSR) protocol proposed by Clausen et al. [33] was used for performance

comparison. OLSR is a proactive routing protocol where each node periodically

broadcasts its routing table so that its neighbouring nodes can achieve a complete

view of the network state. Important to the operation of OLSR are MPR sets.

An MPR is a subset of the one-hop neighbors of a node selected to forward its

control packets. When each MPR forwards the message, a node is guaranteed

communication with each of its two-hop neighbours. This provides an efficient

implementation of network-wide broadcast. Each node discovers and maintains

topology information through the periodic exchange of HELLO and topology

control (TC) messages. A HELLO message, exchanged between a node and its

one-hop neighbours, contains a list of one-hop neighbours indicating those in the

MPR set, a list of two-hop neighbors, and a list of neighbours that have selected

this node as an MPR. The TC messages contain a list of all the nodes that have

selected the sender as MPR. The MPRs periodically exchange network topology

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information. When exchanging link-state information, each node lists only the

information of the nodes that have selected it as an MPR, that is, its multi-point

relay selector (MS) set.

As S-ELHR uses willingness concept, it was also compared with energy-

efficient OLSR (EE-OLSR) proposed by De et al. [40]. In EE-OLSR, each node

computed its own energetic status, can declare an appropriate willingness. The

willingness selection was based on both metrics: the battery capacity and the

predicted lifetime (based on the energy-drain rate) of a node. A heuristic function

was used to associate a willingness (“default”, “low” or “high”) to a pair (battery,

lifetime). For example, in condition of high battery value, if the predicted lifetime

is short, a node declares a W DEFAULT willingness. On the other hand, if a longer

node lifetime is predicted (because the node is experimenting low traffic), the node

can declare a W HIGH willingness. In the same way, if the battery charge is low

a node is less available to become MPR and declares a W LOW willingness value

(whatever lifetime it predicts). This permits a better load balancing to be obtained

and node with lower residual energy are not stressed.

3.3.3 Results and Discussion

Fig. 3.1. shows that the percentage of relay nodes drops, when the number

of nodes for the protocols increases. As the number of neighbors of a node

increases with the increasing network size, the percentage of nodes selected as

relays is decreased. The weight based willingness permits a better load balancing

to be obtained and node with lower residual energy are not stressed. The CDS

formation with stability metric significantly reduces the number of relay nodes.

82

10

20

30

40

50

60

70

80

90

100

20 40 60 80 100 120

Pe

rcen

tage

of R

ela

y N

od

es

No.of Nodes

OLSRS-ELHR

EE-OLSR

Fig. 3.1: No. of Nodes vs Percentage of Relay Nodes

Moreover, the proposed protocol S-ELHR selects a stable CDS, which can

provide a long lasting routing path. The MPR selection algorithm of OLSR do not

take into consideration the route stability issues. But, an energetic status is used for

MPR construction process in EE-OLSR. The figure shows that the relay set size of

S-ELHR is the least compared to EE-OLSR and OLSR.

The length of the shortest path connecting the different sources and

destinations has a major impact on the performance of the networks. Longer paths

increase the delay, the jitter and the packet loss of the traffic flow. In order to

evaluate the impact on the path lengths, the average path lengths are calculated for

the protocols and it is shown in Fig. 3.2. S-ELHR shows better results compared

to OLSR and EE-OLSR since it selects less CDS nodes in sparse networks. The

probability to have shorter paths is higher when the network is denser. The average

path length in denser network is almost the same in S-ELHR, OLSR and EE-OLSR.

83

2

2.25

2.5

2.75

3

3.25

3.5

3.75

4

20 40 60 80 100 120

Avera

ge

Path

Le

ngth

(Hop

s)

No.of Nodes

OLSREE-OLSR

S-ELHR

Fig. 3.2: No. of Nodes vs Average Path Length in Hops

But, the S-ELHR gives the least average path length compared to OLSR and EE-

OLSR.

Fig. 3.3. presents the average number of topology messages forwarded by

each node during the simulations against network density. There are increasing

topology messages, since the number of relay nodes grow when the network size

increases for the protocols. The connectivity index, degree and energy metrics

used in S-ELHR, take care of the stability of the CDS. Thus, the path through

these nodes experiences less link breakages than the path through MPR in OLSR.

As the EE-OLSR protocol uses the nodes energy consumption as MPR selection

metric, it provides an energy rich path than OLSR. Due to larger MPR set size, EE-

OLSR experiences more topology overhead than S-ELHR. Hence, S-ELHR has

less topology message overhead compared to OLSR and EE-OLSR.

Fig. 3.4 shows that the topology overhead of the protocols increases with

84

200

400

600

800

1000

1200

1400

1600

10 20 30 40 50 60 70 80 90 100 110

Top

olo

gy M

essa

ge O

ve

rhe

ad p

er

Nod

e (

pkts

)

Number of Nodes

OLSRS-ELHR

EE-OLSR

Fig. 3.3: No. of Nodes vs Topology Message Overhead

600

650

700

750

800

850

900

950

1000

1050

1100

1150

1200

0 5 10 15 20 25 30 35

Topolo

gy M

essag

e O

verh

ea

d p

er

Node (

pkts

)

Max. Speed (m/s)

OLSRS-ELHR

EE-OLSR

Fig. 3.4: Mobility vs Topology Message Overhead

85

the varying speed. The more topology change due to nodes speed has an impact

in the increasing topology overhead for the protocols. Both OLSR and EE-OLSR

experience frequent changes in the MPR set when the node speed is more than

25m/s. This leads to more topology message forwarding. However, topology

overhead of S-ELHR is the least among the protocols, because the DS set is not

changing frequently due to stability metric.

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90 100 110

Avg. E

ne

rgy C

onsum

ption p

er

Nod

e (

J)

Number of Nodes

OLSRS-ELHR

EE-OLSR

Fig. 3.5: No. of Nodes vs Average Energy Consumption

Due to less control overhead, the S-ELHR consumes the least energy

compared to EE-OLSR and OLSR. As the network is sparse with minimum number

of nodes, the energy consumption of nodes is high due to mobility of nodes. The

percentage of relay nodes drops with the increasing network size as shown in Fig.

3.1., the energy consumption of nodes drops and it is shown in Fig. 3.5.

Fig. 3.6 shows that energy consumption of nodes increases with the

increasing speed. When the speed is more than 20m/s, EE-OLSR and OLSR

86

25

30

35

40

45

50

55

60

65

70

75

0 5 10 15 20 25 30 35

Avg.

Ene

rgy C

onsu

mptio

n p

er

No

de (

J)

Max. Speed (m/s)

OLSRS-ELHR

EE-OLSR

Fig. 3.6: Mobility vs Average Energy Consumption

consume more energy due to more route breakage. With minimum topology

message forwarding through lesser and stable nodes, S-ELHR consumes the least

energy compared to EE-OLSR and OLSR.

The number of control packets transmitted for each successful data packet

transmission with varying network size is shown in Fig. 3.7. S-ELHR generates

less control packets because less number of nodes is selected as relays compared to

EE-OLSR and OLSR. It can be seen that the control overhead of the protocols

increase as the node mobility increases. Due to less link breakages, S-ELHR

generates much less overhead compared to EE-OLSR and OLSR, despite the

increase in node mobility. Also, a new route is reconstructed with route recovery

mechanism. Hence, routing overhead of S-ELHR is the least compared to EE-

OLSR and OLSR. This is shown in Fig. 3.8.

The performance of the protocols is evaluated under varying network size.

87

0

2

4

6

8

10

12

14

10 20 30 40 50 60 70 80 90 100 110

Co

ntr

ol O

ve

rhe

ad (

pkts

)

Number of Nodes

OLSRS-ELHR

EE-OLSR

Fig. 3.7: No. of Nodes vs Control Overhead per Data Packet

1.5

2

2.5

3

3.5

4

4.5

5

5.5

0 5 10 15 20 25 30 35

Contr

ol O

ve

rhe

ad

(pkts

)

Max. Speed (m/s)

OLSRS-ELHR

EE-OLSR

Fig. 3.8: Mobility vs Control Overhead per Data Packet

88

In the simulation, the maximum node speed is set to 25m/s and five CBR packets

are generated every second from 10 random source and destination nodes. As

shown in Fig. 3.9, the performance of OLSR drops as the network size increases

and this shows that OLSR does not scale with growing network size. The results

in Fig. 3.9 show that the S-ELHR maximizes the packet delivery ratio when the

number of nodes grows in the network. The source routing mechanism of S-ELHR

reduces the packet loss with more stable routes.

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90 100 110

Pa

cket

Deliv

ery

Ratio(%

)

Number of Nodes

OLSRS-ELHR

EE-OLSR

Fig. 3.9: No. of Nodes vs Packet Delivery Ratio

The performance under varying nodes speed is shown in Fig. 3.10. The

maximum speed of nodes has been increased from 5m/s to 30m/s in steps of 5m/s.

The protocols show similar delivery ratio when the node speed is low, but the

delivery ratio drops as node speed increases. The source routing and route recovery

mechanism of S-ELHR enable more packet delivery than the others. Thus, the

89

20

30

40

50

60

70

80

90

100

0 5 10 15 20 25 30 35

Pa

cket

De

live

ry R

atio

(%

)

Max Speed (m/s)

OLSRS-ELHREE-SOLSR

Fig. 3.10: Mobility vs Packet Delivery Ratio

packet delivery ratio of S-ELHR is higher when compared to EE-OLSR and OLSR,

exhibiting better resistance to node mobility.

Fig. 3.11 depicts the average end-to-end delay experienced by data packet

transmitted from source to destination against the maximum node speed 25m/s

with varying network size. S-ELHR has the lowest average end-to-end delay than

OLSR and EE-OLSR, as packets routed by S-ELHR experience a smaller path.

The generation of excessive control packets in OLSR and EE-OLSR consume a

large network capacity which in turn leads to larger delay.

Fig. 3.12 depicts the end-to-end delay against maximum node speed for a

network with 50 nodes. The figure shows that the delay incurred by the protocols

increases with increased node speed. This is due to frequent path breaks which

are associated with increased node mobility. However, S-ELHR minimizes the

90

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

10 20 30 40 50 60 70 80 90 100 110

Ave

rag

e E

nd-t

o-E

nd D

ela

y(s

)

Number of Nodes

OLSRS-ELHR

EE-OLSR

Fig. 3.11: No. of Nodes vs End-to-End Delay

0

0.05

0.1

0.15

0.2

0.25

0 5 10 15 20 25 30 35

Avera

ge E

nd-t

o-E

nd D

ela

y (

s)

Max. Speed (m/s)

OLSRS-ELHR

EE-OLSR

Fig. 3.12: Mobility vs End-to-End Delay

91

delay with route recovery mechanism by allowing the nodes to start forwarding the

packet through new route, after the path break.

3.4 CHAPTER SUMMARY

This chapter investigates the design of hybrid routing protocol S-ELHR, to

provide a stable and sustainable topology for routing in MANET. S-ELHR uses

CDS nodes for routing and it is selected based on stability metric which takes into

account the link connectivity time, energy and degree of nodes, to elect stable relay

nodes as dominating nodes. The selected dominating nodes perform the topology

discovery and data transmission. The S-ELHR computes routes on-demand and

follows a source-routing mechanism. To adapt to network topology changes, a

route recovery mechanism is also introduced.

The performance of the proposed protocol is evaluated under various

network sizes, node speed and compared to EE-OLSR and OLSR. The simulation

results show that the packet delivery ratio of S-ELHR is the highest in large

networks. As the proposed protocol uses the dominating nodes for relaying the

messages, the control overhead of S-ELHR is minimum, which in turn reduces

the energy consumption of the network. S-ELHR exhibits good scalability under

varying network sizes. The average end-to-end delay of S-ELHR is also the

least with route recovery mechanism. The source-routing mechanism with route

recovery of S-ELHR exhibits better performance with high node mobility. This

shows that S-ELHR protocol is suitable for dense mobile network with high traffic

loads.

CHAPTER 4

DESIGN OF WEIGHTED DOMINATING SET BASED

ROUTING PROTOCOL FOR AD HOC

COMMUNICATIONS IN EMERGENCY AND RESCUE

SCENARIOS

4.1 INTRODUCTION

As a consequence of disasters, emergency rescue team can deploy and

use MANET for emergency communications. A MANET deployed in such

emergency rescue operations is named as emergency MANET (e-MANET). e-

MANET consists of collection of wireless mobile nodes such as PDA, Notebook,

mobile phone or hand-held devices etc, communicating among them through

wireless channels. The important characteristics of this network are battery power,

low bandwidth and dynamic topology. It can provide an instant and distributed

peer-to-peer ad hoc communication solution for the rescue workers [72]. In

emergency scenarios, it is assumed that it is not possible to recharge the battery

powered devices.

Two nodes can communicate directly with each other if they are within

each other’s transmission range, otherwise intermediate nodes have to route the

messages for them. Energy efficiency, quick response time and stability are equally

important for routing in e-MANETs, since mobile nodes have homogeneous

lifetimes. The presence of dynamic and adaptive routing protocols will enable ad

92

93

hoc networks to be formed quickly, and then it ensures efficient communications

during the rescue operations [98, 128].

The on-demand routing protocols in MANET commonly use a route

discovery approach, to find a path from the source node to destination node. The

route control messages are broadcasted during the route discovery process. The

flooding of route request leads to congestion and also consumes more battery

power. Frequent route changes also result in frequent route computation process.

Therefore, it is crucial that MANET routing protocols must include information

on mobility and residual energy into the algorithm design to adapt the network and

node changes. The stability of path is an important design criterion to be considered

while developing multi-hop ad hoc communication protocols for e-MANET.

4.2 WEIGHTED CDS BASED ROUTING (WEIGHTED-CDSR)

From the literature review, it is observed that the major design objective of

the CDS approach is the construction of virtual backbone with minimum number

of nodes. The quality of the node to act as a dominating member is not at all

considered. There is a need for a generic protocol which is stable, scalable and

power efficient.

This chapter proposes a novel approach that integrates multiple factors

like link stability, mobility and energy into a single metric for Maximum Weight

Minimum CDS (MWMCDS) formation. An energy efficient reactive routing

protocol named Weighted-CDSR, is also proposed that takes advantage of the

MWMCDS. This protocol works in two stages, MWMCDS formation and Routing.

In this protocol, a node can play one of two roles: non-dominating or dominating

94

node. A non-dominating node just receives the packet, while a dominating node

rebroadcasts or relays the messages that it receives.

4.2.1 Network Model and Problem Statement

An emergency MANET is modeled as a vertex-weighted, connected and

an undirected graph G = (V,E,WT ) with vertex weight function WT : V → R+,

where V = {v1,v2, ...,vn} denotes the set of mobile nodes of e-MANET, E =

{(vi,v j) | i ≤ n, j ≤ n} ⊆ V × V denotes the communication links between the

nodes, and WT= {WTi | ∀i ∈ V} denotes the set of weights associated with the

nodes. A homogeneous network deployed in 2D plane, where nodes have same

transmission range is assumed. An edge exists between the two nodes if they are

within the transmission range of each other.

Definition 4.1. Maximum Weighted Minimum CDS (MWMCDS): Given a graph

G = (V,E,WT ) with node weight function W : V → R+, MWMCDS problem is to

find a minimum size CDS of G such that its total weight is maximum.

The problem is to find a MWMCDS which can work for the longest time.

4.2.2 Weight Calculation

The proposed weight WTu for a node u consists of three metrics, taking

into account the link stability (γLSu ), energy of the node (γEN

u ) and the node

mobility (γMOBu ). A link stability metric is used to compute the stability of the

communication with its neighbors. The energy metric is to choose the one with

more energy among the stable nodes. The mobility metric predicts the speed of

a node. A minimum mobility factor γMOBmin is assumed and is assigned with value

95

0.01. The WTu calculation includes γMOBmin if the mobility metric γMOB

u is 0. The

weight of a node u is defined as,

WTu =

γLS

u .γENu

γMOBu

, if γMOBu >0

γLSu .γEN

uγMOB

min, Otherwise

(4.1)

To compute the weight in (4.1), the following subsections explain the calculation

of the link stability (section 4.2.2.1), mobility metric (section 4.2.2.2) and energy

metric (section 4.2.2.3).

4.2.2.1 Link Stability Metric

The link stability (γLSu ) is calculated based on the Received Signal

Strength(RSS). Assume ∆Ruv is the variation of the RSS between nodes u and v

with ∆Ruv =(Rt+1

uv −Rtuv)

t . The distance between the two nodes is unchanged, when

∆Ruv = 0. When ∆Ruv>0, it means that the distance between the two nodes is

closing. When ∆Ruv<0, it means that distance between the two nodes is increasing.

The link stability between nodes u and v is defined as luv =1

(1−∆Ruv)and the stability

of a node u is,

γLSu = ∑

∀v∈Nu1

luv = ∑∀v∈Nu

1

1(1−∆Ruv)

(4.2)

4.2.2.2 Mobility Metric

The mobility (γMOBu ) of nodes can be considered in terms of neighbor

sets. Let A\B denote the symmetric difference between two sets A and B. Let

A∪ B denote the union of these sets. The mobility factor is then calculated as

96

the percentage of neighbors which remains the same between the sending of two

consecutive Hello packets:

γMOBu =

|Nt+11 (u)\Nt

1(u)||Nt+1

1 (u)∪Nt1(u)|

(4.3)

The main importance of this metric is to let the algorithm prefer more stable nodes

which are not likely to change their neighbor sets rapidly.

4.2.2.3 Energy Metric

The energy metric (γENu ) of a node is calculated as

γENu =

Eurm

Euinit

(4.4)

where γENu gives the ratio of energy currently available at u to its initial energy. It is

necessary to balance the traffic through the network nodes in order to increase the

minimum lifetime of the nodes. By using this metric, the most energy-rich nodes

are selected.

4.2.3 Algorithm for Maximum Weighted CDS Construction

Each node has a two-hop neighbor table for keeping the topology

information about the nodes that are at one-hop and two hops away. This local

topology information is used in selecting the dominating nodes. Every mobile node

transmits a hello message to its neighbors. The hello message of node u includes

<WTu, Nu1>. When a node receives a hello message from one of its neighbor, it

updates the two-hop neighbor table.

97

Initially all nodes are marked in WHITE color. After hello message

transmission, a node marks itself as a dominating node in BLACK color, according

to the marking procedure as described in Algorithm 4.1. A node is an intermediate

node if it has two unconnected neighbors (lines 2 to 4). A node u is covered by

another node v, when each neighbor of u is also neighbor of v, and WTu ≤WTv.

An intermediate node becomes an intergateway node if it is not covered by

any neighbor (lines 5 to 9). An intergateway node not covered by any pair of

connected neighboring nodes becomes a gateway node (lines 10 15). A gateway or

intergateway node is marked in BLACK color (line 16 and 17). All nodes marked

in BLACK forms the MWMCDS of the network. This process needs only two

messages. The first message allows the node to collect information about their two-

hop neighbors and the second is used by the node to inform its neighbors about its

final decision.

Theorem 4.1: Let S be the set of BLACK nodes and S is a Connected Dominating

Set.

Proof: The algorithm marks gateway and intergateway nodes in BLACK color. It

is necessary to prove the property for gateway node since gateway node is also an

intergateway node. Suppose that, on the contrary, the created set S is not a CDS.

Then, there exist some nodes which are not in S, and which have no neighbors with

nodes in S. Among such nodes, let x be the node with the largest weight value. If

all neighbors of x are non-intermediate, the graph is a complete graph. Otherwise,

let y be an intermediate neighbor of x with the largest weight value. Since, y is not a

gateway node, it is covered by one (u) or two ( u and w) of its neighbors and has the

lowest weight among them. Note that, if the cover set contains two nodes and one

of them w is non-intermediate, then u alone covers y and is an intermediate node.

Node x must be neighbor of u by the coverage condition. However, WTy<WTu

98

Algorithm 4.1: MARKING PhaseData: Marking of node u

Result: u is marked with BLACK or WHITE

1. intermediate(u) = intergateway(u) = gateway(u) = f alse

2. foreach v,w ∈ Nu1 do

3. if v 6= w && w /∈ Nv1 then

4. intermediate(u) = true

5. if intermediate(u) then6. intergateway(u) = true

7. foreach v ∈ Nu1 do

8. if Nu1 ⊆ Nv

1 && WTu ≤WTv then9. intergateway(u) = f alse

10. if intergateway(u) then11. gateway(u) = true

12. foreach v,w ∈ Nu1 do

13. if v 6= w && v ∈ Nw1 && Nu

1 ⊆ Nv1 ∪Nw

1 then14. if WTu ≤WTv && WTu ≤WTw then15. gateway(u) = f alse

16. if gateway(u) ‖ intergateway(u) then17. Color(u)← BLACK

contradicts the choice of y. Therefore, the set of nodes not in S and not neighbors

of any nodes from S is empty. Hence S is CDS. �

99

4.2.4 Routing in Weighted-CDSR

4.2.4.1 Route discovery and Maintenance

The proposed protocol uses three control messages namely Weighted-

RREQ, Weighted-RREP and Weighted-RERR for the route discovery. The

Weighted-RREQ is a broadcast message originated by the source node to find a

path to the destination. The Weighted-RREP is a unicast message, originated by

the destination to notify the route to the source node. The nodes are notified through

the Weighted-RERR message when the next hop link breaks. The formats of these

packets are shown in Table 4.1.

Table 4.1: Route Discovery Packet Formats

Packet Name FieldsWeighted-RREQ Type Hop Count

bcast id: Broadcast IDdest addr: Destination Node IDdest seq#: Destination Sequence Numbersrc addr: Source Node IDsrc seq#: Source Sequence Number

Weighted-RREP Type Hop Countdest addr: Destination Node IDdest seq#: Destination Sequence Numbersrc addr: Source Node IDlife time: Life Time

Weighted-RERR Type Dest Countdest addr: Unreachable Destination Node IDdest seq#: Unreachable Destination Seq#

The Weighted-RREQ packet includes address, sequence number of the

source and the destination nodes. It also includes the broadcast id and the hop

count. When a node wants to transmit a packet to a destination, it first checks its

100

two-hop table. If the destination node is in the two-hop table, then it is forwarded

directly. Every node, after receiving a Weighted-RREQ, processes the packet as

explained in Algorithm 4.2.

Algorithm 4.2: WEIGHTED-RREQ PACKET ProcessingInput: Node ′n′ receives a Weighted-RREQ packet ′p′

Output: Forwarding of Weighted-RREQ or Sending of

Weighted-RREP packet

1. if n == p.dest addr then

2. sendReply(Weighted−RREP)

3. else

4. if isDominatingNode(n) then

5. if nextHop = twoHopTable.lookU p(p.dest addr) 6= 0 then

6. send(p,nextHop) ;

7. else

8. if rt.lookU p(p.dest addr) then

9. seqNum = rt.getSeqNum(p.dest addr)

10. if seqNum ≥ p.dest seq# then

11. sendReply(Weighted−RREP)

12. else

13. rt.addEntry(p.src addr)

14. p.hop cnt ++

15. f orward(p)

16. else

17. drop(p)

The destination node replies with Weighted-RREP which follows the return

101

path to the source host (lines 1 and 2). A mobile host broadcasts the Weighted-

RREQ when it has to find a route to a destination. This happens when the

destination node is not in the two-hop table. When a dominating node receives this

request packet, it also checks its two-hop table. It directly sends the packet to the

destination if the destination is in the two-hop table (lines 3 to 6). If the destination

is not in the two-hop table, it first creates or updates a route to the previous hop

without a valid sequence number. It checks to determine whether it has received

this packet with the same source address and request id. If so, it discards the packet.

It creates a Weighted-RREP packet, when the destination sequence number in its

routing table entry is greater than or equal to the destination sequence number

in Weighted-RREQ (lines 7 to 11). Otherwise, it increases the hop cnt value in

the Weighted-RREQ by one and rebroadcasts RREQ to the network (lines 12 to

15). It also updates the routing table for forward route entry with last hop address

from which it has received the request packet. This packet is dropped by a non-

dominating node (lines 16 and 17). This process is repeated until it finds the

destination node.

The destination prepares the Weighted-RREP packet. The src addr is the

destination node address and dest addr is the source node address which initiates

the route discovery. The destination node copies these details from the Weighted-

RREQ packet. Every node after receiving a Weighted-RREP, processes the packet

as explained in Algorithm 4.3. When a node receives a Weighted-RREP packet,

it searches for a route to the previous hop. If needed, a route is created for the

previous hop, without a valid sequence number (lines 4 to 8). The node increases

the hop count value in the Weighted-RREP packet by one, in order to account

the new hop through the intermediate node (line 9). The forward route for this

destination is created if it does not already exist. The hop cnt field is incremented

102

Algorithm 4.3: WEIGHTED-RREP PACKET ProcessingInput: node ′n′ receives EAR-RREP packet ′q′

1. // If reply is for me, discard it

2. if n == q.dest addr then

3. f ree(q)

4. else

5. // forward route entry

6. if rt.lookU p(q.src addr) == 0 then

7. rt.addEntry(q.src addr)

8. nextHop = rt.lookU p(q.dest addr)

9. q.hop cnt ++

10. send(q,nextHop)

by one when it is forwarded by the dominating nodes in the established path. The

originator node knows the distance to the destination from this hop cnt field. The

life time denotes how long the route is valid. The Weighted-RERR message is sent

whenever a link break causes one or more destinations to become unreachable from

some of the node’s neighbors.



This section explains the performance of proposed routing protocol

Weighted-CDSR through simulation using the network simulator NS-2.34 [50].

A MANET is assumed with N mobile nodes moving in the simulation area. The

simulation parameters are listed in Table 4.2.

103

Table 4.2: Weighted-CDSR Simulation Parameters

Parameter Value

Area Size 1000 x 1000 m2


Traffic Type Constant Bit Rate (CBR)


MAC Protocol IEEE 802.11

Propagation Model Two-way Ground


Bandwidth 2 Mbps

Queue Size 50 packets


Transmitting Power 0.667W

Receiving Power 0.365W

Idle Power 0.1W

No. of Nodes 60,70,80,90,100,110,120 (default:75)

No. of Connections 1, 3, 5, 7, 9, 12, 15, 18, 20 (4 pkts/sec) (default:10)

Maximum Speed (m/s) 5, 10, 15, 20, 25, 30, 35 (default:20)

Experiments are repeated for 30 trials with different network sizes, load

conditions and mobility. The performance of Weighted-CDSR was compared to

the well-known on-demand routing protocols : DSR (Johnson et al. [73]), AODV

(Perkins et al. [116]) and DYMO (Chakeres et al. [27]). The reason for choosing

these protocols for comparison is that they have been adopted by IETF and a lot of

existing ad hoc routing protocols use these protocols as their measuring yardstick.

To verify the performance of the proposed protocol, the routing process over the

degree-based CDS proposed by Wu et al. [170], named Wu(Degree)-CDSR was

104

also considered.

To see the performance of MWMCDS construction algorithm, the

following metrics have been used.

- Average CDS Size: the fraction of the network nodes in the CDS.

- Average Route Length: the average number of hops in the established path

between each source-destination pair.

In rescue scenarios, the communication protocol must be energy efficient

with fast response time. It should also be scalable and reachable as more nodes

are added during these scenarios. To measure these requirements, the following

performance metrics have been used.

- Routing Overhead:(measure energy efficiency) the total number of routing

packets transmitted during the simulation time.

- End-to-End Delay:(measure response time) the average time for a data packet

to reach the destination from the source.

- Packet Delivery Ratio:(measure scalability and reachability) the ratio of data

packets received at the destination to the total packets transmitted.

- Energy Consumption:(measure energy efficiency) the total energy consumed

for sending and receiving the packets.


AODV proposed by Perkins et al. [116], is an on-demand routing protocol.

A source node initiates a route discovery process, when it wants to send a message

105

to a destination node and it does not already have a valid route to that destination

node. During route discovery, a source node broadcasts a RREQ packet to

its neighboring nodes. The neighboring nods then forward the request to their

neighboring nodes, and so on, until either the destination node or an intermediate

node that has a fresh route to the destination node is found. To ensure that all routes

are loop free, AODV uses destination sequence numbers. Before a node forwards

a RREQ packet to its neighboring nodes, it also records the node information in its

routing table. This information is used to construct the reverse route for the RREP

packet. If a link breaks and is detected, a RERR packet is used to notify other nodes

that the loss of that link has occurred.

DSR proposed by Johnson et al.[73], is an on-demand routing protocol

based on source routing. In DSR, mobile nodes maintain route caches that contain

the source routes of which the mobile node is aware. The route cache entries are are

continually updated as new routes are learned. When a source node wants to send

a message to a destination node, it looks up its route cache to determine whether

it already has a route to the destination node. If it has a route to the destination

node, it will use this route to send the message. But if the node does not have such

a route, it initiates the route discovery process by broadcasting a RREQ packet.

Each node receiving the RREQ packet checks whether it knows of a route to the

destination node. If it does not, it adds its own address to the route record of the

packet and then forwards the packet along its outgoing links. A RREP packet is

generated when either the RREQ packet reaches the destination node, or when it

reaches an intermediate node that contains in its route cache an unexpired route to

the destination node. If any link on a source route is broken, the source node is

notified using a RERR packet. The source node removes any route using this link

from its route cache. Then a new route discovery process must be initiated if this

106

route is still needed.

Dynamic MANET On-demand (DYMO) developed by Chakeres et al.

[27], is an on-demand routing protocol based on AODV. DYMO consists of

two operations: route discovery and management. During route discovery, the

originating node causes dissemination of a Routing Element (RE) throughout the

network to find the target node. Each intergateway node creates a route to the

originating node during dissemination. When the target node receives the RE

it responds with RE unicast toward originating node. During propagation, each

node creates a route to the target node. The routes have been established between

the originating node and the target node in both directions, when RE reached the

originating node. In DYMO, nodes also maintain their routes and links, in order

to react quickly to changes in the network topology. When a packet is received

for a route that is no longer available, the source of the packet is notified with

RERR packet. When the source nodes receives the RERR, it will re-initiate route

discovery if it still has packets to deliver.


Fig. 4.1. shows the average CDS size with varying network sizes. A

degree-based CDS is considered as an optimum size since it gives preference to

the nodes that have larger number of uncovered neighbors. The CDS constructed

based on degree is unstable, because it includes nodes with lowest energy or highest

speed. The weight calculation in the proposed work combines multiple metrics

such as link stability, node mobility and energy for forming a Stable CDS, which

can work for a longer time than degree-CDS. So, the CDS size in Weighted-CDS

is larger than Wu(degree)-CDS.

107

0

5

10

15

20

25

30

35

25 50 75 100 125 150 175 200 225

Avera

ge

CD

S S

ize

No.of Nodes

Wu(Degree)-CDSWeighted-CDS

Fig. 4.1: No. of Nodes vs CDS Size

The average route length metric gives a measure of how well a routing

protocol can perform over CDS. As expected, the higher the CDS size, the shorter

the routes. This is presented in Fig. 4.2. As the Weighted-CDS results in larger

size CDS than Wu(Degree)-CDS, the routes are shorter than the routes obtained in

Wu(Degree)-CDS. The graph induced by Wu(Degree)-CDS is sparse, this results

in longer routes.

The results in Fig. 4.3. show the generated routing overhead against node

mobility. The overhead of the all routing protocols increases with the increasing

node speed. This is because when node mobility increases, existing path may be

broken and more RREQ packets fail to reach their destinations. As a consequence,

more RREQ packets are generated and transmitted. The reduction in the routing

overhead is achieved in Weighted-CDSR as it constructs the most stable routes

108

2

2.25

2.5

2.75

3

3.25

3.5

3.75

4

25 50 75 100 125 150 175 200 225

Avera

ge

Rou

te L

eng

th

No.of Nodes

Wu(Degree)-CDSWeighted-CDS

Fig. 4.2: No. of Nodes vs Average Route Length

15

30

45

60

75

90

105

120

135

0 5 10 15 20 25 30 35 40

Ro

utin

g O

verh

ead x

10

3 (

packe

ts)

maximum speed (meters/sec)

AODVDSR

DYMOWeighted-CDSR

Wu(Degree)-CDSR

Fig. 4.3: Mobility vs Routing Overhead

109

with the longest duration in contrast with AODV, DSR and DYMO. Weighted-

CDSR reduces the rate of route reconstruction due to link breakage. Clearly the

reduction in route reconstruction rate in Weighted-CDSR reduces the rate of extra

control messages. Although Wu(Degree)-CDS results in minimum size CDS, it

does not guarantee an optimal network performance because the routing path is

broken frequently due to mobility of the nodes. Thus the routing overhead of

Wu(Degree)-CDSR is higher than Weighted-CDSR.

50

60

70

80

90

100

0 5 10 15 20 25 30 35 40

Packet D

eliv

ery

Ratio

(%

)


AODVDSR

DYMOWeighted-CDSR

Wu(Degree)-CDSR

Fig. 4.4: Mobility vs Packet Delivery Ratio

Fig. 4.4. plots the packet delivery ratio of the routing protocols against

the maximum node speed. The results show that the delivery ratio decreases with

the increased node mobility. This is due to the fact that the routes are highly

prone to breakage as the host speed increases. The weight based CDS construction

algorithms use a mobility metric to let the algorithm prefer more stable nodes which

are not likely to change their neighbor sets rapidly. Comparing the obtained results,

Weighted-CDSR has the highest delivery ratio as it establishes routes with stable

110

nodes. The packet delivery ratio of Wu(Degree)-CDSR is close to Weighted-CDS

when the maximum speed is less than 5m/s. The performance of Wu(Degree)-

CDSR degrades when the speed is more than 5m/s. This is due to the loss of

connection with the neighbors as only nodes with more neighbors are added in

Wu(Degree)-CDSR.

20

25

30

35

40

45

50

0 5 10 15 20 25 30 35 40

Avera

ge E

nerg

y C

on

sum

ed x

10

2 (

J)


AODVDSR

DYMOWeighted-CDSR

Wu(Degree)-CDSR

Fig. 4.5: Mobility vs Energy Consumption

Fig. 4.5. depicts the average energy consumption of the nodes against node

mobility. The energy consumption of the protocols increases with the increased

node speed. More energy is consumed due to the frequent route reconstruction

process resulting from link breakage. The Weighted-CDSR consumes less energy

compared to others because of the reduction in routing overhead. The energy

consumption of Wu(Degree)-CDSR is minimum among all the protocols when the

maximum speed is less than 5m/s. This is due to less number of nodes participate

in routing and the mobility does not affect the routing path frequently. When the

111

speed is greater than 5m/s, the frequent route breakage in Wu(Degree)-CDSR leads

to high energy consumption than Weighted-CDSR.

0

10

20

30

40

50

60

70

80

90

100

110

120

130

20 30 40 50 60 70 80 90 100 110 120 130

Routin

g O

ve

rhea

d x

10

3 (

packets

)

Number of nodes

AODVDSR

DYMOWeighted-CDSR

Wu(Degree)-CDSR

Fig. 4.6: No. of Nodes vs Routing Overhead

Fig. 4.6. shows the performance of the protocols in terms of routing

overhead versus network density. As shown in the figure, the routing overhead

generated by each of the protocols increases as the network density increases.

At high density with more than 100 nodes, the overhead generated by DSR and

Weighted-CDSR is reduced. The route cache mechanism of DSR reduces the

number of RREQ transmissions. In the case of Weighted-CDSR, the transmission

of RREQ is restricted only to the CDS nodes. As a consequence, the routing

overhead is reduced. When the network is sparse with less than 40 nodes,

Wu(Degree)-CDSR incurs more routing overhead as mobility of the nodes affect

the routing path. Since the CDS size grows with the increasing network size, the

routing overhead of Wu(Degree)-CDSR is least compared to AODV, DYMO and

DSR.

112

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

20 30 40 50 60 70 80 90 100 110 120 130

End

-to-E

nd

Dela

y (

sec)

Number of nodes

AODVDSR

DYMOWeighted-CDSR

Wu(Degree)-CDSR


Fig. 4.7. demonstrates the performance of all the protocols in terms of end-

to-end delay. When the network density increases, the transmitted RREQ packets

fail to reach the destinations due to high collisions and channel contention that have

been caused by excessive redundant transmissions. Therefore, the waiting time of

data packets is increased. The figure also reveals that, in sparse network with 30 or

40 nodes, especially when the network is poorly connected, the end-to-end delay

is higher in AODV, DSR and Weighted-CDSR. The end-to-end delay gets reduced

when the network connectivity increases with the increasing network density. But,

in dense network with more than 80 nodes, the complexity of the network increases

and hence the end-to-end delay increases. DSR needs to put the route information

in the data packets which creates longer delay as the network density increases.

The path accumulation of DYMO stores the routes to all nodes while processing

RREQ packets. Thus the delay is always less in DYMO. The number of nodes

in CDS increases when the network density increases. The denser CDS provides

113

shorter routes. Thus delay in Weighted-CDSR is less than AODV and DSR, when

the network density is high with more than 50 nodes. The end-to-end delay in

Wu(Degree)-CDSR is the highest among DYMO, AODV and Weighted-CDSR, as

minimum number of nodes experience heavy load.

50

60

70

80

90

100

20 30 40 50 60 70 80 90 100 110 120 130

Packet D

eliv

ery

Ratio

(%

)

Number of nodes

AODVDSR

DYMOWeighted-CDSR

Wu(Degree)-CDSR


Fig. 4.8. depicts the packet delivery ratio of all the protocols against

network density. When the network density is low, the network connectivity is

poor. The performance of AODV, DSR, DYMO and Weighted-CDSR drops when

the network density is set to low with 30 or 40 nodes. However, when the network

density is increased, the performances of AODV and Weighted-CDSR are good.

In DSR, both the route-reply cycle and data packet transmissions carry the source

route information. As a consequence, long delay is experienced by the packets

when the network density increases. Due to the long delay, the performance of

DSR drops with high network density. The path accumulation policy in DYMO

shows increased performance at low network density. The packet delivery ratio of

114

Wu(Degree)-CDSR is least among DSR, AODV and Weighted-CDSR due to heavy

load on less number of nodes. Also, the nodes in Wu(Degree)-CDSR experience

frequent route breakage due to mobility and energy. This leads to more packet

dropping.

10

15

20

25

30

35

40

45

50

55

60

65

20 30 40 50 60 70 80 90 100 110 120 130

Avera

ge E

ne

rgy C

onsu

med

x 1

02 (

J)

Number of nodes

AODVDSR

DYMOWeighted-CDSR

Wu(Degree)-CDSR

Fig. 4.9: No.of Nodes vs Energy Consumption

Fig. 4.9. shows the average energy consumption of all the protocols with

increasing node density. The route discovery operation works with less number

of nodes in the forwarding of the RREQ packets and the route re-computation is

less in Weighted-CDSR. As a consequence, the energy consumption is minimum

in Weighted-CDSR compared to others. The energy consumption of Wu(Degree)-

CDSR is the least among DYMO, DSR and AODV, when the network is dense

with more than 60 nodes. This is due to least number of nodes involved in routing.

As explained earlier, the routing overhead of Wu(Degree)-CDSR is high when the

network is sparse, which leads to the highest energy consumption.

115

50

60

70

80

90

100

1 3 5 7 9 12 15 18 20

Pa

cke

t D

eliv

ery

Ra

tio (

%)

Number of connections (4 pkts/sec)

AODVDSR

DYMOWeighted-CDSR

Wu(Degree)-CDSR

Fig. 4.10: No. of Traffic Sources vs Packet Delivery Ratio

The results in Fig. 4.10. show the packet delivery ratio for all the

protocols with the increasing traffic sources. When the number of flows increases,

the number of nodes initiating route discovery operation also increases. As a

consequence, more RREQ packets are generated and transmitted which lead to

a high consumption of the communication bandwidth. This leads to the delivery

of fewer data packets at the destinations, thereby degrading the delivery ratio. At

offered load of 20 flows, the high delivery ratio is achieved by Weighted-CDSR

with more stable nodes, when compared with others. The is due to the reduction of

the number of nodes involved in the dissemination of RREQ packets, which leads

to the reduction of routing overhead and packet collisions. As a consequence more

communication bandwidth is freed for data transmission. When the offered load is

minimum with 1pkt/sec, Wu(Degree)-CDSR provides the highest packet delivery

ratio. Due to energy depletion, the performance of Wu(Degree)-CDSR decreases

when the offered load increases.

116

4.4 CHAPTER SUMMARY

This chapter discusses the design of a stable, scalable and an energy

efficient, reactive routing protocol named Weighted-CDSR, suitable for MANETs.

A novel approach is introduced that integrates multiple factors like link stability,

mobility and energy into a single metric for maximum weighted CDS formation.

The proposed protocol uses CDS for the broadcast and data transmission. The

CDS selection based on combined metrics, increases the path availability for a

longer period. The performance of the proposed work is compared to degree-based

and well known reactive routing protocols with different mobility speed, network

density and number of connections. Simulation results show that the number of

control packet transmission is reduced because only the dominating nodes act as

routers to route the messages of other nodes. The energy consumption is also less

due to the reduction in route re-computation. With stable backbones, Weighted-

CDSR gives high packet delivery ratio in all the cases. As the network congestion

is reduced, more bandwidth is available for data transmission in Weighted-CDSR,

it performs well when the number of flows increases. As more nodes are added to

the backbone when the network density is high, the end-to-end delay is minimum

in Weighted-CDSR. Based on the increased packet delivery ratio, improved energy

efficiency, lesser routing overhead and lesser end-to-end delay, the proposed

protocol Weighted-CDSR, is well suited for the requirements of emergency and

rescue scenarios.

CHAPTER 5

DESIGN OF AN EGO CENTRALITY AND CONTACT

DURATION BASED BACKBONE ROUTING

PROTOCOL FOR MOBILE OPPORTUNISTIC

NETWORKS

5.1 INTRODUCTION

Recently, there is a growing interest in the research towards mobile

opportunistic networks or intermittently connected networks (e.g., vehicular ad

hoc networks, mobile sensor networks, and pocket switched networks), in which

the communication between mobile nodes is opportunistic. Mobile Opportunistic

Networks are kind of Delay Tolerant Network (DTN), which offers support for

communication scenarios where the nodes are sparse and the connectivity between

them are short-lived due to high node mobility. The routing approach in DTN uses

store-carry-forward mechanism that allows intermediate nodes to store messages

for an extended period of time (called carry) and to deliver messages towards

destination when an opportunity to forward a message becomes available. Thus,

in contrary to MANET approach, the DTN can deliver messages also when an

instantaneous end-to-end path between the nodes does not exist. However, many

protocols in this network aim to ensure delivery by creating multiple message

copies, which can lead to congestion and decreased performance especially in

dense networks [23, 77, 106, 114, 149].

117

118

The consideration of social characteristics present a new direction in

the design of data routing for DTN [136]. Centrality is one of the most

useful mathematical measure developed by social network analysts to capture the

structural properties of social relationship. It aims to identify the most important

vertices within the graph that represents a network [78]. Centrality metrics could

be based on degree of a vertex [51] or geodesic distance between them [52].

Betweenness centrality measures the proportion of shortest paths between any

pair of nodes passing through a specific node [52]. In the context of DTN, ego

network is defined as a network consists of single node and its 1-hop neighbors

with which the ego has direct contact [49]. Ego Centrality for an ego network is

the representation of the nodes with which the ego node has come into contact

[49]. In DTN routing, the utility of a node is a measure of the contribution of the

node to enhance a routing metric such as throughput or an end-to-end delay. In

probabilistic routing, the messages are forwarded to mobile nodes that have higher

probabilities of meeting the destination nodes named contact frequency utility. A

duration utility can reflect the transmission capacity between a pair of nodes with

higher accuracy than a contact frequency utility [91].

Most of the works only consider the single feature of the node’s behavior,

such as centrality, interest, encounter frequency. But these works have some

difficulties in representing the social relations. The numerical features, like

centrality and the number of interest, cannot reflect the node’s forwarding ability.

Since the successful delivery not only depends on the number of connections,

but also the encounter time and frequency. So, in this study, a backbone routing

protocol named BRP is proposed, considering both contact frequency and contact

duration to achieve high throughput and low end-to-end delay. Also, to improve

the forwarding performance in a sparse network, BRP uses a buffer mechanism.

119

5.1.1 Social-aware Routing in Mobile Opportunistic Networks

This section presents the routing protocols based on centrality, contact

duration and frequency.

Daly et al. [36] proposed the concepts of node’s centrality and the social

similarity to be used as the link metric. Centrality indicates the number of

connections of the node to other nodes. Social similarity is the number of common

friends with the destination. A node with higher centrality and social similarity

was chosen as the relay node. Hui et al. [70] presented Bubble Rap Forwarding,

where forwarding decision was made based on the node’s centrality. The message

is forwarded to the node with higher global centrality in the global network,

and then forwarded to the node with higher local centrality in the destination

node’s community. Vazquez et al. [160] proposed both centralized and distributed

algorithms based on the centrality metrics. They constructed a CDS that includes

the most central nodes. They evaluated the resulting performance using the three

most common centrality measures: degree, closeness and betweenness.

Liu et al. [95] proposed three algorithms for computing mobile backbone

in mobile opportunistic network. Two of the algorithms exploit the sociality

feature of mobile opportunistic networks by computing node betweenness, which

counts the times that a node appears in all shortest paths. They proposed

centralized algorithms for backbone formation using betweenness measure and

delay weighted betweenness. For a given opportunistic network G and an integer

k, their algorithms construct a backbone of size k with the objective of minimizing

the total packet delivery cost of the network. Yang et al. [185] developed an

adaptive backbone based routing approach with diverse connection predication

characteristics. According their approach, when the past meeting frequency of two

120

nodes is known, an edge between these two nodes use the frequency as the weight

of this edge. In this way, a weighted graph is derived with a certain degree of

knowledge on node movements. The edge weights were used to predict expected

delivery latency. They proposed a localized algorithm for the delay tolerant CDS

(DTCDS) for DTNs. They designed an accumulated node coverage condition for

the minimum equally effective DTCDS problem, where each node after obtaining

the meeting frequency with other nodes, decides whether to serve as DTCDS

node and help with forwarding or withdraw, in a localized manner. Kim et al.

[82] defined an expanded ego network by comprising the ego’s 2-hop neighbor

nodes as well as the ego’s 1-hop ones. In DTN, the expanded ego network can be

easily self-configured at a node and it can contain more network information than

the ego network. Therefore, it is expected that the effectiveness of the expanded

ego network will be higher than the one of the ego network in terms of data

routing and dissemination. They examined the relationship among the expanded

ego betweenness, the ego betweenness, and the betweenness of the entire network

for a node.

According to the related works, the issues of the available socially aware

routing protocols in DTN can be summarized as the following two aspects: (1)

Multi-copy based routing generates a lot of message copies, which leads to great

system overhead. (2) Most of the socially aware single-copy based routing only

considers the single feature of the node’s behavior. So, an improved single-copy

forwarding protocol is needed that reduces the overhead and considers multiple

social features of the node’s behavior. This study proposes an efficient way to form

a CDS based on ego-centrality, contact duration and frequency. It also explains a

backbone routing protocol that uses the backbone (dominating) nodes for single-

copy forwarding of a message.

121

5.2 BACKBONE BASED ROUTING PROTOCOL (BRP)

The proposed protocol BRP does not need any distributed knowledge

of the global network topology to generate a backbone. Each node u in the

network maintains one-hop neighbor table, contact table, ego centrality value and

the dominating status. The one-hop neighbor table contains the ID and the ego

centrality value (as explained in section 5.2.2.1) of its neighbors. The contact table

of node u contains the following fields for each encountered node v: the node ID

of v, the sum of inter-contact times between the nodes u and v denoted by σuv, the

number of encounters χuv, the end time of the last encounter teuv and the start time

of the ongoing encounter tbuv. These fields are used to calculate the average inter-

contact time of links (explained in section 5.2.2.2). The dominating status of node

is either true (dominating) or false (non-dominating).

5.2.1 Network model and Problem Statement

A mobile opportunistic network is considered that consists of N mobile

nodes, denoted by a set V = {n1,n2, ...,nN}. Each node in the network is mobile

and any pair of nodes can communicate with each other when their distance is

within their communication range. The set E = (u,v) denotes the communication

link between the nodes. An opportunistic network is modeled as a graph G(V,E),

where V is the set of nodes in the network and E is the set of communication

links between any pair of nodes. Each edge (u,v) has a weight ξuv representing an

average inter-contact time of the link. A homogeneous network where nodes have

same transmission range is assumed. A subset of network nodes can construct a

backbone, which is called as CDS. Only the nodes in the CDS can act as relay for

multi-hop communication to send a message from source to destination.

122

Definition 5.1. Ego-Centrality Contact-Duration Connected Dominating Set

(ECCDS): Given a vertex weighted, edge weighted, and an undirected graph

G(V,E,VW,EW ) representing a mobile opportunistic network, where V denotes

the nodes in the network, E denotes the communication links, VW = {ϑ1,ϑ2, ...ϑn}

is the set of weights associated with the vertices (i.e., ϑx is ego-centrality of x)

and EW = {ξ1,ξ2, ...ξm} is the set of weights associated with the edges ( i.e., ξi

is average inter-contact time of link i). Consider a subset of nodes S ⊆ V and

E ′ = {(u,v) | u,v ∈ S,(u,v) ∈ E}, S is said to be ECCDS if

- S is CDS,

- ∑∀v∈S

ϑv is maximum and

- ∑∀e∈E ′

ξe is minimum.

5.2.2 Ego-Centrality and Contact-Duration based ConnectedDominating Set (ECCDS)

5.2.2.1 Ego Centrality Calculation

An efficient algorithm to compute ego centrality is proposed by Everett et

al. [49]. Each node represents its ego network (one-hop neighbors) by means of the

adjacency matrix A. The elements of this matrix are given by

A[i, j] =

1, if there is a link between i and j

0, Otherwise

In an ego network, every pair of non-adjacent nodes must have a geodesic

of length 2 which passes through ego. Therefore, when i 6= j, and being 1 the

123

matrix with all its elements equal to 1, the expression A2[1−A]i, j gives the number

of shortest paths with length 2 that links nodes i and j. Thus, the ego centrality is

the sum of the reciprocal of the resulting non-zero elements. Since the matrix is

symmetric ego-centrality calculations need to consider only the zero entries above

the leading diagonal and calculate A2[1− A]i, j for those entries [49]. Thus the

egocentric value of node v is

ϑu = ∑j>i ∧ [1−Ai j]6=0

1A2

i j, A2

i j 6= 0 (5.1)

5.2.2.2 Calculation of Average Inter-Contact time

When two nodes u and v go out of the transmission range of each other, the

last inter-contact time between nodes u and v is set to ∆uv = teuv - tb

uv. The sum of

inter-contact time between nodes u and v is σuv = ∆′uv + ∆uv, where ∆′uv is the sum

of the inter-contact time of node u to v before last encounter. Then the number of

encounters χuv is increased by 1. The average inter-contact time between nodes u

and v is expressed as

ξuv =

+∞, if χuv = 1

σuvχuv−1 , if χuv > 1

(5.2)

5.2.2.3 Algorithm for ECCDS Construction

Yang et al. [184] have proposed an adaptive backbone based routing

protocol with delay tolerant connected dominating set. They used the contact

frequency to measure the delivery latency of a link. They have also formulated

accumulated delivery latency and accumulated node coverage condition for a node

124

to mark or unmark its dominating status. Similar to that, accumulated average inter-

contact time and accumulated node coverage condition are proposed, considering

the average inter-contact time of links (instead of contact freqency) and ego

centrality of nodes (instead of node ID).

Accumulated Average Inter-contact Time: Assume that there are t node

disjoint paths connecting nodes u and v, Pi with sum of average inter-contact

time of σP1,σP1, ...σPt . The average of inter-contact time between u and v

through these paths is

ϖuv =1t∗

t

∑i=1

σPi. (5.3)

Accumulated Node Coverage Condition (ANCC): As in Fig. 5.1, node v is

unmarked if for any two neighbors of v, u and w, a group of replacement

paths, P1,P2.....Pt exists connecting u and w such that

1. each intermediate node on any replacement path Pi, (i = 1,2, ..., t), has a

higher Ego-centrality than v and

2. the accumulated average inter-contact time of the group of replacement

paths is smaller than or equal to the average inter-contact time of path

u,v,w. i.e.,

ϖuw ≤ (ξuv +ξvw

2) (5.4)

twvus

P1

P2

Fig. 5.1: Accumulated Node Coverage Condition

125

Based on ANCC, Algorithm 5.1 is applied to form ECCDS.

Each node in the network runs this localized algorithm when a new or

updated information is collected during the contact. All marked nodes form a

ECCDS of a network and are responsible for relaying the messages.

Algorithm 5.1: ECCDS ConstructionData: Each Node u decides its dominating status

Result: ECCDS as Backbone

1. Node u applies accumulated node coverage condition

2. if ANCC(u) == true then

3. u.dominating = f alse

4. else

5. u.dominating = true

5.2.3 Routing in BRP

5.2.3.1 Message Forwarding

The pseudo-code for message forwarding in BRP is given in Algorithm

5.2. The message is received by a node, when it is the destination of the message

(lines 1 to 2). When the receiving node is a dominating node, it checks whether

the destination is a one-hop neighbor and sends the message if so (lines 4 to 6).

Otherwise it checks whether any new or updated information is collected in the

contact table during the last Hello message transmission. There may be situations

where a node is disconnected or stable for a longer period, no new or updated

information is collected in such cases. The packet delivery ratio can be increased

126

if the possibility of reaching the destination is checked and packets are buffered in

case of stale routes (lines 8 to 9).

Algorithm 5.2: MESSAGE PACKET ProcessingInput: Node n receives a message p to destination d

Output: Forwarding or buffering of p

1. if n == d then

2. receive(p)

3. else

4. if isDominating(n) == true then

5. if oneHopNeighbor.lookU p(d) 6= 0 then

6. send(p,d)

7. else

8. if no new entry or udpate in Contact Table then

9. bu f f erMessage(p)

10. else

11. if isDuplicate(p) == f alse then

12. broadcast(p)

13. else

14. drop(p)

To cope with disruptions, BRP buffers the message instead of discarding

it. The rationale behind this behavior is that a buffered message may be sent later

when a connection becomes available. Otherwise, nodes broadcast the message if

it is not already transmitted (lines 11 to 12). The message is dropped when the

receiving node is not a dominating node (lines 13 to 14) .

127

5.2.3.2 Forwarding of Buffered Messages

The pseudo-code for buffered message forwarding is given in Algorithm

5.3. Each node, apart from buffering a packet, also decides when a buffered packet

can be sent. Whenever the contact table is updated, a node sends a packet if the

destination is a one-hop neighbor (lines 1 to 4). Otherwise, it broadcasts all the

messages and removes them from the buffer to save transmission bandwidth and

storage (lines 5 to 7).

Algorithm 5.3: BUFFERED MESSAGE ProcessingInput: Dominating node n has messages in its Buffer

Output: Forwarding of buffered messages

1. if contactTable is updated then

2. foreach message p with destination d in Buffer do

3. if oneHopNeighbor.lookU p(d) 6= 0 then

4. send(p,d)

5. else

6. broadcast(p)

7. removeMessage(p)



To evaluate the performance of proposed protocol, simulations are

conducted for the protocols PROPHET [94], Adaptive-Routing [84] and

128

Table 5.1: BRP Simulation Parameters

Parameter Value

Area Size 3000 x 3000 m2


Traffic Type TCP

Message Size 10kB

MAC Protocol IEEE 802.11g

Propagation Model Free Space


Bandwidth 54 Mbps

Queue Size 1000


Transmitting Power 0.667W

Receiving Power 0.365W

Idle Power 0.1W

No. of Nodes 30, 40, 50, 60, 70, 80 (default:40)

Pause time 10s

Maximum Speed (m/s) 1, 5, 10, 15, 20, 25 (default:10)

Message Sending Rate/Node 1, 3, 5, 7, 9, 11, 13, 15 (default:25)

Message Lifetime(sec) 100 to 1000 (default:750s)

CoMANDR [126]. All the simulations are performed in NS-2 [50]. The simulation

parameters are listed in Table 5.1.

In the simulation, N mobile nodes are deployed in 3000 x 3000 m2 area.

The wireless channel model is similar to Adaptive-Routing [84]. The mobility

model used in the simulation is random waypoint. The source and destination

nodes are selected randomly and each node generates 25 messages. All messages

129

have a lifetime of 750s. The default bundle size is set to 10kB for DTN routing. All

experiments are repeated 30 times for a simulation time of 3600s. The results show

the mean values of all simulation runs and error-bars denote the 95% confidence

interval.

The following metrics have been used to evaluate the performances of the

protocols.

- Message Delivery Ratio: It shows the ratio of successfully received messages

at the destination to the number of created messages.

- End-to-End Delay: It represents the time that is needed to transfer a message

from the source to the destination.

- Hop Count: It gives the length of a path (number of nodes) for a message to

reach the destination from the source.

- Number of forwarded messages : It denotes the average number of messages

forwarded by a node during the simulation time.


The PRoPHET algorithm proposed by Lindgren et al. [94] studied pairwise

contacts to make routing decisions. PRoPHET reduces the overhead by calculating

a node’s delivery predictability for a specific destination. This metric is calculated

so that a node with a higher value for a certain destination is estimated to be a better

candidate for delivering a bundle to that destination. It is later used when making

forwarding decisions. According to this protocol, nodes exchange and update the

delivery predictability when they meet other nodes. Also, a node exchanges all

messages to a node when the other node has a higher delivery probability. The

130

delivery predictability for a node was based on the number of encounters, the

age of these encounters, and the existence of a transitive property for mutually

encountered nodes.

Adaptive-Routing, a routing scheme proposed by Lakkakorpi et al. [84],

uses only local information to transmit the messages from source to destination

using either AODV or DTN routing, depending on current node density, message

size, and path length to destination. The Adaptive-Routing approach is to choose in

the sending node whether to use DTN (e.g., epidemic or spray and wait) or AODV

for message delivery. The benefit of the approach is that both routing protocols

can remain untouched, and intermediate node need to support only pure DTN or

AODV functionality. The decision on which protocol to use for transmitting a

given message from source to destination is made on application level. They have

used the TCP-Convergence layer as described in RFC7242 [112] to support bundle

protocol.

CoMANDR, a combined MANET/DTN Routing proposed by

Raffelsberger et al. [126], uses the routing table that is calculated by the

MANET protocol to route packets over a multi-hop end-to-end path. To cope

with disruptions, CoMANDR utilized two mechanisms: packet buffering and

utility-based forwarding. If the routing table contains no valid entry for a packet’s

destination, CoMANDR buffers the packet instead of discarding it. The decision

to which node a buffered packet should be forwarded is based on a utility function.

CoMANDR used a modified version of the PROPHET meeting probability

calculation function to calculate the utility of a node. In contrast to the PROPHET

protocol, that only considers when two nodes directly meet (i.e., there is a direct

link between the nodes), CoMANDR also considers multi-hop information from

the routing table. When a node i has a routing table entry for another node j (with

131

a distance less than infinite), CoMANDR considers node i and j to be in contact.

This allows nodes to exploit multi-hop paths to determine contacts with other

nodes.


The results in Fig. 5.2 to Fig 5.4 demonstrate the message delivery ratio

of the protocols with varying network size, node’s speed and message lifetime.

Fig. 5.2 presents that the proposed work achieves high message delivery ratio

in both sparse and densely connected networks. In BRP, as relay nodes with

minimum average inter-contact time and maximum ego-centrality are selected,

messages are forwarded quickly to the destination due to more contacts of nodes.

The proposed protocol BRP also buffers messages during network disconnections

to increase the packet delivery ratio. Thus it delivers more messages even when

the network is sparse. The connectivity between the nodes increases with the

network size, BRP delivers more messages through the backbone nodes. Being an

extended PROPHET routing protocol with buffering scheme, CoMANDR achieves

high message delivery ratio than PROPHET. The epidemic approach of Adaptive-

Routing leads to high delivery ratio with more network overhead.

The results in Fig. 5.3 demonstrate that message delivery performance of

the protocols increases with the increasing speed. The probability of reaching the

destination is high when node’s speed is high. Thus the delivery ratio is increasing

with the increasing node speed. As the backbone nodes are selected with ego

centrality and inter-contact time of links, the possibility of delivering the packets

to destination is high in BRP than the other protocols. Due to more contacts during

high mobility, more messages are delivered than low mobility. The buffering

scheme of CoMANDR enables it to achieve high delivery ratio with increasing

132

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

20 30 40 50 60 70 80 90

Me

ssage

De

live

ry R

atio

Number of Nodes

PROPHETAdaptive-Routing

CoMANDRBRP

Fig. 5.2: No. of Nodes vs Message Delivery Ratio

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 5 10 15 20 25

Messa

ge

Deliv

ery

Ra

tio

Max. Speed (m/s)


CoMANDRBRP

Fig. 5.3: Mobility vs Message Delivery Ratio

133

node’s speed. When the node’s speed is less, the probability of a node to reach the

destination is also less, thus both PROPHET and Adaptive-Routing achieve lower

delivery ratio. This is reverse when node’s speed is high.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 100 200 300 400 500 600 700 800 900 1000 1100

Message

Deliv

ery

Ra

tio

Message lifetime(TTL) in seconds


CoMANDRBRP

Fig. 5.4: Message Lifetime vs Message Delivery Ratio

The results in Fig. 5.4 illustrate that more messages are delivered with the

increasing message lifetime. As more messages are dropped with less lifetime,

all the protocols achieve less message delivery ratio. The BRP protocol uses few

nodes for relaying the messages, which results in less congestion, thus achieves the

highest delivery ratio. With buffering scheme, the CoMANDR protocol achieves

higher delivery ratio than Adaptive-Routing and PROPHET. When the message

lifetime is high (more than 700s), more messages are alive in the network due

to multiple copies created by both Adaptive-Routing and PROPHET. Due to high

message congestion, these two protocols achieve less delivery ratio compared to

BRP and CoMANDR.

134

0

50

100

150

200

250

300

350

400

450

500

20 30 40 50 60 70 80 90

En

d-t

o-E

nd D

ela

y (

s)

Number of Nodes


CoMANDRBRP


Fig. 5.5 illustrates that the end-to-end delay of the four protocols increases

as the number of nodes increases in the network. The number of nodes in the

backbone is increasing with network density which leads to the increasing end-to-

end delay. The BRP achieves the lowest end-to-end delay because nodes with the

most centrality value are selected and fewer nodes participate in routing. End-

to-end delay of CoMANDR protocol is lesser compared to Adaptive-Routing

and PROPHET, because it buffers the messages. Both Adaptive-Routing and

PROPHET create multiple message copies, results in more congestion and longer

delays.

Fig. 5.6 shows the end-to-end delay of the protocols with respect to varying

node speed. The BRP protocol achieves the lowest end-to-end delay compared to

PROPHET and CoMANDR, because of message forwarding through short contact

links and single copy forwarding. The end-to-end delay of probability based

135

protocols PROPHET and CoMANDR is longer because the message redundancy

would be serious as the messages are flooded in the network. The epidemic

approach of Adaptive-Routing forward the message throughout the network until

each node has a copy. The chances of meeting the destination are more with the

increasing node’s speed, thus Adaptive-Routing results in the lowest end-to-end

delay.

0

50

100

150

200

250

300

350

400

450

500

1 5 10 15 20 25

End-t

o-E

nd

Dela

y (

s)

Max. Speed (m/s)


CoMANDRBRP

Fig. 5.6: Mobility vs End-to-End Delay

As the message lifetime increases, all protocols deliver more messages

to the destinations. Thus an end-to-end delay is increasing with the increasing

message lifetime as shown in Fig. 5.7. As expected, the epidemic and probability

based routing scheme leads to congestion, thus longer delays in Adaptive-Routing,

PROPHET and CoMANDR.

Fig. 5.8 to Fig. 5.10 show the average number of hops per message with

respect to varying network size, node’s speed and message lifetime. The results in

136

0

50

100

150

200

250

300

350

400

450

500

0 100 200 300 400 500 600 700 800 900 1000 1100

En

d-t

o-E

nd D

ela

y (

s)



CoMANDRBRP

Fig. 5.7: Message Lifetime vs End-to-End Delay

Fig. 5.8 show that the average number of hops achieved by BRP is the least due to

single copy forwarding through relay nodes. The multi-copy protocols Adaptive-

Routing and PROPHET have a higher hop count as they are able to deliver more

messages via long paths. The CoMANDR’s utility-based forwarding technique

finds more paths but it needs more hops.

Fig. 5.9 shows the average hop count of the protocols with varying node

speed. Frequent path breaks happen when the maximum speed is more than 10m/s.

Thus the end-to-end path breaks while the message is on its way to the destination,

which leads to the increasing average hop count. The average hop count of BRP

is the least when compared to PROPHET and CoMANDR, because the chances

of meeting the destination node is high with the increasing speed. The epidemic

approach of Adaptive-Routing protocol creates a copy of the message when it meets

new nodes. A node will have new neighbors when node’s speed is high. Thus it

137

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

20 30 40 50 60 70 80 90

Ho

p C

oun

t

Number of Nodes


CoMANDRBRP

Fig. 5.8: No. of Nodes vs Hop Count

1

1.25

1.5

1.75

2

2.25

2.5

2.75

3

3.25

3.5

3.75

4

1 5 10 15 20 25

Hop C

ount

Max. Speed (m/s)


CoMANDRBRP

Fig. 5.9: Mobility vs Hop Count

138

replicates the messages and the copies will reach the destination quickly when

the speed is high. Thus Adaptive-Routing protocol shows less hop count with the

increasing node’s speed.

1

1.25

1.5

1.75

2

2.25

2.5

2.75

3

3.25

3.5

3.75

4

0 100 200 300 400 500 600 700 800 900 1000 1100

Hop

Cou

nt



CoMANDRBRP

Fig. 5.10: Message Lifetime vs Hop Count

The average hop count with varying message lifetime is shown in Fig. 5.10.

The number of messages alive in the network depends on its lifetime. When the

message lifetime is high, more messages are buffered or forwarded. As the message

congestion is more with the increasing lifetime, other protocols experience longer

hop count. The results show that BRP has the least hop count with relay nodes and

reduced overhead.

Fig. 5.11 to Fig. 5.13 show the total number of message forwards of nodes

against network density and maximum node’s speed. The results in Fig. 5.11 show

that BRP performs the best. The reduced number of forwarded messages is due

to the shorter paths through relay nodes found by BRP. The message forwarding

139

0

200

400

600

800

1000

1200

1400

1600

1800

2000

20 30 40 50 60 70 80 90

No.o

f F

orw

ard

ed M

essag

es(p

kts

)

No. of Nodes


CoMANDRBRP

Fig. 5.11: No. of Nodes vs No. of Forwarded Messages

is higher in Adaptive-Routing where the message continues to be forwarded

throughout the network until each node has a copy. The number of forwarded

messages by PROPHET and CoMANDR are lesser than Adaptive-Routing.

Fig. 5.12 shows the number of forwarded messages against node’s speed.

The BRP protocol achieves the least number of forwarded messages as it forwards

the messages through the short lived links. The epidemic and probability approach

based protocols create a copy of the message when it meets new nodes. A node

will have new neighbors when node’s speed is high. Thus the number of forwarded

messages by other protocols is higher than BRP with the increasing node’s speed.

The results in Fig. 5.13 demonstrate that the replication based protocols

PROPHET and Adaptive-Routing have higher number of forwarded messages

when message lifetime is increased. Due to single copy forwarding, the forwarded

messages are lesser in CoMANDR compared to Adaptive-Routing and PROPHET.

140

0

50

100

150

200

250

300

350

400

450

500

1 5 10 15 20 25

No

. of

Fo

rward

ed

Me

ssag

es(p

kts

)

Max. Speed (m/s)


CoMANDRBRP

Fig. 5.12: Mobility vs No. of Forwarded Messages

0

50

100

150

200

250

300

350

400

450

500

0 100 200 300 400 500 600 700 800 900 1000 1100

No.o

f F

orw

ard

ed M

essag

es(p

kts

)



CoMANDRBRP

Fig. 5.13: Message Lifetime vs No. of Forwarded Messages

141

Instead of allowing all nodes to do routing, BRP uses the backbone nodes. Thus

BRP achieves the lowest number of forwarded messages when compared to the

others.

0

20

40

60

80

100

120

140

160

180

200

1 3 5 7 9 11 13 15

No.o

f F

orw

ard

ed M

essag

es(p

kts

)

No. of Message/node


CoMANDRBRP

Fig. 5.14: No. of Messages vs No. of Forwarded Messages

Fig. 5.14 illustrates the number of forwarded messages with the number

of messages. The number of forwarded messages is high in Adaptive-Routing as it

replicates the messages until each node gets a copy. The probability based protocols

PROPHET and CoMANDR have lesser forwards compared to Adaptive-Routing.

The proposed protocol BRP achieves the least number of forwarded messages as

the messages are forwarded to the destination within short duration.

5.4 CHAPTER SUMMARY

This chapter addresses the feasibility of using the ego centrality metric

and average inter-contact time between the nodes to select relays that serve

142

as a backbone for routing. A localized algorithm is proposed to select an

efficient backbone. A routing protocol named BRP has been proposed for

mobile opportunistic network. In BRP, the selected backbone nodes forwards

a single copy or buffers the messages. The performance of BRP has been

verified through extensive simulations and compared with well known protocols

PROPHET, Adaptive-Routing and CoMANDR. The centrality and inter-contact

time based relay selection works well in most cases even if a minimum density of

nodes exist. BRP delivers more messages with well and intermittently connected

networks. The reduction of the number of relays involved in the path selection

results in minimum hop count and end-to-end-delay. The number of forwarded

messages of BRP is also minimum compared to other protocols due to single copy

transmission and relay based forwarding. The proposed protocol BRP achieves the

performance as like epidemic routing without redundantly forwarding the message.

In addition, BRP performs better when compared to PRoPHET, Adaptive-Routing

and CoMANDR.

CHAPTER 6

DESIGN OF CONNECTED K−COVERAGE

TOPOLOGY CONTROL FOR AREA MONITORING IN

WIRELESS SENSOR NETWORKS

6.1 INTRODUCTION

WSN consists of large number of sensor nodes deployed to cover a

Field of Interest (FoI). The applications of WSN include monitoring, control and

surveillance. Each sensor node has a sensing radius within which it can sense data.

It has a communication radius within which it can communicate with another node.

Each of these nodes will collect raw data from the environment, do local processing

and communicate possibly with each other in a multi-hop fashion to transmit the

data to the sink. Coverage addresses on how well the sensor nodes cover the FoI.

Sensor nodes are prone to failure unexpectedly due to the interference and energy

depletion. Due to the severe resource limitations, coverage is the most fundamental

and challenging issue in WSN [68, 104, 199].

Coverage can be classified into area and point. The monitoring area is

fully covered by a set of sensor nodes in area coverage, whereas in point (target)

coverage, every point in the area is covered. Multiple coverage is needed for the

purpose of reliability in case of failure. In densely deployed sensor networks, it

will be useful to select a subset of sensor nodes to keep active at any given time

to conserve energy and prolonging the sensor network lifetime. The set of active

143

144

nodes must be connected to transmit the data to the sink. It is desirable to have

several sensor nodes monitor the same area and let each sensor node report via

different routes to avoid losing an important event. Thus energy efficiency and

reliability are equally important design challenges in WSN [25, 54].

A CDS based topology control in WSNs is a kind of hierarchical method

to ensure sufficient coverage while reducing redundant connections in a relatively

crowded network. A CDS can preserve 1-coverage [9, 10, 184, 188]. However,

fault tolerant coverage is necessary in WSN because nodes prone to failure and

turn on or off frequently [19, 80]. Thus, it is important to maintain a certain

degree of redundancy in a CDS. This study proposes a k−coverage CDS, where the

k−coverage property takes care of fault-tolerance and robustness of dominatees,

which ensure that every dominatee has atleast k adjacent dominator neighbors.

6.1.1 CDS for Topology Control in WSN

Wightman et al. [167] proposed four topology contruction and maintenance

algorithms based on the received signal strength, named A3, A3Lite, A3Cov, and

A3CovLite. Rizvi et al.[129] proposed a CDS based topology control algorithm

named A1 which constructs an energy-efficient virtual backbone. The topology

construction phases of A1, uses fewer messages and it achieves good connectivity

under topology maintenance for better sensing coverage. J.A.Torkestani [156]

proposed a degree-constrained minimum-weight CDS (DCDS) construction

algorithm to improve the network coverage and lifetime. DCDS seeks for a set

of the most energetic connected sensor nodes whose maximum degree is bounded

by d (degree). Mahjoub et al. [100] constructed disjoint connected backbones using

graph coloring and localized rules for connected coverage. Rong-rong et al. [130]

developed a fault-tolerant topology control algorithm based on higher fault-tolerant

145

degree. Each backbone node has backup nodes for promoting the energy efficiency

and fault-tolerant capability of the network.

6.2 k−Coverage Connected Dominating Set (k−CCDS)

As the applications of WSN are becoming more complicated, a CDS

can preserve only 1-coverage. Meanwhile, fault tolerance and robustness of

dominatees should also be considered. The k− coverage condition ensures that

every dominatee has k− dominator neighbors in the CDS. Therefore, a dominatee

node can be connected still with the CDS even if its k−1 dominator neighbors are

dead. This k−Coverage CDS provides multi-path redundancy for load balancing

and transmission error tolerance. A CDS constructed with these requirements is

called k−CCDS.


A homogeneous WSN is assumed with N sensor nodes deployed randomly

to cover the monitoring area A. Each sensor node has an initial energy,

communication radius Rc and sensing radius Rs with Rs ≤ Rc. All the sensor nodes

have the same Rs and Rc. Each sensor node is capable of monitoring the events

within Rs distance and it communicates with other sensor nodes within Rc distance.

Once the sensor nodes are deployed, dominators are selected and the sensed data is

communicated to the Base Station(BS) through the dominating nodes.

Definition 6.1. Coverage and k−Coverage: Given a set of sensor nodes,

S={s1,s2, ...sn}, in a 2D area A, each sensor node si,(i = 1,2, ...n), is located at

coordinate (xi,yi) inside A. Any point x = (xi,yi) in A is said to be covered by si if

x ∈ A(si), and x is said be k−covered if x ∈ A(s j),( j = 1,2, ...k).

146

Definition 6.2. k−Coverage Connected Dominating Set (k−CCDS): A graph

G(V,E) is said to be connected if each pair of vertices is connected by a path.

A set D ⊆ V is said to be k−Coverage set if every vertex in V\D is adjacent to

atleast k vertices in D. A set C ⊆ V is a k−Coverage Connected Dominating Set,

if the induced sub graph G(C,E ′) is connected. The set C is also a k−Coverage set

of G.

6.2.2 Weighted Coverage Cost Calculation

Each sensor node broadcasts an update packet with information about its

remaining energy to all its neighbors. In order to reduce packet collisions, the

nodes use random back-offs before sending the update packets. Upon receiving the

update packets from all its neighbors, a node calculates its weighted coverage cost

as follows. Let Etot(si) denotes the total energy of si from its sensing neighbors.

Esitot = ∑

∀x∈CR(si)

Exr , if Ex

r>Eth (6.1)

The Weighted Coverage Cost (WCC) of a sensor node si is

WCC(si) =

0, if Esir <Eth

Esir +Esi

tot , Otherwise

When WCC is calculated, sensor node with low remaining energy is

considered as overlapping sensor. This will cause the loss of coverage and it can

be avoided by preventing those sensor nodes that have remaining energy below a

certain threshold Eth (10% of Esiinit) from taking part in WCC calculation process.

Sensor node si compares the residual energy with the threshold energy. The

147

coverage weight is set to 0, when the remaining energy of sensor node si falls below

the threshold energy Eth. Otherwise, the weight is set to sum of the remaining

energy of sensor node si and Etot(si). This WCC is communicated to its neighbors

through an update message.

Fig. 6.1 shows the coverage redundancy of a sample network with five

sensor nodes. An initial energy of 5J is assumed on each sensor node and the

remaining energy is given in parenthesis. Sensor node A has three overlapping

sensor nodes namely B, C and D. As the remaining energy of A is greater than Eth ,

the WCC of A is sum of the remaining energy of the overlapping sensor nodes and

its own remaining energy. Thus sensor node A has WCC 10. The WCC of all the

nodes is illustrated in Fig. 6.2.

ARs

Rc

A(4) B(3)

C(2)

D(1)

E(2)

Fig. 6.1: Sensor’s Radius and Coverage Redundancy

148

WCCWCC(A) = 10

WCC(B) = 7

WCC(C) = 6

WCC(D) = 5

WCC(E) = 2

Fig. 6.2: Weighted Coverage Cost

6.2.3 Distributed k−CCDS Construction Algorithm

In densely deployed WSN, it is more practical to apply distributed

algorithms, by simultaneously executing the algorithm on all nodes. This

approach can conserve energy and a k−CCDS is formed faster compared to

centralized algorithms. This motivated to design a distributed algorithm for

constructing k−CCDS. The proposed algorithm begins with Maximal Independent

Set (MIS) construction, followed by connection phase. Any vertex outside of

the maximal independent set must be adjacent to some node in the set. The

MIS construction phase of k−CCDS algorithm produces a set which satisfies the

coverage requirement. The MIS is a dominating set of a graph G, but it is not

connected. The connection phase of the algorithm chooses the required number of

connectors (dominators) to make the set connected.

6.2.3.1 CDS Construction

A MIS is constructed first and minimum number of connectors are added to

make MIS, a CDS. Initially all nodes are in Non-MIS member (NMIS) state. After

completion of MIS phase, each node is in one of two states: MIS, or GATEWAY

(may become connector).

149

Algorithm 6.1: DISTRIBUTED MIS CONSTRUCTION

1. if State(s) == MIS ‖ GAT EWAY then2. drop(Message)

3. if State(s) == NMIS then4. switch on Message do5. case CNM(1):

6. State(s)←− GAT EWAY

7. broadcast(CNM(0))

8. case UPDATE:

9. Reply(State(s), WCC(s))

10. case CNM(0):

11. broadcast(UPDATE)

12. set a timeout

13. if timer expires then14. if WCC(s) is highest among its neighbors then15. State(s)←−MIS


17. else18. if received CNM(1) Message then19. State(s)←− GAT EWAY


21.

150

There are two types of messages in MIS construction phase: Color

Notification Message (CNM) and UPDATE. The CNM message indicates the state

of a node. CNM(1) is sent out when a node becomes a MIS node and CNM(0) is

sent out when a node becomes a GATEWAY. The UPDATE message is used when

a node inquires the weight and state of its neighbors. CNM(2) and CNM(3) are

used by DOMINATOR and DOMINATEE nodes respectively.

First, the sink node changes its state to MIS and it broadcasts the CNM(1)

message. The MIS construction procedure is explained in Algorithm 6.1. A node

in NMIS state changes its state to GATEWAY, when a CNM(1) message is received

(lines 3 to 7). This node may become connector to form connected dominating set.

When a CNM(0) message is received by the node in NMIS state, it changes to MIS

if it has the highest coverage weight among its one-hop neighbors (lines 10 to 20).

Algorithm 6.2, explains the selection of connectors to form CDS. Each

GATEWAY node maintains a list of its MIS neighbors and it begins the connection

phase when all its neighbors become MIS or GATEWAY. A MIS node starts the

connection phase by entering the MIS-Transition, when all its neighbors are in

GATEWAY state.

During the connection phase, a node broadcasts a DOMINATOR message

when it becomes the member of CDS (lines 4 to 14). Assume that all messages are

delivered in order. Nodes in MIS-Transition state select the connectors. A node in

this state, sets up a timeout. When it receives a DOMINATOR message during the

timeout, it stops the timeout because one of its neighbor becomes CONNECTOR.

Otherwise, the node will choose the neighbor with the best weight and sends the

CONNECT message.

151

Algorithm 6.2: DISTRIBUTED CDS CONSTRUCTION

1. switch on State(si) do

2. case DOMINATOR:

3. drop(message)

4. case MIS-Transition

5. set a timeout

6. if timeout expires then

7. Let x ∈ Nsi1 has the highest WCC

8. send CONNECT message to x

9. State(s)←− DOMINATOR


11. else

12. if received CNM(2) message then

13. State(s)←− DOMINTOR


15. case NMIS-Transition

16. broadcast(UPDATE)

17. if Received CONNECT Message then

18. if ∃x ∈ Nsi1 |State(x) == DOMINATOR then

19. State(s)←− DOMINATOR


21. if Received CNM(2) Message then

22. State(s)←− DOMINAT EE


24.

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A GATEWAY node, after receiving GATEWAY and MIS messages from

all its neighbors, begins the connection phase by entering into NMIS-Transition

(lines 15 to 22). This node may receive three types of messages: DOMINATOR,

CONNECT and DOMINATEE. It ignores the DOMINATEE message. As

mentioned in Kim et al. [81], a GATEWAY is also allowed to receive the

CONNECT message and to change the state to DOMINATOR.

6.2.3.2 k−CCDS Construction

A k−CCDS algorithm consists of two phases:

1. Phase I:1-Coverage CDS Construction. Construct a CDS C of G by using

Algorithm 6.1 and 6.2. All nodes in C are DOMINATOR.

2. Phase II: k−Coverage CDS Construction. Construct k - 1 disjoint MISs

{M2, ...,Mk} of G\C using Algorithm 6.1. These k - 1 disjoint MISs are

added to the CDS C, to form k−CCDS.



To evaluate the performance of k-CCDS, simulations are conducted using

Atarraya Simulator [168]. It is designed specifically for the evaluation of topology

control protocols in WSN. All the simulations assumed that the nodes are deployed

randomly on a 2D plane. The nodes can communicate with each other using full

duplex wireless radio that conform to 802.15.4 wireless standard. The simulation

153

setup is presented in Table 6.1. The reported results are averaged over 100

simulation runs with 95% of confidence interval.

Table 6.1: k−CCDS Simulation Parameters

Parameter Value

Deployment Area 200m x 200m

Node Distribution Random

Number of Sensors 100 to 400 (default:200)

Communication Range 40m to 70m (default:40m)

Initial Energy 1J

Transmitter/Receiver Circuit( Eelec ) 50 x 10−9 J/bit

Transmit Amplifier( εamp) 100 x 10−12 J/bit/m2

No.of trails 100

Data packet size 100 bytes

Control packet size 25 bytes

The following performance metrics have been used to validate the performance of

the k−CCDS protocol.

- Average CDS Size: This metric is defined as the average number of nodes

that are activated to cover the monitoring area (number of dominators in the

CDS).

- Lifetime of the CDS: It is defined as the average period of time during which

the set of active sensors remain connected.

- Number of Uncovered Nodes: It refers to the number of nodes, which are not

covered by at least one active node at the end of the simulation.

154

- Residual Energy: It is the average remaining energy of the sensor nodes at

the end of simulation round.

- Coverage Area: It is the percentage of covered area against the total

deployment area.

- Convergence Time: It measures the time taken for the execution of a protocol

to construct a CDS.


Wightman et al. [167] have proposed four topology construction and

maintenance algorithms named A3, A3Lite, A3Cov, and A3CovLite. In A3,

authors assume that the sensor nodes have no information about the position and

the distance is estimated based on the received signal strength. The residual energy

of the child node and its distance from the parent are two metrics that A3 uses

to construct CDS. A3 uses four messages for topology construction. They have

proposed the A3Lite with two messages. A3Cov and A3CovLite are combination

of A3 and coverage problem. A3Cov first checks whether an unconnected node

is sensing-covered by another active node. If so, the node is sent directly to the

sleeping mode. A3CovLite is a combination of A3Lite and A3Cov.


The coverage and connectivity requirements need minimum number of

dominating nodes. Fig. 6.3. shows the size of CDS against the total number of

network nodes. The A3Cov algorithm uses a selection metric giving priority to the

farther nodes from the parent having higher energy level. It also uses extra nodes

155

to increase the coverage area. In k−CCDS, the number of dominating nodes is

controlled by k parameter. Larger value of k increases the number of active nodes

and smaller value of k leads to smaller CDS.

0

10

20

30

40

50

60

70

80

90

100

50 100 150 200 250 300 350 400 450

Avera

ge C

DS

Siz

e

No.of Nodes

A3Cov1-CCDS2-CCDS

Fig. 6.3: No. of Nodes vs Average CDS Size

The results imply that the CDS size gets bigger when the network density

increases. This is because when the network density increases, the number of

neighbors of each node increases as well. Thus the CDS size needs to be larger

to dominate all nodes in a network. As revealed by Fig. 6.3., k−CCDS still

outperforms A3Cov. Specifically, the CDS size obtained from k−CCDS is less

than that of A3Cov.

Simulations were also conducted to compare the performance of the

algorithms when changing the communication or sensing range ( Rc = 2.Rs) as

well as to see how it affects the size of the resulting CDS. In this simulation, 100

nodes were randomly deployed into a fixed area of size 200m x 200m. Fig. 6.4.,

156

0

5

10

15

20

25

30

35

40

45

50

35 40 45 50 55 60 65 70 75

Avera

ge C

DS

Siz

e

Communication Range (m)

A3Cov2-CCDS

Fig. 6.4: Communication Range vs Average CDS size

illustrates that the CDS size decreases as the range increases. It is due to the fact

that the larger the range is, more nodes can communicate.

The performances of the algorithms are evaluated in terms of CDS lifetime.

N nodes were randomly distributed in a 200m x 200m region. The communication

range was set to 40m. The number of nodes varied from 100 to 400. Fig. 6.5.,

reveals that CDS life time in k−CCDS is much longer than A3Cov and the lifetime

reduces as the network size increases in both algorithms. As CDS size grows

with increasing network size, more nodes are active and energy depletion occurs

frequently. The proposed algorithm k−CCDS provides the longer lifetime because

k−CCDS uses the coverage cost to activate the nodes with the maximum coverage

weight. A3Cov adds extra nodes to increase the coverage area without selection

metric. Thus, the CDS lifetime in A3Cov is less than k−CCDS.

The number of uncovered nodes at the end of simulation time 18000s with

157

100

200

300

400

500

600

700

800

900

1000

1100

50 100 150 200 250 300 350 400 450

Life

tim

e o

f C

DS

(sec)

No.of Nodes

A3Cov2-CCDS

Fig. 6.5: No. of Nodes vs CDS Lifetime

0

20

40

60

80

100

120

50 100 150 200 250 300 350 400 450

Ave

rage N

o. of U

ncove

red

Nod

es

Number of Nodes

A3Cov2-CCDS

Fig. 6.6: No. of Nodes vs Uncovered Nodes

158

increasing network is presented in Fig. 6.6. The results reveal that the number

of uncovered nodes increases as the network size gets bigger. In comparison,

k−CCDS results in less number of uncovered nodes due to the reason that it forms a

CDS with more connected nodes covering the area much better than A3Cov. Also,

k−CCDS never includes node with energy less than Eth. As additional nodes are

added without any selection metric in A3Cov to increase the coverage, the energy

depletion occurs more frequently.

0

10

20

30

40

50

60

70

80

90

100

50 100 150 200 250 300 350 400 450

Re

sid

ual E

nerg

y (

%)

No.of Nodes

A3Cov2-CCDS

Fig. 6.7: No. of Nodes vs Residual Energy

Fig. 6.7. presents the residual energy of the network at the end of

simulation for 7200s with increasing network size. The result shows that the

average residual energy of the network with k−CCDS is higher than the other

approaches. The reason is that the proposed method reduces the number of nodes

covering the same points of the area based on the energy threshold Eth. The

k−CCDS approach never activates a node with energy less than Eth. Thus, this

159

approach avoids the rapid exhaustion of the active sensors. The result also reveals

that A3Cov has lowest residual energy because nodes farther from the parent are

activated to minimize the number of dominating nodes. This causes a non-uniform

distribution of the overhead and places heavy load on the active nodes. Therefore,

nodes exhaust energy rapidly in A3Cov and reconstruction of backbone occurs

frequently when nodes use 90% of its energy.

0

10

20

30

40

50

60

70

80

90

100

50 100 150 200 250 300 350 400 450

Covere

d A

rea (

%)

No.of Nodes

A3Cov2-CCDS

Fig. 6.8: No. of Nodes vs Coverage Area

The k−CCDS protocol requires fewer dominating nodes than A3Cov,

hence less energy is used in the network at a given moment. Also, it selects as

dominators those with more coverage weight, so they will not die immediately

after being selected. The coverage ratio is presented with varying network size

in Fig. 6.8. It shows that, k−CCDS and A3Cov provide less coverage when the

network is sparse. As nodes with more sensing neighbors are added in k−CCDS,

it is able to provide more coverage than A3Cov.

160

100

150

200

250

300

350

400

450

500

0 50 100 150 200 250 300 350 400

Con

verg

en

ce T

ime (

se

c)

No.of Nodes

A3Cov2-CCDS

Fig. 6.9: No. of Nodes vs Convergence Time

Fig. 6.9 presents the convergence time with respect to the network density

for the two algorithms. A node in dense networks is likely to have more neighbors.

A3Cov takes longer time to form CDS than k−CCDS. This is due to more time for

processing the neighbor information and informing the details to all its neighbors.

It is clear that k−CCDS creates CDS faster than A3Cov as the outcome of MIS

construction is faster than tree construction.

Fig. 6.10 shows the convergence time of the two algorithms with increasing

communication range. The number of neighbors of a node increases when

communication range increases. As stated earlier A3Cov communicates its

neighbor details to all its neighbors. Due to the increased neighbors, the processing

time of neighbor details is increased for A3Cov. This leads to overall increase in

the execution time of A3Cov algorithm. In k−CCDS, a node decide its state with

WCC. Hence, less convergence time in k−CCDS.

161

100

110

120

130

140

150

160

170

180

190

200

35 40 45 50 55 60 65 70 75

Con

verg

en

ce T

ime (

se

c)

Communication Range (meters)

A3Cov2-CCDS

Fig. 6.10: Communication Range vs Convergence Time

6.4 CHAPTER SUMMARY

This chapter investigates the application of connected dominating set for

topology control in WSN. Topology control mechanisms build reduced topology

using CDS, to select number of active nodes. Those nodes which are not part of

CDS can go to sleep state. The constructed CDS can provide only 1-coverage (i.e.,

every sleep is covered by at least one active node). In this chapter, a k−Coverage

topology control problem for connected area coverage is addressed. In this

problem, the area of sleep node is covered by atleast k sensor nodes. A weight based

coverage cost metric named Weighted Coverage Cost (WCC) has been introduced.

A node calculates its WCC based on its energy and the energy of its sensing

neighbors. A distributed algorithm is developed for k−CCDS construction, where

the nodes are selected based on weight. The performance of the proposed work is

also compared with the recent coverage protocol A3Cov with varying network size

162

and communication range. As nodes with more WCC is added in k−CCDS, it can

provide a topology control with fewer nodes using fewer resources. It is observed

from the results that, the CDS size grows with increased network. The results

also demonstrate that the lifetime of CDS is longer than A3Cov which results in

more residual energy in the network and less number of uncovered nodes. It is

also observed that the convergence time of k−CCDS is lesser than A3Cov with

increasing neighbors and network density. As a future extension, a k−connected

k−coverage topology control can be developed for mobile sensor networks.

CHAPTER 7

DESIGN OF CONNECTED DOMINATING SET BASED

ENERGY EFFICIENT PRESSURE AWARE ROUTING

FOR UNDERWATER ACOUSTIC SENSOR

NETWORKS

7.1 INTRODUCTION

UnderWater Acoustic Sensor Networks (UWASN) consists of sensors

that are deployed to perform collaborative monitoring tasks over a given area

[4, 146, 179]. UWASN can be used in a wide spectrum of aquatic applications,

such as oceanographic data collection, pollution monitoring, offshore exploration,

disaster prevention and coastline surveillance [3]. UWASN shares many properties

with terrestrial sensor networks such as the large number of nodes and energy

issues, still these are different in many aspects from terrestrial sensor technology.

Communications in UWASN have to be done through acoustic channels, because

electromagnetic radio signals attenuate quickly in water. The speed of sound in

water is five-order slower than the speed of light, which brings long propagation

and end-to-end delay. The bandwidth of an acoustic channel is low and the error

rate is high. Most underwater sensor nodes, except some fixed nodes equipped on

surface level buoys have low or medium mobility (move up to 1-3 m/sec) owing to

water currents and other underwater activities [2, 63].

163

164

Routing is a challenging task in UWASN with energy constraint and sudden

topology changes due to some node failures. Several routing protocols have

been proposed for underwater sensor networks. A review of underwater routing

protocols is presented in [14, 53, 119, 120]. These protocols are classified into:

location based [110, 180, 188], pressure (depth) based [13, 111, 183]. The location

based protocols require full-dimensional location information. This is difficult

to get since localization in UWASN is another challenging research issue. The

pressure based protocols use depth information which can be obtained easily with

a depth sensor. In comparison, obtaining full-dimensional location information is

more difficult than depth. These routing protocols flood the packets to the sink

with geographic information, which leads to more energy consumption. Energy

efficiency becomes more critical and challenging in UWASN because of the much

higher transmission and receiving power consumption of acoustic channel. So, an

energy efficient routing algorithm is to be provided for underwater communication.

Ant Colony Optimization (ACO) [43, 44] is a population-based meta-

heuristic approach using the intelligent foraging behavior of ants, which has been

applied to NP-hard combinatorial optimization problems successfully like Subset

Selection, Traveling Salesman, Vertex Cover Problem, Minimum Spanning Tree

and Connected Dominating Set [64, 75, 76, 121, 122, 154, 195]. In ACO, artificial

ants walk the graph, to find a solution to the problem. The behavior of the ants

is inspired by that of real ants: they deposit pheromone on the path in a quantity

proportional to the quality of the solution represented by that path. The ants choose

probabilistically the paths with strong pheromone concentration. This indirect form

of communication, known as stigmergy, intensifies the search around the most

promising parts of the search space. On the other hand, there is also a degree

of pheromone evaporation, which allows to diversifying the search to new and

165

hopefully more successful areas of the search space.

This chapter proposes an energy efficient pressure (depth) aware routing

protocol named Connected Dominating Set based Energy-Efficient Pressure-Aware

Routing Protocol (CDS-EPRP), for UWASN. In CDS-EPRP, CDS concept is

adapted for maintaining connectivity among the sensor nodes and surface sink.

CDS has been used for many applications such as routing [46, 61, 62, 65, 83,

87, 127, 156] and topology control [15, 129] in WSN. A review of applications

of CDS can be found in [190]. Recently, a CDS based coverage protocol has

been proposed by Senel et al. [137], for guaranteed connectivity in UWASN. They

applied heuristics for CDS construction and the depths of dominating nodes are

adjusted to give minimal overlap among the sensors and to provide the full coverage

and connectivity. The proposed CDS-EPRP selects CDS based on node’s energy

using an ACO technique. These selected CDS form a backbone for routing. When

a sensor node wants to communicate an information to surface sink, it uses these

CDS nodes. The pressure of these CDS nodes are used during the forwarding of

packet.

7.1.1 Energy Efficient Routing in UWASN

Modified Energy Weight Routing (MEWR) proposed by Zhang et al. [196],

is a low flooding routing protocol for delay sensitive UWASN. This protocol

consists of two phases. In the first phase, the senders discover their neighbor nodes.

In the second phase, the senders select part of their neighbors as the intermediate

nodes and flood the packets to the intermediate nodes. They formulated a mixed

metric based on energy consumption and delay, to evaluate the cost of link. An

energy-efficient protocol based on estimating quality of link is proposed by Zhao

et al. [197], considering both node’s residual energy and link quality. This protocol

166

chose forwarding nodes by node’s residual energy, position and transmission

quality of link. QELAR proposed by Hu et al. [67] is an adaptive, energy-

efficient and lifetime-aware routing protocol based on reinforcement learning.

They suggested that the residual energy of sensor nodes and the energy distribution

among a group of nodes during the calculation of reward function will extend the

network lifetime.

Gopi et al. [55] proposed an Energy optimized Path Unaware Layered

Routing Protocol (E-PULRP) where sensor nodes report events to a stationary

sink node using on the fly routing. E-PULRP consists of two phases: layering

phase and communication phase. In the layering phase, nodes occupy different

layers in the form of concentric shells, around a sink node. The layer widths and

transmission energy of nodes in each layer are chosen taking into consideration the

probability of successful packet transmission and minimization of overall energy

expenditure in packet transmission. During the communication phase, intermediate

relay nodes are selected on the fly, for delivering packets from the source node to

sink node. Huang et al. [68] proposed a power-efficient routing protocol where

a forwarding node selector is employed to determine the appropriate sensors

to forward the packets to the destination. A fuzzy-logic based forwarding tree

trimming mechanism is adopted to prevent excess spread of forwarded packets. A

routing schemes developed by Zorzi et al. [200], considered both the propagation

delay and energy consumption to select the forwarding nodes in UWASN.

167

7.2 CDS BASED ENERGY-EFFICIENT PRESSURE-AWAREROUTING PROTOCOL (CDS-EPRP)

Because the replenishment of batteries in underwater environment might

be impossible, an energy-efficient routing scheme is needed for UWASN. When a

sensor wants to send a packet to the sink, a multi-hop path should be determined to

minimize the packet energy consumption during the packet relay. In this chapter,

an energy-efficient routing named CDS-based Energy-Efficient Pressure-Aware

Routing Protocol (CDS-EPRP) is proposed, which provides a minimum energy

routing path.

Fig. 7.1: CDS-EPRP for UWASN

The proposed protocol CDS-EPRP first marks subset of network nodes

called CDS as forwarders set, next a multi-hop path is established through these

CDS nodes. The proposed protocol also applies a connectivity maintenance

mechanism for the CDS, to adapt to changing network topology due to water

current. The working of CDS-EPRP is illustrated in Fig. 7.1.

168

Every node in UWASN has a flag to indicate its dominating status and

it is set to false initially. Every nodes communicate its depth value to their

neighbors. Each node u calculates the feasible neighborhood Nuf by comparing

depth difference with depth threshold dth. The CDS construction phase of CDS-

EPRP selects 10 nodes randomly, each node sends an ant packet and these ants

perform a random walk over the network. As it walks, selects dominating node

from the feasible neighborhood. In the proposed approach, pheromone is updated

once at the end of each iteration, when all ants have constructed their dominating

sets. The ant that constructed the best solution of the iteration updates the

pheromone trails on the nodes. The dominating flag of those nodes is set to true.

The pheromone is decreased for nodes which are not part of CDS. All dominating

nodes will inform the status to their neighbors. A non-dominating node is called

as dominatee node. An ACO based CDS formation is explained in section 7.2.2.

Section 7.2.3.1 explains the data packet forwarding through multi-hop path. The

connectivity maintenance of CDS is described in section 7.2.3.2.


In UWASN, the sensor nodes are deployed at different depth levels. The

deployment of UWASN can be either 2-D or 3-D. In 3-D UWASN, the surface

sinks are equipped with radio and acoustic modems, where radio frequency (RF)

modems will be used to communicate with each other and to communicate with

the final on-shore station, while acoustic modems are used to communicate with

the sensor nodes. In horizontal directions, these sensor nodes can move freely

with water currents (1 - 3m/s) but vertically a node may have small variations,

which are negligible [110]. A homogeneous UWASN is assumed with N sensor

nodes deployed randomly to cover the monitoring area. Each underwater sensor

169

node u has an initial energy Euinit , communication radius Rc and sensing radius

Rs with Rs ≤ Rc. All the sensor nodes have the same Rs and Rc. Each sensor

node is capable of monitoring the events within Rs distance and it communicates

with other sensor nodes within Rc distance. The CDS-EPRP protocol is based on

multi-sink architecture, which not only helpful for increasing the delivery ratios but

also increases the network life by decreasing the energy consumption of the sensor

nodes around the sink. Furthermore, CDS-EPRP assumes that each underwater

sensor node knows its depth information, which is the vertical distance from itself

to the water surface. In practice, this information can be easily obtained with an

inexpensive depth sensor that can be equipped with each sensor node.

A 3-D underwater acoustic sensor network can be represented as a graph

G(V,E), where V = {v1,v2, ...vN} is a finite set of sensor nodes deployed in 3-D

space, with N = |V |, and E is the set of communication links among the nodes.

A communication link exists between vi and v j (i.e., (vi,v j) ∈ E ) if nodes vi

and v j are within the each other’s communication range (Rc). Let S be the set

of traffic sources. This set represents the sensor nodes that sense information in the

underwater environment and communicate the information to the surface sink.

Definition 7.1. Maximum Energy Minimum CDS (MEMCDS) : Given an

undirected graph G = (V,E) with node energy weight function W : V → R+,

MEMCDS problem is to find a minimum size CDS among the CDSs with

maximum total energy weight.

7.2.2 ACO based CDS Construction

In the proposed algorithm, each node has an initial pheromone value of τ0.

This pheromone is evaporated with time based on the pheromone persistence rate,

170

0 ≤ ρ ≤ 1. If the pheromone value falls below a pheromone-threshold τmin, the

value is set is τmin. The state transition rule for an ant is based on the pheromone

value and a heuristic component which is based on energy of uncovered nodes. In

order to construct CDS by an ant, at each step, an ant k selects the next node. The

probability pkj of selecting j by an ant k at node i is as follows.

pkj =

[τ j]α [η j]

β

∑

j∈Nif

[τ j]α [η j]β(7.1)

where pkj is the probability of selecting the next hop node j by an ant k. τ j denotes

the pheromone value of node j. η j is the energy weight of node j. It is calculated

as follows

η j =E j

rm

E jinit

(7.2)

α and β are the parameters used to control the relative weight of pheromone trail

and heuristic functions respectively. Nif is the feasible neighborhood of node i.

Nif is the set of neighbors of i whose depth difference is greater than the depth

threshold dth. It also excludes nodes already visited in the partial tour of ant k and

it may be further restricted to a candidate set of next hop neighbor of a node i.

The pheromone update rule plays an important role and it is used to increase

the pheromone values on the solution components. In the proposed approach,

pheromone is updated once at the end of each iteration, when all ants have

constructed their dominating sets. The ant that constructed the best solution of

the iteration updates the pheromone trails on the nodes in the following way.

τi = ρτi +∆τibi (7.3)

where ρ is the persistence rate and ∆τ ibi is the amount of reinforcement on the node

171

i due to iteration best solution Sib. |Sib| is the number of nodes in the iteration best

solution. ∆τ ibi is computed using the following expression

∆τibi =

0, if i /∈ Sib

1|Sib| , if i ∈ Sib

(7.4)

As the pheromone evaporation takes place on all nodes in the network at a fixed

rate, the dominating nodes of iteration best solution Sib receive reinforcement. If

the pheromone value of any node reduces below a minimum pheromone value, τmin,

the pheromone value is set to τmin to ensure that this node has a small probability

of getting selected. There is no maximum value on the pheromone.

The pseudo-code for CDS construction is explained in Algorithm 7.1. The main

idea of the algorithm is as follows: At first, pheromone, solution space and

dominating set are initialized (lines 1 to 3). Then, an ant constructs the CDS

according to equation 7.1 (lines 4 to 10). Next, the best solution of the iteration

is selected (lines 11 to 13). Finally, the pheromone update according to equation

7.3 is done (line 15).

7.2.3 Routing in CDS-EPRP

7.2.3.1 Data Packet forwarding

Once the CDS of the network is computed, data forwarding can be done

through these nodes. CDS-EPRP is an on-demand routing protocol. Neither path

maintenance nor recovery is required in CDS-EPRP. In order to save energy, the

data forwarding will use multi-hop relay. In the multi-hop relay, the packets are

routed from source sensor node to the surface sink through the dominating nodes.

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Algorithm 7.1: ACO for Connected Dominating Set Construction

1. Initialize pheromone value (τ0) on each node

2. f =|V |3. D = φ

4. foreach i← 1 to Na do5. S = φ

6. while S 6= CDS do7. Calculate pk

j as in equation (7.1)

8. j = argmax{pkj}

9. S = S∪ j

10. coverNeighbors(G, j)

11. if | S | < f then12. f = | S |13. D = S

14. Update the pheromone for D using equation (7.3)

Each node maintains a list of its dominating nodes. Every node knows the depth of

their neighbors. Data packet forwarding is explained in Algorithm 7.2. Because the

sinks are located above the water, the packet should be routed to a dominating node

with a smaller depth. A node calculates the depth difference with its dominating

neighbors whose depth is smaller than the node’s depth (lines 1 to 3). Then, it

chooses a dominating node with minimum difference as its next-hop (lines 4 to 6).

In this way, the packet is routed until it reaches one of the surface sinks.

7.2.3.2 Connectivity Maintenance of CDS

As the dominating nodes are playing a key role in data forwarding, the

connectivity among the dominating nodes should be maintained. The connectivity

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Algorithm 7.2: Next-Hop Determination for Data PacketsInput: Node ′i′ receives a data packet ′p′

Output: Sending of p to next-Hop

1. foreach j ∈ NiD do

2. if d j < di then3. Calculate d p(i, j)

4. Choose j = argmin j∈NiD

d p(i, j)

5. next−Hop = j

6. send(p,next−Hop)

Algorithm 7.3: Connectivity Maintenance of CDS

1. NuD← neighborDominatorsOf(u)

2. NuE ← neighborDominateesOf(u)

3. Nu1 ← Nu

D∪NuE

4. if (isDominating(u) == true) and (Eurm < Eth) then

5. Communicate the Nu1 list to it neighbors

6. foreach i, j ∈ NuD do

7. if (NiD == φ ) and ( (N j

D == φ ) then8. x = argmaxx∈(Ni

1∩N j1)∧[(d p(x,i)>dth)∨(d p(x,i)>dth)]

Exrm

9. dominating(x)← true

10. if (RSSud > Rs) then

11. if (NuD == φ) and (Nu

1 6= φ ) then12. x = argmaxx∈Nu

1∧(d p(x,u)>dth)Exrm

13. dominating(x)← true

14. if (isDominating(u) == true then15. dominating(x)← f alse

174

maintenance algorithm should preserve the CDS structure as much as possible

even when the nodes are moving around and the topology is changing slowly.

In UWASN the mobility is very little or a few nodes fail due to low power, the

computation of the CDS for whole network will waste energy and it is better

to use an incremental approach. In order to maintain connectivity, dominatee-

dominator and dominator-dominator edges have to be preserved. The connectivity

maintenance is given in Algorithm 7.3. When the remaining energy of dominating

node is below Eth (20% of Euinit), it sends neighbor list to all its neighbors, so that

they can get attached with the next best dominating nodes (lines 4 to 9). Each node

upon receiving beacon packets, measures the distance ( RSSd ) based on received

signal strength as in [66]. When the distance is greater than sensing radius (Rs),

sensor nodes make a decision. A sensor node may change it’s dominating status to

false or selects a neighbor with more energy based on the depth threshold (lines 10

to 15).



In this section, the simulation results of proposed routing protocol CDS-

EPRP are presented with an underwater sensor network simulation package Aqua-

sim [182], which is extended on the network simulator-NS2. In the simulations,

sensor nodes are deployed randomly in a 800m x 800m x 500m 3-D area. The

transmission range was set to 100m and 8 surface sinks were deployed at a distance

of 100m. The mobility due to water current was set to 1-3m/s and vertical

movements are not considered during the simulation. Any node in the network can

generate data packet and it is selected randomly. For ACO, the parameters were set

175

to: Na = 10, α = 1 , β = 1, ρ = 0.97, and τmin = 0.005. The initial pheromone was

set to τ0 = 10. The width was set to 100m for VBF. The simulation parameters are

listed in Table 7.1. The reported results are averaged over 100 simulation runs with

95% of confidence interval.

Table 7.1: CDS-EPRP Simulation Parameters

Parameters Value

Area Size 800m x 800m x 500m


Traffic Type Constant Bit Rate(CBR)

Number of Traffic Sources 1 to 10 ( default:5)



Number of Nodes 200 to 500 (default:400)

Mobility Model Random-walk Mobility

Maximum Speed 3m/s (default:1m/s)

Transmission Power 2 W

Reception Power 0.1 W

Idle Power 10mW

The following metrics have been used to evaluate the performance of

routing protocols.

- Packet Delivery Ratio (PDR): It is the ratio of the number of data packets

received successfully at the sinks to the total number of packets generated by

the source nodes.

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- Average End-to-end Delay: It represents the average time for a data packet to

travel from the source node to any one of the surface sinks.

- Total Energy Consumption: It is the total energy consumed for packet

delivery, including transmitting, receiving, and idling energy consumption

of all nodes in the network during the simulation.


Vector-based-forwarding (VBF) proposed by Xie et al. [180], is a

geographic routing approach where each packet carries the position information

of source, sink and intermediate forwarders. Sensor nodes forward the packets by

broadcasting them to nodes residing in a constrained ’pipe’ of given width in the

direction of the sink. On receiving a packet, each node calculates its own position

based on the location of the predecessor node embedded in the packet and the

angle of arrival of signal. Only the sensor nodes that fall in a routing pipe, centered

around the source-sink vector are eligible for forwarding. The efficiency of the

protocol depends on the critical determination of the radius of the pipe: If the

radius is too small, few or no relays can be found in the pipe; if it is too large, too

many nodes might receive the packet, whose retransmission increases interference,

overhead, and duplicate packets.

Depth-based-routing (DBR) proposed by Yan et al. [183], is a geographic

routing protocol, where each nodes knows the depth to the surface sink using

pressure sensors. On receiving a data packet, each node forwards it only if its

depth is less than that of the sender. Before forwarding the data packet, each node

calculates holding time for a packet that depends on the difference between its own

depth and that of the sender. In particular, the larger the depth, the smaller the

177

holding time, so that nodes that are closer to the surface sink are the first to forward

the data packet. While holding, a node if it overhears that the packet that it is about

to broadcast is transmitted by another node, then it drops the packet.


The parameter dth determines the feasible neighborhood of nodes. The

dominating nodes were selected based on energy weight from the feasible

neighborhood. The number of dominating nodes (size) selected by ACO based

CDS is plotted in Fig. 7.2. The communication radius (Rc)and sensing radius (Rs)

were set to 100m and 70m respectively. So, the dth is set to a value larger than Rs.

The network size was varied from 70m to 100m. The results show that the larger

dth, the smaller the size. This is due to increasing feasible neighborhood with larger

dth and the increasing number of dominatees of a dominating node. Thus reduction

in the number of dominating nodes.

As CDS-EPRP is based on multi-sink architecture, the performance of

CDS-EPRP was compared by varying the number of sinks. The packet delivery

ratio CDS-EPRP is plotted in Fig. 7.3. CDS-EPRP involves the dominating nodes

for forwarding the data packets and the packet delivery ratio increases with the

increasing number of sinks. The packets will be dropped due to congestion with

only one sink. The packet delivery ratio is high with multi-sink, as packets can

follow different paths to reach any one of the sinks. When the network is sparse

( number of sensors less than 250) CDS-EPRP delivers less packets than denser

networks, because the CDS maintenance is required frequently for nodes in sparse

network.

178

50

100

150

200

250

300

150 200 250 300 350 400 450 500 550

No

. of D

om

inatin

g N

ode

s

Number of Nodes

dth=100dth=90dth=80dth=70

Fig. 7.2: No. of Nodes vs No. of Dominating Nodes with dth

0

10

20

30

40

50

60

70

80

90

100

150 200 250 300 350 400 450 500 550

Packet D

eliv

ery

Ratio (

%)

Number of Nodes

No. of Sink=1No. of Sink=4No. of Sink=8

Fig. 7.3: No. of Nodes vs Packet Delivery Ratio with varying Sinks

179

Fig. 7.4 shows the end-to-end delivery of CDS-EPRP for multi-sink

architecture with varying network size. When the network is sparse, nodes check

the status of dominating neighbors by measuring the distance (RSSd). The CDS

connectivity mechanism will be executed by nodes when the distance is greater

than the sensing radius. Thus packets experience more delay with single sink. If

multiple sinks are available, they can reach any one of the sink, which results in

lower end-to-end delay. As the number of neighbors of a node increases with dense

network, the number of nodes involved for data forwarding in CDS-EPRP is less

which results in reduced delay with the increasing network size.

0

0.5

1

1.5

2

150 200 250 300 350 400 450 500 550

End-t

o-E

nd D

ela

y (

s)

Number of Nodes

No. of Sink=1No. of Sink=4No. of Sink=8

Fig. 7.4: No. of Nodes vs End-to-End Delay with varying Sinks

The performance of CDS-EPRP was also compared with VBF and DBR.

Both VBF and DBR are flooding routing protocols and use holding time for each

node to reduce flooding. The holding time of VBF is determined by the adaptive

factor and DBR uses a global parameter δ .

180

0

10

20

30

40

50

60

70

80

90

100

150 200 250 300 350 400 450 500 550

Packe

t D

eliv

ery

Ratio(%

)

Number of Nodes

VBFDBR

CDS-EPRP


The packet delivery ratio of the protocols with varying network size is

plotted in Fig. 7.5. The results show that the delivery ratio is increasing with

dense network for all the protocols. In VBF and DBR, the number of neighbors

is increasing with dense network and the nodes in the communication radius can

hear the broadcast packet which results in more packet delivery by spending more

energy. In CDS-EPRP, each dominatee is associated with at least one dominating

node and these dominating nodes are always connected. As each dominating node

selects a neighbor dominator with lower depth, a single copy of data packet is

forwarded. Thus it achieves high packet delivery ratio due to less congestion and

CDS connectivity maintenance.

Fig. 7.6 shows the end-to-end delay for all protocols. Here, DBR shows the

worst performance due to congestion in the acoustic channel and failure of packet

retransmissions. VBF finds the shortest path from the source node to the sink along

181

the virtual pipe between them. But, DBR uses the depth information and it is used

for holding time calculation. Thus the delay in VBF is shorter than DBR. As CDS-

EPRP forward a single copy of data packet, it has less end-to-end delay.

0

0.5

1

1.5

2

2.5

3

150 200 250 300 350 400 450 500 550

End-t

o-E

nd

Dela

y(s

)

Number of Nodes

VBFDBR

CDS-EPRP


Fig. 7.7 shows the energy consumption of the protocols. The results show

that CDS-EPRP has the least energy consumption compared with DDR and VBF.

The VBF consumes more energy due to flooding of packets through the virtual

pipe between the source and destination. The energy consumption of DBR is

less compared to VBF due to the redundant packet suppression technique with

two-queue mechanism adapted by DBR. As energy consumption is related to

distance, to minimize energy consumption CDS-EPRP selects the next hop node

with minimum depth among the dominating neighbors. Also, a single copy of the

packet is forwarded to the network, resulting in reduced congestion. Thus CDS-

EPRP consumes less energy than the others.

182

0

1000

2000

3000

4000

5000

6000

7000

8000

150 200 250 300 350 400 450 500 550

To

tal E

nerg

y C

on

sum

ption

(W

)

Number of Nodes

VBFDBR

CDS-EPRP

Fig. 7.7: No. of Nodes vs Energy consumption

In order to check the performance of CDS-EPRP with different offered

load, the delivery ratios and end-to-end delays were analyzed, with the increasing

data packets by different sensor nodes. Assumed that a network was deployed with

400 nodes, varied the load from one to 10 and each source sensor node generates

a data packet for every 10 seconds. The results in Fig. 7.8 show that the packet

delivery ratio decreases with the increasing load. The network is congested with

more data packets when the offered load is high. As DBR and VBF flood the

packets which result in more packet loss. The multi-sink architecture and redundant

packet suppression technique of DBR increase the packet delivery. Thus the packet

delivery ratio of DBR is higher than VBF. As CDS-EPRP is based on multi-sink

and a single copy forwarding of data packets through subset of nodes, more packets

are delivered compared to DBR and VBF.

Fig. 7.9 presents the results of end-to-end delay of the protocols with the

183

0

10

20

30

40

50

60

70

80

90

100

0 1 2 3 4 5 6 7 8 9 10 11

Packe

t D

eliv

ery

Ratio(%

)

Offered Load (traffic sources)

VBFDBR

CDS-EPRP

Fig. 7.8: Offered Load vs Packet Delivery Ratio

0

0.5

1

1.5

2

2.5

3

0 1 2 3 4 5 6 7 8 9 10 11

End-t

o-E

nd D

ela

y (

s)


VBFDBR

CDS-EPRP

Fig. 7.9: Offered Load vs End-to-end Delay

184

increasing load. Due to broadcasting, both DBR and VBF transmit the multiple

copies of the same data packet. In such cases, when nodes receive more data

packets, then for every receiving node, the depth or AoA is checked in DBR and

VBF respectively. Thus the delay is decreasing with the increasing load. But,

CDS-EPRP does not follow flooding and packets will be delivered to any of the

sink, which results the lowest delay compared to the others. As dominating nodes

are heavily loaded when it receives more data packets, resulting in increasing end-

to-end delay.

0

1000

2000

3000

4000

5000

6000

0 1 2 3 4 5 6 7 8 9 10 11

Tota

l E

nerg

y C

on

sum

ed (

W)


VBFDBR

CDS-EPRP

Fig. 7.10: Offered Load vs Energy Consumption

The energy consumption of the protocols with offered load is presented

in Fig. 7.10. At high offered load, the network is more congested, consuming

more energy. Thus the energy consumption is increasing with the increasing load.

With CDS connectivity maintenance and forwarding of a singly copy, CDS-EPRP

achieves the lowest energy consumption.

185

7.4 CHAPTER SUMMARY

This chapter discusses the design of an on-demand routing protocol for

energy-efficient routing in UWASN based on connected dominating set, named

CDS-EPRP. CDS-EPR does not require global location information and neither

route discovery nor maintenance is needed. The dominating nodes are selected

based on node’s energy using ACO. The data forwarding in CDS-EPRP is multi-

hop relay where the next hop is selected based on depth and energy. It takes

advantage of multi-sink architecture, where data packets can reach any one of the

sinks. The connectivity of dominating nodes is also maintained by energy threshold

and distance. The performance of CDS-EPRP was compared with VBF and DBR.

As the dominating nodes are responsible for data forwarding, the network is less

congested with varying network size when compared to VBF and DBR. Thus it can

achieve the highest packet delivery ratio and the lowest energy consumption and

end-to-end delay. At higher offered load, with multi-sink architecture, dominating

nodes are delivering the packets to any of the sinks, resulting in highest packet

delivery ratio, lowest end-to-end delay and energy consumption. As a future work,

coverage of the area can also be maintained by the dominating nodes through the

self deployment strategies.

CHAPTER 8

CONCLUSION AND FUTURE WORK

8.1 SUMMARY OF CONTRIBUTIONS

This thesis focused on defining five new concepts based on CDS and

designing algorithms for the new concepts. With new applications (MANETs,

MON, WSN and UWASN), the concept of CDS is modified or extended and still

serves as network backbone for communication.

The first study of this thesis presents S-ELHT, a stability based energy-

efficient link-state hybrid routing protocol to meet the stable path requirement

of mobile ad hoc networks. As link-state routing propagates the link state

information into the network using CDS (also called MPR), the number of message

transmission depends on the link disconnections. In addition to reducing the

number of forwarding nodes with node degrees, the stability of nodes is measured

based on energy and link connectivity time. These measures provide an indication

of how much a link is stable while deciding about dominating nodes for propagating

link state informations. Unlike link-state routing, S-ELHR do not maintain routing

tables. From a given source to destination, a source route is computed based on

the local topology information base. The data packets carry a complete routing

path. S-ELHR also performs a route recovery when the next hop in the source

route is not a one-hop neighbor. The proposed algorithm, S-ELHR, has been

compared with OLSR and EE-OLSR in terms of delay, packet delivery ratio, energy

consumption with various network size and node mobility. It is also observed

186

187

that the proposed algorithm provides good packet delivery ratio, shorter delay and

energy consumption compared to OLSR and EE-OLSR in all cases.

The second study of this thesis presents Weighted-CDSR, a reactive routing

protocol for ad hoc communications to meet the requirements of emergency and

rescue scenarios. During emergency situations, due to battery concerns and the

wide physical dispersement of individual agents, full wireless connectivity needs to

be continuously maintained among all mobile hosts. The application of traditional

MANET routing protocol in these situations consume more energy due to route

discovery mechanism, caused by frequent path disconnections. To handle path

disconnections, the stability and mobility of nodes are considered in Weighted-

CDSR. The stability is calculated based on received signal strength of a node with

all its neighbors. The mobility is computed as the ratio of new neighbors to the

total number of neighbors during the last two HELLO message transmissions.

To further reduce the CDS size, the degree of nodes is also considered in

weight calculation of a node. Like on-demand routing protocols, Weighted-CDSR

maintains routing tables at dominating nodes. The route discovery and data packet

forwarding involved only dominating nodes. The performance of Weighted-CDSR

is compared with AODV, DSR, DYMO and Weighted(Degree)-CDSR in terms of

end-to-end delay, packet delivery ratio, energy consumption with various network

size, node mobility and number of traffic sources. It is observed that the Weighted-

CDSR provides good packet delivery ratio, shorter delay and minimum energy

consumption.

The third study of this thesis presents BRP, a backbone routing protocol

based on node’s inter-contact time and ego-centrality, for mobile opportunistic

networks. As multi-copy based routing protocols generate lot of message copies,

which leads higher system overhead. Although, a single-copy based routing

188

reduces the overhead, the relay selection considered the single feature of the node’s

behavior. BRP is an improved single-copy forwarding protocol that reduces the

overhead and considered multiple social features of the node’s behavior such as

contact time and centrality. The successful delivery of message not only depends

on the number of connections, but also the encounter time and frequency. So, the

backbone nodes in BRP are selected based on the accumulated coverage condition,

where each node checks for the replacement path between pair of its neighbors

with higher centrality value and less inter-contact time. The backbone nodes are

involved in message forwarding and it also buffers the messages when the network

is disconnected. The performance of BRP was compared with Adaptive-Routing,

CoMANDR and PRoPHET in terms of various network size, mobility speed,

message lifetime and number of messages. It is found that, a single forwarding of

messages through nodes with more centrality and less inter-contact time reduced

the delay, energy consumption and increased the packet delivery ratio.

The fourth study of this thesis presents k−CCDS, a fault-tolerant coverage

control protocol based on sensing coverage of nodes for WSN. In dense WSN,

the sensing areas of sensor nodes may overlap with each other. In general, the

larger the overlap of the sensing range, the more redundant data generated, leads

to more energy consumption. The lifetime of WSN can be extended by doing

network operations with a subset of nodes and make the other nodes in sleep

state. A weight based coverage cost based on remaining energy of nodes, is

proposed to select a subset of nodes with more weight. These selected active nodes

need to be connected to reduce the delay during the communication. To further

provide reliability during communication, redundant nodes are added in k−CCDS.

The performance of k−CCDS was compared with A3Cov in terms of CDS size,

coverage, energy consumption, number of uncovered nodes and lifetime of the

189

network, with various network size and communication range. As coverage and

connectivity are maintained in k−CCDS with subset of nodes providing network

wide operations, the proposed protocol performed better than A3Cov in all the

cases.

The fifth study of this thesis presents CDS-EPRP, an energy-efficient

pressure-aware routing protocol for UWASN. Energy efficiency becomes more

critical and challenging in UWASN because of much higher transmission and

receiving power consumption of acoustic channel. When an underwater sensor

wants to send a packet to the surface sink, a multi-hop minimum energy consuming

path need to be determined. CDS-EPRP applied an ACO technique to select the

relay nodes with more energy and these relays provide a full coverage of the

network. The connectivity of these relays need to be maintained during the lifetime

of the network. Therefore, CDS-EPRP implemented a maintenance mechanism to

maintain the CDS structure inspite of node’s energy depletion and node’s mobility.

In order to save energy, node chooses the nearest neighbor as next-hop based on the

depth information. The performance of CDS-EPRP was compared with DBR and

VBF in terms of packet delivery ratio, energy consumption, and end-to-end delay.

It is observed that it provides good packet delivery ratio, less delay and energy

consumption.

8.2 CONCLUSION

An extensive study on routing is done for MANETs, MON, WSN and

UWASN. Specifically, a link stability based routing mechanism is studied for

MANETs as it has an effect in performance of routing protocols. The feasibility of

using CDS as communication layer is studied for efficient broadcasting, reducing

190

routing overhead, and energy-efficient routing in MANETs. The computation of

stability in terms of received signal strength, energy, link connectivity index and

mobility speed, are analyzed. The effect of these metrics on CDS for routing is

also verified. Also, the application of CDS as backbone for message relaying is

studied in MON.

The application of CDS for connected coverage is addressed for WSN.

Although, a CDS can preserve 1-coverage, the applicability of CDS for fault-

tolerant coverage is also studied. But, the redundant connectivity among the

dominating nodes are not considered. The use of ACO for CDS construction is

studied and an energy efficient routing is designed over CDS for UWASN.

8.3 SCOPE FOR FUTURE WORK

The proposed protocols of this research work can be extended in the

following ways:

- The proposed stability based routing protocols can be extended to meet the

QoS requirements of multimedia communications.

- A multicast routing protocol based on Weighted-CDSR can be designed for

cooperative communications in MANETs.

- The performance of centrality based forwarding can be analyzed for MONs.

- A k-connected k-coverage topology control can be developed for mobile

sensor networks.

- A data aggregation based CDS-EPRP can be developed to further reduce the

redundant packet transmissions in UWASN.

REFERENCES

[1] Adjih, C., Jaquet, P., and Viennot, L., Computing connected dominated setswith multipoint relays, International Journal Ad Hoc & Sensor WirelessNetworks, (2005), 27–39.

[2] Akyildiz, I. F., Pompili, D., and Melodia, T., Underwater acoustic sensornetworks: research challenges, Ad hoc networks, 3(3), (2005), 257-279.

[3] Akyildiz, I. F., Pompili, D., and Melodia, T., State-of-the-art in protocolresearch for underwater acoustic sensor networks, In Proceedings of the 1stACM international workshop on Underwater networks, (2006), 7-16.

[4] Akyildiz, I. F., and Vuran, M. C., Wireless Sensor Networks, John Wiley &Sons, (2010).

[5] Al-Karaki, J. N., and Kamal, A. E., Efficient Virtual Backbone Routing inMobile Ad Hoc Networks, Computer Networks, 52(2), (2008), 327-350.

[6] Ali, T., Jung, L. T., and Faye, I., End-to-End Delay and Energy EfficientRouting Protocol for Underwater Wireless Sensor Networks, WirelessPersonal Communications, 79(1), (2014), 339-361.

[7] Alves, J., Petroccia, R., and Potter, J. R., MPR: Multi-Point Relay protocolfor Underwater Acoustic Networks, In Proceedings of ACM InternationalConference on Underwater Networks & Systems, (2014).

[8] Alzoubi, K. M., Wan, P. J., and Frieder, O., Message-optimal connecteddominating sets in mobile ad hoc networks, In Proceedings of the 3rdACM international symposium on Mobile ad hoc networking & computing,(2002), 157-164.

[9] Ammari, H. M., On the problem of k-coverage in mission-oriented mobilewireless sensor networks, Computer Networks, 56, (2012), 1935–1950.

[10] Ammari, H. M., and Das, S. K., Centralized and Clustered k-CoverageProtocols for Wireless Sensor Networks, IEEE Transactions on Computers,61(1), (2012), 118–133.

[11] An, B., and Papavassiliou, S., A Mobility based clustering approach tosuuport mobility management and multicast routing in mobile ad hoc

191

192

wireless networks, International Journal of Network Management, 11,(2001), 387-395.

[12] Anitha, V. S., and Sebastian, M. P., Application oriented connecteddominating set-based cluster formation in wireless sensor networks, Journalof the Brazilian Computer Society, 17, (2011), 3–18.

[13] Ayaz, M., Abdullah, A., Faye, I., and Batira, Y., An efficient dynamicaddressing based routing protocol for underwater wireless sensor networks,Computer Communications, 35, (2012), 475-486.

[14] Ayaz, M., Baig, I., Abdullah, A., and Faye, I., A survey on routingtechniques in underwater wireless sensor networks, Journal of Network andComputer Applications, 34(6), (2011), 1908-1927.

[15] Aziz, A. A., Sekercioglu, Y. A., Fitzpatrick, P., and Ivanovich, M., A surveyon distributed topology control techniques for extending the lifetime ofbattery powered wireless sensor networks, IEEE Communications Surveys& Tutorials, , 15(1), (2013), 121-144.

[16] Bao, L., and Garcia-Luna-Aceves, J. J., Stable energy aware topologymanagement in ad hoc networks, Pervasive and Mobile Computing, 8(3),(2010), 313-327.

[17] Basagni, S., Distributed and Mobility-adaptive clustering for multimediasupport in multi-hop wireless networks, In Proceedings of IEEE 50thVehiclular Technology Conference, (1999), 889-893.

[18] Basagni, S., Mastrogiovanni, M., Panconesi, A., and Petrioli, C., LocalizedProtocols for Ad Hoc Clustering and Backbone Formation: A PerformanceComparison, IEEE Transactions on Parallel and Distributed Systems, 17(4),(2006), 292-306.

[19] Bian, Y., Yu, H. and Zeng, P., Construction of a fault tolerance connecteddominating set in wireless sensor network, In Proceedings of IEEEInternational Conference on Measuring Technology and MechatronicsAutomation (ICMTMA 2009), (2009), 1, 610–614.

[20] Blum, J., Ding, M., Thaeler, A., and Cheng, X., Connected dominating set insensor networks and MANETs, In Handbook of combinatorial optimization,(2005), 329-369.

[21] Boice, J., Garcia-Luna-Aceves, J. J., and Obraczka, K., Combining on-demand and opportunistic routing for intermittently connected networks, AdHoc Networks, 7(1), (2009), 201-218.

193

[22] Boukerche, A., Turgut, B., Aydin, N., Mohammad, Z.A., Boloni, L., andTurgut D., Routing protocols in ad hoc networks: A survey, ComputerNetworks, 55, (2011), 3032–3080.

[23] Cao, Y., and Sun, Z., Routing in delay/disruption tolerant networks: Ataxonomy, survey and challenges, IEEE Communications Surveys &Tutorials, 15(2), (2013), 654-677.

[24] Cardei, M., Cheng, M. X., Cheng, X., and Du, D. Z., Connected Dominationin Multihop Ad Hoc Wireless Networks, In JCIS, (2002), 251-255.

[25] Cardei, M., and Wu, J., Energy-efficient coverage problems in wireless ad-hoc sensor networks, Computer Communications, 29, (2006), 413–420.

[26] Carle, J., Gallais, A., and Simplot-Ryl, D., Preserving Area Coverage inWireless Sensor Networks by using Surface Coverage Relay DominatingSets, In Proceedings of the 10th IEEE Symposium on Computers andCommunications (ISCC 2005), (2005), 347–352.

[27] Chakeres, I., and Perkins, C., Dynamic MANET on-demand (DYMO)routing, http://tools.ietf.org/html/draft-ietf-manet-dymo-21, (2010).

[28] Chen, H., and Lou, W., GAR: Group aware cooperative routing protocol forresource-constraint opportunistic networks, Computer Communications, 48,(2014), 20-29.

[29] Cheng, G., Song, M., Zhang, Y., Xing, Y. H., and Bao, X. Y., Routingprotocol based on social characteristics for opportunistic networks, TheJournal of China Universities of Posts and Telecommunications, 21(1),(2014), 67-103.

[30] Cheng, X., Ding, M., Hongwei, D., and Jia, X., Virtual backboneconstruction in multihop ad hoc wireless networks, International Journalof Wireless Communications and Mobile Computing, 6, (2006), 183–190.

[31] Chizari, H., Hosseini, M., Salleh, S., Razak, S. A., and Abdullah, A. H.,EF-MPR, a new energy eFficient multi-point relay selection algorithm forMANET, The Journal of Supercomputing, 59(2), (2012), 744-761.

[32] Chun-Hao, Y., Kuo-Feng, S., An energy-efficient routing protocol inunderwater sensor networks, In Proceedings of the 3rd internationalconference on sensing technology, ICST, (2008).

[33] Clausen, T., and Jacquet, P., Optimized link state routing protocol (OLSR),Internet Draft www.ietf.org/rfc/rfc3626.txt, (2003).

194

[34] Czyzowicz, J., Dobrev, S., Fevens, T., Gonzalez-Aguilar, H., Kranakis, E.,Opatrny, J., and Urrutia, J., Local algorithms for dominating and connecteddominating sets of unit disk graphs with location aware nodes, In LATIN2008: Theoretical Informatics, (2008), 158-169.

[35] Dai, F., and Wu, J., An extended localized algorithm for connecteddominating set formation in ad hoc wireless networks, IEEE Transactionson Parallel and Distributed Systems, 15(10), (2004), 908-920.

[36] Daly, E. M., and Haahr, M., Social network analysis for information flowin disconnected delay-tolerant MANETs, IEEE Transactions on MobileComputing, 8(5), (2009), 606-621.

[37] Das, A., Mandal, C., Reade, C., and Aasawat, M. An improved greedyconstruction of minimum connected dominating sets in wireless networks, InProceedings of IEE Wireless Communications and Networking Conference(WCNC), (2011), 790-795.

[38] Das, B., and Bharghavan, V., Routing in ad-hoc networks usingminimum connected dominating sets, In Proceedings of IEEE InternationalConference on Communications, ICC’97 Montreal, Towards the KnowledgeMillennium, 1, (1997), 376-380.

[39] Datta, S., Stojmenovic, I., and Wu, J., Internal node and shortcut basedrouting with guaranteed delivery in wireless networks, Cluster computing,5(2), (2002), 169-178.

[40] De Rango, F., Fotino, M., and Marano, S., EE-OLSR: energy efficientOLSR routing protocol for mobile ad-hoc networks, In Proceedings of IEEEMilitary Communications Conference, MILCOM 2008, (2008), 1-7.

[41] Demmer, M., and Fall, K., DTLSR: delay tolerant routing for developingregions, In Proceedings of the 2007 ACM workshop on Networked systemsfor developing regions, (2007).

[42] Domingo, M. C., A distributed energy-aware routing protocol for underwaterwireless sensor networks, Wireless Personal Communications, 57(4),(2011), 607-627.

[43] Dorigo, M., Birattari, M., and Stutzle, T., Ant colony optimization, IEEEComputational Intelligence Magazine, 1(4), (2006), 28-39.

[44] Dorigo, M., and Stutzle, T., The ant colony optimization metaheuristic:Algorithms, applications, and advances, In Handbook of metaheuristics,(2003), 250-285.

195

[45] Du, D. Z., Thai, M. T., Li, Y., Liu, D., and Zhu, S., Strongly connecteddominating sets in wireless sensor networks with unidirectional links, InProceedings of Frontiers of WWW Research and Development-APWeb 2006,(2006), 13-24.

[46] Du, H., Wu, W., Ye, Q., Li, D., Lee, W., and Xu, X., CDS-based virtualbackbone construction with guaranteed routing cost in wireless sensornetworks, IEEE Transactions on Parallel and Distributed Systems, 24(4),(2013), 652-661.

[47] Duresi, A., and Paruchuri, V., Adaptive backbone protocol for heterogeneouswireless networks, Telecommunication Systems, 38(3-4), (2008), 83-97.

[48] El-Hajj, W., Trabelsi, Z., and Kountanis, D., Fast distributed dominatingset based routing in large scale MANETs, Computer Communications, 30,(2007), 2880–2891.

[49] Everett, M., and Borgatti, S. P., Ego network betweenness, Social networks,27(1), (2005), 31-38.

[50] Fall, K., and Varadhan, K., The ns Manual,www.isi.edu/nsnam/ns/doc/ns doc.pd f , (2011).

[51] Freeman, L. C., A set of measures of centrality based on betweenness,Sociometry, (1977), 35-41.

[52] Freeman, L. C., Centrality in social networks conceptual clarification, Socialnetworks, 1(3), (1979), 215-239.

[53] Gao, T., Ji, T., Jin, R., Ding, J., and Zou, J., A survey of routing protocol inunderwater sensor networks, Future Computer and Information Technology,86, (2014), 437-445.

[54] Ghosh, A., and Das, S.K., Coverage and connectivity issues in wirelesssensor networks: A survey, Pervasive and Mobile Computing, 4, (2008),303–334.

[55] Gopi, S., Govindan, K., Chander, D., Desai, U. B., and Merchant,S. N., E-PULRP: Energy optimized path unaware layered routingprotocol for underwater sensor networks, IEEE Transactions on WirelessCommunications, 9(11), (2010), 3391-3401.

[56] Guha, S., and Khuller, S., Approximation algorithms for connecteddominating sets, Algorithmica, 20(4), (1999).

196

[57] Guo, Z., Malakooti, S., Sheikh, S., Al-Najjar, C., Lehman, M., andMalakooti, B., Energy aware proactive optimized link state routing in mobilead-hoc networks, Applied Mathematical Modelling, 35(10), (2011), 4715-4729.

[58] Guo, Z., Malakooti, S., Sheikh, S., Al-Najjar, C., and Malakooti, B., Multi-objective OLSR for proactive routing in MANET with delay, energy, andlink lifetime predictions, Applied Mathematical Modelling, 35(3), (2011),1413-1426.

[59] Han, B., and Jia, W., Clustering wireless ad hoc networks with weaklyconnected dominating set, Journal of Parallel and Distributed Computing,67(6), (2007), 727-737.

[60] Hassan, D. S., Fahmy, H. M., and Bahaa-ElDin, A. M., RCA: Efficientconnected dominated clustering algorithm for mobile ad hoc networks,Computer Networks, 75, (2014), 177-191.

[61] He, J. S., Ji, S., Pan, Y., and Cai, Z., Approximation algorithms forload-balanced virtual backbone construction in wireless sensor networks,Theoretical Computer Science, 507, (2013), 2-16.

[62] He, J. S., Ji, S., Pan, Y., and Li, Y., Greedy construction of load-balancedvirtual backbones in wireless sensor networks, Wireless Communicationsand Mobile Computing, 14(7), (2014), 673-688.

[63] Heidemann, J., Ye, W., Wills, J., Syed, A., and Li, Y., Research challengesand applications for underwater sensor networking, In Proceedings of IEEEWireless Communications and Networking Conference, 1, (2006), 228-235.

[64] Ho, C. K., Singh, Y. P., and Ewe, H. T., An enhanced ant colony optimizationmetaheuristic for the minimum dominating set problem, Applied ArtificialIntelligence, 20(10), (2006), 881-903.

[65] Hong, Y., Bradley, D., Kim, D., Li, D., Tokuta, A. O., and Ding, Z.,Construction of higher spectral efficiency virtual backbone in wirelessnetworks, Ad Hoc Networks, 25, (2015), 228-236.

[66] Hosseini, M., Chizari, H., Soon, C. K., and Budiarto, R., RSS-based distancemeasurement in underwater acoustic sensor networks: An application ofthe lambert w function, In Proceedings of 4th International Conference onSignal Processing and Communication Systems (ICSPCS), (2010), 1-4.

197

[67] Hu, T., and Fei, Y., QELAR: A machine-learning-based adaptive routingprotocol for energy-efficient and Lifetime-extended underwater sensornetworks, IEEE Transactions on Mobile Computing, 9(6), (2010), 796-809.

[68] Huang, C. F., and Tseng, Y. C., A survey of solutions to the coverageproblems in wireless sensor networks, Journal of Internet Technology, 6,(2005), 1–8.

[69] Huang, C. J., Wang, Y. W., Liao, H. H., Lin, C. F., Hu, K. W., and Chang,T. Y., A power-efficient routing protocol for underwater wireless sensornetworks, Applied Soft Computing, 11(2), (2011), 2348-2355.

[70] Hui, P., Crowcroft, J., and Yonek, E., BUBBLE Rap: Social-basedforwarding in delay-tolerant networks, IEEE Transactions on MobileComputing, 10(11), (2011), 1576–1589.

[71] Islam, K., Akl, S. G., and Meijer, H., A Constant Factor Localized Algorithmfor Computing Connected Dominating Sets in Wireless Sensor Networks,In Proceedings of 14th IEEE International Conference on Parallel andDistributed Systems ICPADS’08, (2008), 559-566.

[72] Jang, H. C., Lien, Y. N., and Tsai, T. C., Rescue information systemfor earthquake disasters based on MANET emergency communicationplatform, In Proceedings of the 2009 International Conference on WirelessCommunications and Mobile Computing (IWCMC 09): Connecting theWorld Wirelessly , (2009), 623-627.

[73] Johnson, D., Hu, Y., and Maltz, D., The dynamic source routingprotocol for Mobile ad hoc networks for IPv4, In Internet draftwww.ietf.org/rfc/rfc4728.txt, (2007).

[74] Joshi, R. D., and Rege, P. P., Implementation and analytical modellingof modified optimised link state routing protocol for network lifetimeimprovement, IET communications, 6(10), (2012), 1270-1277.

[75] Jovanovic, R., and Tuba, M., Ant colony optimization algorithm withpheromone correction strategy for the minimum connected dominating setproblem, Computer Science and Information Systems, 10(1), (2013), 133-149.

[76] Jovanovic, R., Tuba, M., and Simian, D., Ant colony optimizationapplied to minimum weight dominating set problem, In Proceedings ofthe 12th WSEAS international conference on Automatic control, modelling

198

& simulation,, World Scientific and Engineering Academy and Society(WSEAS), (2010), 322-326.

[77] Karkkainen, T., Pitkanen, M., and Ott, J., Applications in Delay-Tolerantand Opportunistic Networks, Mobile Ad Hoc Networking: The Cutting EdgeDirections, Second Edition, Wiley Publications, (2013), 315-359.

[78] Katsaros, D., Dimokas, N., and Tassiulas, L., Social network analysisconcepts in the design of wireless ad hoc network protocols, IEEE Network,24(6), (2010), 23-29.

[79] Khedr, Ahmed M., and Osamy, W., Mobility-assisted minimum connectedcover in a wireless sensor network, Journal of Parallel DistributedComputing, 72, (2012), 827–837.

[80] Kim, D., Wang, W., Li, X., Zhang, Z., and Wu, W., A New ConstantFactor Approximation for Computing 3-Connected m-Dominating Sets inHomogeneous Wireless Networks, In Proceedings of IEEE (INFOCOM2010), (2010), 1–9.

[81] Kim, D., Wu, Y., Li, Y., Zou, F., and Du, D.-Z., Constructing minimumconnected dominating sets with bounded diameters in wireless networks,IEEE Transactions on Parallel and Distributed Systems, 20(2), (2009), 147-157.

[82] Kim, Y. H., Kim, C. M., Han, Y. H., Jeong, Y. S., and Park, D. S.,Betweenness of Expanded Ego Networks in Sociality-Aware Delay TolerantNetworks, In Ubiquitous Information Technologies and Applications,(2013), 499-505.

[83] Kui, X., Sheng, Y., Du, H., and Liang, J., Constructing a CDS-based networkbackbone for data collection in wireless sensor networks, InternationalJournal of Distributed Sensor Networks, 2013, (2013), 1-12.

[84] Lakkakorpi, J., Pitkanen, M., and Ott, J., Adaptive routing in mobileopportunistic networks, In Proceedings of the 13th ACM internationalconference on Modeling, analysis, and simulation of wireless and mobilesystems, (2010), 101-109.

[85] Lee, C. Y., Shiu, L. C., Lin, F. T., and Yang, C. S., Distributed topologycontrol algorithm on broadcasting in wireless sensor network, Journal ofNetwork and Computer Applications, 36(4), (2013), 1186-1195.

199

[86] Leu, S., and Chang, R., A weight-value algorithm for finding connecteddominating set in a MANET, Journal of Network and ComputerApplications, 35, (2012), 1615-1619.

[87] Levin, L., Kowalski, D. R., and Segal, M., Message and time efficient multi-broadcast schemes, Theoretical Computer Science, 569, (2015), 13-23.

[88] Li, V., Park, H. S., and Oh, H., A cluster-label-based mechanism forbackbones on mobile ad hoc networks, In Lecture notes in computer science,3970, (2006), 26–36.

[89] Li, Y., Thai, M. T., Wang, F., Yi, C. W., Wan, P. J., and Du, D. Z., On greedyconstruction of connected dominating sets in wireless networks, WirelessCommunications and Mobile Computing, 5(8), (2005), 927-932.

[90] Li, Y., Wu, Y., Ai, C., and Beyah, R., On the construction of k-connected m-dominating sets in wireless networks, Journal of combinatorialoptimization, 23, (2012), 118–139.

[91] Li, Z., and Shen, H., SEDUM: Exploiting Social Networks in Utility-BasedDistributed Routing for DTNs, IEEE Transactions on Computers, 62(1),(2013), 83-97.

[92] Liang, O., Sekercioglu, Y. A., and Mani, N., A survey of multipointrelay based broadcast schemes in wireless ad hoc networks, IEEECommunications Surveys and Tutorials, 8(1-4), (2006), 30-46.

[93] Lim, H., and Kim, C., Flooding in wireless ad hoc networks, ComputerCommunications, 24(3), (2001), 353-363.

[94] Lindgren, A., Davies, E., Grasic, S., and Doria, A., Probabilisticrouting protocol for intermittently connected networks,http://tools.ietf.org/html/rfc6693.txt, (2012).

[95] Liu, T., Zhu, Y., Jiang, R., and Li, B., A sociality-aware approach tocomputing backbone in mobile opportunistic networks, Ad Hoc Networks,24, (2015), 46-56.

[96] Luo, G., Zhang, J., Huang, H., Qin, K., and Sun, H., Exploiting intercontacttime for routing in delay tolerant networks, Transactions on EmergingTelecommunications Technologies, 24(6), (2013), 589-599.

[97] Macker, J., and Corson, S., Mobile Ad hoc Networks (MANET). IETFWorking Group Charter, (1997).

200

[98] Macone, D., Oddi, G., and Pietrabissa, A., MQ-Routing: Mobility-, GPS-and energy-aware routing protocol in MANETs for disaster relief scenarios,Ad Hoc Networks , 11(3), (2013), 861-878.

[99] Meghanathan, N., and Farago, A., On the stability of paths, Steiner trees andconnected dominating sets in mobile ad hoc networks, Ad Hoc Networks, 6,(2008), 744–769.

[100] Mahjoub, D., and Matula, D. W., Constructing efficient rotatingbackbones in wireless sensor networks using graph coloring, ComputerCommunications, 35, (2012), 1086-1097.

[101] Miao, J., Hasan, O., Mokhtar, S. B., Brunie, L., and Gianini, G., A delayand cost balancing protocol for message routing in mobile delay tolerantnetworks, Ad Hoc Networks, 25, (2015), 430-443.

[102] Misra, R., and Mandal, C., Rotation of CDS via Connected DomaticPartition in Ad hoc Sensor Networks, IEEE Transactions on Mobilecomputing, 8(4), (2009), 488-499.

[103] Misra, R., and Mandal, C., Minimum connected dominating set using acollaborative cover heuristic for ad hoc sensor networks, IEEE Transactionson Parallel and Distributed Systems, (2010), 21(3), 292-302.

[104] Misra, S., Kumar, M.P., and Obaidat, M.S., Connectivity preservinglocalized coverage algorithm for area monitoring using wireless sensornetworks, Computer Communications, 34, (2011), 1484-1496.

[105] Montolio-Aranda, P., Garcia-Alfaro, J., and Megıas, D., Improved floodingof broadcast messages using extended multipoint relaying, Journal ofNetwork and Computer Applications, 34(2), (2011), 542-550.

[106] Mota, V. F., Cunha, F. D., Macedo, D. F., Nogueira, J., and Loureiro, A. A.,Protocols, mobility models and tools in opportunistic networks: A survey,Computer Communications , 48, (2014), 5–19.

[107] Moulahi, T., Nasri, S., and Guyennet, H., Broadcasting based on dominatedconnecting sets with MPR in a realistic environment for WSNs and ad hoc,Journal of Network and Computer Applications, 35, (2012), 1720-1727.

[108] Moussaoui, A., Semchedine, F., and Boukerram, A., A link-state QoSrouting protocol based on link stability for Mobile Ad hoc Networks, Journalof Network and Computer Applications, 39, (2014), 117-125.

201

[109] Moussaoui, A., and Boukeream, A., A survey of routing protocols based onlink-stability in mobile ad hoc networks, Journal of Network and ComputerApplications, 47, (2015), 1-10.

[110] Nicolaou, N., See, A., Xie, P., Cui, H. J., and Maggiorini, D., Improvingthe robustness of location-based routing for underwater sensor networks, InProceedings of IEEE OCEANS’07, (2007), 1–6.

[111] Noh, Y., Lee, U., Wang, P., Choi, B. S. C., and Gerla, M., VAPR: Void-AwarePressure Routing for Underwater Sensor Networks, IEEE Transactions onMobile Computing, 12(5), (2013), 895-908.

[112] Ott, J., Demmer, M., and Perreault, S., Delay-Tolerant Networking TCPConvergence-Layer Protocol, http://tools.ietf.org/html/rfc7242, (2014).

[113] Pan, D. R., Cao, W., Liu, X., Sun, J. J., and Shi, X. J., A performance-guarantee routing algorithm in complex distributed opportunistic networks,The Journal of China Universities of Posts and Telecommunications, 19,(2012), 87-93.

[114] Pelusi, L., Passarella, A., and Conti, M., Opportunistic networking: dataforwarding in disconnected mobile ad hoc networks, IEEE CommunicationsMagazine, 44(11), (2006), 134-141.

[115] Pemmaaraju, S.V., and Piwani, I.A., Energy Conservation via DomaticPartitions, In Proceedings of the 7th ACM international symposium onMobile ad hoc networking and computing, (2006), 143–154.

[116] Perkins, C., Belding-Royer, E., and Das, S., Ad hoc On-Demand DistanceVector Routing, Internet Draft www.ietf.org/rfc/rfc3561.txt, (2003).

[117] Polat, B. K., Sachdeva, P., Ammar, M. H., and Zegura, E. W., Messageferries as generalized dominating sets in intermittently connected mobilenetworks, Pervasive and Mobile Computing, 7(2), (2011), 189-205.

[118] Samuel, H., Zhuang, W., and Preiss, B., DTN based dominating set routingfor MANET in heterogeneous wireless networking, Mobile Networks andApplications, 14(2), (2009), 154-164.

[119] Pompili, D., Melodia, T., and Akyildiz, I. F., Distributed routing algorithmsfor underwater acoustic sensor networks, IEEE Transactions on WirelessCommunications, 9(9), (2010), 2934-2944.

202

[120] Pompili, D., and Akyildiz, I. F., Overview of networking protocols forunderwater wireless communications, IEEE Communications Magazine,47(1), (2009), 97-102.

[121] Potluri, A., and Singh, A., Hybrid metaheuristic algorithms for minimumweight dominating set, Applied Soft Computing, 13(1), (2013), 76-88.

[122] Potluri, A., and Singh, A., Metaheuristic algorithms for computingcapacitated dominating set with uniform and variable capacities, Swarm andEvolutionary Computation, 13, (2013), 22-33.

[123] Qayyum, A., Viennot, L., and Laouiti, A., Multipoint relaying for floodingbroadcast messages in mobile wireless networks, In Proceedings of the35th Annual Hawaii International Conference on System Sciences (HICSS),(2002), 3866-3875.

[124] Qureshi, H. K., Rizvi, S., Saleem, M., Ali Khayam, S., Rakocevic, V.,and Rajarajan, M., Poly: A reliable and energy efficient topology controlprotocol for wireless sensor networks, Computer Communications, 34,(2011), 1235–1242.

[125] Qureshi, H. K., Rizvi, S., Saleem, M., Ali Khayam, S., Rakocevic, V., andRajarajan, M., Evaluation and improvement of CDS-based topology controlfor wireless sensor networks, Wireless Networks, 19(1), (2013), 31–46.

[126] Raffelsberger, C., and Hellwagner, H., Combined Mobile Ad-hoc andDelay/Disruption-tolerant Routing, Lecture Notes in Computer Science,8487, (2014), 1-14.

[127] Rai, M., Verna, S., and Tapaswi, S., A Power Aware Minimum ConnectedDominating Set for Wireless Sensor Networks, Journal of Networks, 6(6),(2009), 511-519.

[128] Ramrekha, T. A., Talooki, V. N., Rodriguez, J., and Politis, C., EnergyEfficient and Scalable Routing Protocol for Extreme Emergency Ad HocCommunications, Mobile Network and Applications, 17, (2012), 312-324.

[129] Rizvi, S., Qureshi, H. K., Khayam, S. A., Rakocevic, V., and Rajarajan,M., A1: An energy efficient topology control algorithm for connected areacoverage in wireless sensor networks, Journal of Network and ComputerApplications, 35(2), (2012), 597-605.

[130] Rong-rong, Y., Bin, L., Ya-qian, L., and Xiaoe-chen, H., Adaptivelyfault-tolerant topology control algorithm for wireless sensor networks, The

203

Journal of China Universities of Posts and Telecommunications, 19, (2012),13–18.

[131] Ruan, L., Du, H., Jia, X., Wu, W., Li, Y., and Ko, K. I., Agreedy approximation for minimum connected dominating sets, TheoreticalComputer Science, 329(1), (2004), 325-330.

[132] Sakai, K., Sun, M. T., and Ku, W. S., Maintaining CDS in Mobile ad hocnetworks, Lectuere Notes in Computer Scicence, 5258, (2008), 141-153.

[133] Sakai, K., Sun, M. T., Ku, W. S., and Huang, S. C. H., On mobility handlingof sub-optimal timer-based CDS construction, Ad Hoc Networks, 29, (2015),1-14.

[134] Sarma, N., and Nandi, S., Route Stability Based QoS Routing in Mobile AdHoc Networks, Wireless Personal Communications, 54, (2010), 203-224.

[135] Sausen, P. S., Spohn, M. A., and Perkusich, A., Broadcast routing inwireless sensor networks with dynamic power management and multi-coverage backbones, Information Sciences, 180, (2010), 653–663.

[136] Schurgot, M. R., Comaniciu, C., and Jaffres-Runser, K., Beyond traditionalDTN routing: social networks for opportunistic communication, IEEECommunications Magazine, 50(7), (2012), 155-162.

[137] Senel, F., Akkaya, K., Erol-Kantarci, M., and Yilmaz, T., Self-deployment of mobile underwater acoustic sensor networks formaximized coverage and guaranteed connectivity, Ad Hoc Networks,doi:doi:10.1016/j.adhoc.2014.09.013, (2014).

[138] Shang, W., Wan, P., Yao, F., and Hu, X., Algorithms for minimum m-connected k-tuple dominating set problem, Theoretical Computer Science,381, (2007), 241–247.

[139] Shaukat, K., and Syrotiuk, V. R., Statistical monitoring to control a proactiverouting protocol, In Ad-Hoc, Mobile, and Wireless Networks, (2007), 46-58.

[140] Sheu, P. R., Tsai, H. Y., Lee, Y. P., and Cheng, J. Y., On calculatingstable connected dominating sets based on Link stability for Mobile ad hocnetworks, Tamkang Journal of Science and Engieering, 12(4), (2009), 417-228.

[141] Shukla, K. K., and Sah, S., Construction and maintenance of virtualbackbone in wireless networks, Wireless Networks, 19(5), (2013), 969-984..

204

[142] Sinha, P., Sivakumar, R., and Bharghavan, V., Enhancing ad hoc routing withdynamic virtual infrastructures, In Proceedings of the 20th IEEE INFOCOMConference, 3, (2001), 1763-1772.

[143] Sivakumar, R., Das, B., and Bharghavan, V., An improved spine-basedinfrastructure for routing in ad hoc networks, IEEE Transaction on Paralleland Distributed Systems, 13, (2001), 14-25.

[144] Sivakumar, R., Sinha, P., and Bharghavan, V., CEDAR: a core extractiondistributed ad hoc routing algorithm, IEEE Journal on Selected Areas inCommunications, 17, (1999), 1454-1465.

[145] Smys, S., and Josemin, B. G., Efficient self-organized backbone formation inmobile ad hoc networks (MANETs), Computers & Electrical Engineering,(2011), doi:10.1016/j.compeleceng.2011.03.006

[146] Sozer, E. M., Stojanovic, M., and Proakis, J. G., Underwater acousticnetworks, IEEE Journal of Oceanic Engineering, 25(1), (2000), 72-83.

[147] Spohn, M. A., and Garcia-Luna-Aceves, J. J., Improving route discoveryin on-demand routing protocols using tow-hop connected dominating sets,Pervasive and Mobile Computing, 4, (2006), 509-531.

[148] Spyropoulos, T., Psounis, K., and Raghavendra, C. S., Spray and wait:an efficient routing scheme for intermittently connected mobile networks,In Proceedings of the 2005 ACM SIGCOMM workshop on Delay-tolerantnetworking, (2005), 252-259.

[149] Spyropoulos, T., Rais, R. N., Turletti, T., Obraczka, K., and Vasilakos, A.,Routing for disruption tolerant networks: taxonomy and design, Wirelessnetworks, 16(8), (2010), 2349-2370.

[150] Srinivasan, P., and Kamalakkannan, P., RSEA-AODV: Route Stability andEnergy Aware Routing for Mobile Ad Hoc Networks, International Journalof Computers, Communications and Control, 8(6), (2013), 891-900.

[151] Stojmenovic, I., Comments and Corrections to “Dominating Setsand Neighbor Elimination-Based Broadcasting Algorithms in WirelessNetworks”, IEEE Transactions on Parallel and Distributed Systems, 15(11),(2004).

[152] Stojmenovic, I., Seddigh, M., and Zunic, J., Dominating sets and neighborelimination-based broadcasting algorithms in wireless networks, IEEETransactions on Parallel and Distributed Systems, 13(1), (2002), 14-25.

205

[153] Su, W., Lee, S. J., and Gerla, M., Mobility prediction and routing in ad hocwireless networks, International Journal of Network Management, 11(1),(2001), 3-30.

[154] Sundar, S., and Singh, A., New heuristic approaches for the dominating treeproblem, Applied Soft Computing, 13(12), (2013), 4695-4703.

[155] Tan, W. C. W., Bose, S. K., and Cheng, T. H., Power and mobility awarerouting in wireless ad hoc networks, IET communications, 6(11), (2012),425-1437.

[156] Torkestani, J. A., An Adaptive energy-efficient area coverage algorithm forwireless sensor networks, Ad Hoc Networks, 11(6), (2013), 1655–1666.

[157] Torkestani, J. A., Energy-efficient backbone formation in wireless sensornetworks, Computers & Electrical Engineering, 39(6), (2013), 1800-1811.

[158] Torkestani, J. A., A stable virtual backbone for wireless MANETS,Telecommunication Systems, 55(1), (2014), 137-148.

[159] Vahdat, A., and Becker, D., Epidemic routing for partially connected ad hocnetworks, Technical Report CS-200006, Duke University, (2000).

[160] Vazquez-Rodas, A., and Luis, J., A centrality-based topology controlprotocol for wireless mesh networks, Ad Hoc Networks, 24, (2015), 34-54.

[161] Wahid, A., Leel, S., and Kim, D., A reliable and energy-efficient routingprotocol for underwater wireless sensor networks, International Journal ofCommunication Systems, 27, (2014), 2048–2062

[162] Wan, P. J., Alzoubi, K. M., and Frieder, O., Distributed construction ofconnected dominating set in wireless ad hoc networks, Mobile Networks andApplications, 9(2), (2004), 141-149.

[163] Wang, F., Min, M., Li, Y., and Du, D., On the construction of stable virtualbackbones in Mobile ad hoc networks, In Proceedings of IEEE internationalPerformance Computing and Communicaions Conference (IPCCC 2005),(2005), 355-362.

[164] Wang, K., and Guo, H., An improved routing algorithm based on social linkawareness in delay tolerant networks, Wireless personal communications,75(1), (2014), 397-414.

206

[165] Wang, N. C., and Lee, C. Y., A reliable QoS aware routing protocol with slotassignment for mobile ad hoc networks, Journal of Network and ComputerApplications, 32(6), (2009), 1153-1166.

[166] Wang, N., Huang, Y., and Chen, J., A stable weight-based on-demandrouting protocol for mobile ad hoc networks, Information Sciences, 177,(2007), 5522–5537.

[167] Wightman, P. M., and Labrador, M. A., A family of simpledistributed minimum connected dominating set-based topology constructionalgorithms, Journal of Network and Computer Applications, 34, (2011),1997–2010.

[168] Wightman, P.M., and Labrador, M.A., Atarraya: A Simulation Tool to Teachand Research Topology Control Algorithms for Wireless Sensor Networks,In Proceedings of the 2nd International Conference on Simulation Tools andTechniques. ICST (Institute for Computer Sciences, Social-Informatics andTelecommunications Engineering), (2009).

[169] Wu, D., Wang, R., and Zhen, Y., Link stability-aware reliable packettransmitting mechanism in mobile ad hoc network, International Journalof Communication Systems, 25(12), (2012), 1568-1584.

[170] Wu, J., Dai, F., Gao, M., and Stojmenovic, I., On calculating power-awareconnected dominating sets for efficient routing in ad hoc wireless networks,IEEE/KICS Journal of Communications and Networks, 4, (2002), 59-70.

[171] Wu, J., and Li, H., On Calculating connected dominating set forefficient routing in ad hoc wireless networks, In Proceedings of ACM 3rdinternational workshop on Discrete algorithms and methods for mobilecomputing and communications, (1999), 7-14.

[172] Wu, J., and Li, H., A dominating-set-based routing scheme in ad hoc wirelessnetworks, Telecommunication Systems, 18(1-3), (2001), 13-36.

[173] Wu, J., Lou, W., and Dai, F., Extended multipoint relays to determineconnected dominating sets in MANETs, IEEE Transactions on Computers,55(3), (2006), 334-347.

[174] Wu, J., Wu, B., and Stojmenovic, I., Power-aware broadcasting and activityscheduling in ad hoc wireless networks using connected dominating sets,Wireless Communications and Mobile Computing, 3(4), (2003), 425-438.

207

[175] Wu, Y., Deng, S., and Huang, H., Performance analysis of hop-limitedepidemic routing in DTN with limited forwarding times, InternationalJournal of Communication Systems, DOI: 10.1002/dac.2845, (2014).

[176] Wu, Y., and Li, Y., Construction Algorithms for k-Connected m-DominatingSets in Wireless Sensor Networks, In Proceedings of the 9th ACMinternational symposium on Mobile ad hoc networking and computing,(2008), 83-90.

[177] Wu, Y., Wang, F., Thai, My T., and Li, Y., Constructing k-connected m-dominating set in wireless sensor networks, In Proceedings of IEEE MilitaryCommunicatons Conference (MILCOM 2007), (2007), 1–7.

[178] Xia, Y., and Yeo, C. K., Mitigating the impact of node mobility using mobilebackbone for heterogeneous MANETs, Computer Communications, 35(10),(2012), 1217-1230.

[179] Xiao, Y. (Ed.)., Underwater acoustic sensor networks, CRC Press, (2010).

[180] Xie, P., Cui, J. H., and Lao, L., VBF: vector-based forwarding protocolfor underwater sensor networks, In NETWORKING 2006. NetworkingTechnologies, Services, and Protocols; Performance of Computer andCommunication Networks; Mobile and Wireless Communications Systems,(2006), 1216-1221.

[181] Xie, P., Zhou, Z., Nicolaou, N., See, A., Cui, J. H., and Shi, Z., Efficientvector-based forwarding for underwater sensor networks, EURASIP Journalon Wireless Communications and Networking, (2010), 1-13.

[182] Xie, P., Zhou, Z., Peng, Z., Yan, H., Hu, T., Cui, J. H., and Zhou, S.,Aqua-Sim: an NS-2 based simulator for underwater sensor networks, InProceedings of IEEE OCEANS 2009, MTS/ Biloxi-Marine Technology forOur Future: Global and Local Challenges, (2009), 1-7.

[183] Yan, H., Shi, Z. J., and Cui, J. H., DBR: depth-based routing for underwatersensor networks. In Proceedings of NETWORKING 2008 Ad Hoc and SensorNetworks, Wireless Networks, Next Generation Internet, (2008), 72-86.

[184] Yang, S., Dai, F., Cardei, M., Wu, J., and Patterson, F., On ConnectedMultiple Point Coverage in Wireless Sensor Networks, InternationalJournal of Wireless Information Networks, 13(4), (2006), 289–301.

[185] Yang, S., and Wu, J., Adaptive backbone-based routing in delay tolerantnetworks, In Proceedings of 2013 IEEE 10th International Conference onMobile Ad-Hoc and Sensor Systems (MASS), (2013), 356-364.

208

[186] Yen, Y. S., Chang, H. C., Chang, R. S., and Chao, H. C., Routing withadaptive path and limited flooding for mobile ad hoc networks, Computers& electrical engineering, 36(2), (2010), 280-290.

[187] Yi, J., Adnane, A., David, S., and Parrein, B., Multipath optimized link staterouting for mobile ad hoc networks, Ad Hoc Networks, 9(1), (2011), 28-47.

[188] Yu, H., Yao, N., and Liu, J., An adaptive routing protocolin underwater sparse acoustic sensor networks, Ad Hoc Network,http://dx.doi.org/10.1016/j.adhoc.2014.09.016, (2014).

[189] Yu, J., Deng, X., Yu, D., Wang, G., and Gu, X., CWSC:Connectedk-Coverage Working Sets Construction Algorithm in WirelessSensor Networks, AEUE - International Journal of Electronics andCommunications, 67(11), (2013), 937–946.

[190] Yu, J., Wang, N., Wang, G., Yu, D., Connected dominating sets inwireless ad hoc and sensor networks – A comprehensive survey, ComputerCommunications, 36, (2013), 121–134.

[191] Yu, J., Zhang, Q., Yu, D., Chen, C., and Wang, G., Domatic partition inhomogeneous wireless sensor networks, Journal of Network and ComputerApplications, 37, (2014), 186–193.

[192] Yu, R., Wang, X., and Das, K., EEDTC: energy efficient dominatingtree construction in multi-hop wireless networks, Pervasive and MobileComputing, 5, (2009), 318-333.

[193] Yuanyuan, Z., Jia, X., and Yanxiang, H., Energy efficient distributedconnected dominating sets construction in wireless sensor networks,In Proceedings of the 2006 international conference on Wirelesscommunications and mobile computing, (2006), 797-802.

[194] Zeng, Y., Sreenan, C. J., Xiong, N., Yang, L. T., and Park, J. H.,Connectivity and coverage maintenance in wireless sensor networks,Journal of Supercomputing, 52, (2010), 23–46.

[195] Zhang, H. L., Xiong, Y., and Miao, F. Y., Minimum MPR Set Selection:an Improved Ant Colony Optimization Approach, Journal of ChineseComputer Systems, 33(1), (2012), 126-129.

[196] Zhang, S., Wang, Z., Liu, M., and Qiu, M., Energy-aware routing for delay-sensitive underwater wireless sensor networks, Science China InformationSciences, 57(10), (2014), 1-14.

209

[197] Zhao, J. X., and Sun, G. Z., Energy-Efficient Routing Protocol forUnderwater Acoustic Sensor Networks, Journal of Jilin University(Information Science Edition), 3, (2008), 17.

[198] Zhao, Y., Wu, J., Li, F., and Lu, S., On maximizing the lifetime of wirelesssensor networks using virtual backbone scheduling, IEEE Transactions onParallel and Distributed Systems, 23(8), (2012), 1528-1535.

[199] Zhu, C., Zheng, C., Shu, L., and Han, G., A survey on coverage andconnectivity issues in wireless sensor networks, Journal of Network andComputer Applications, 35, (2012), 619–632.

[200] Zorzi, M., Casari, P., Baldo, N., and Harris, A. F., Energy-efficient routingschemes for underwater acoustic networks, IEEE Journal on Selected Areasin Communications, 26(9), (2008), 1754-1766.

LIST OF PUBLICATIONS

I - International Journal Publications

1. Ramalakshmi R and Radhakrishnan S, “Improving Route Discovery Using

Stable Connected Dominating Set in MANETS”, International Journal

on Applications of Graph Theory in Wireless Ad hoc Networks and Sensor

Networks (GRAPH-HOC), Volume 4, Number 1, March 2012.

2. R. Ramalakshmi and S. Radhakrishnan, “Weighted Dominating Set based

Routing for Ad Hoc Communications in Emergency and Rescue Scenarios”,

Wireless Networks - Springer, Volume 21, Issue 2, Feb 2015. (IF: 1.055)

3. R. Ramalakshmi and S. Radhakrishnan, “Connected k−Coverage Topology

Control for Area Monitoring in Wireless Sensor Networks”, Wireless

Personal Communications - Springer, doi:10.1007/s11277-015-2675-9.

(IF: 0.979)

II - International Conference Publications

1. R.Ramalakshmi and S. Radhakrishnan, “Energy Efficient Stable Connected

Dominating Set Construction in Mobile Ad hoc Networks”, Lecture

Notes of the Institute for Computer Sciences, Social Informatics and

Telecommunications Engineering, Volume 84, Part 1, 63-72, 2012.

(Scopus Indexed)

2. R.Ramalakshmi and S. Radhakrishnan, “Coverage and

Connectivity Guaranteed Deployment Pattern for WSN”, Lecture

Notes in Electrical Engineering, Volume 131, 341-347, 2013.

(Scopus Indexed)

210

211

III - Communicated to International Journals

1. Ramalakshmi R and Radhakrishnan S, “Stability based Energy-Efficient

Link-State Hybrid Routing Protocol for Mobile Ad Hoc Networks”,

International Journal of Networks and Computer Applications - Elsevier.

(Under Review)

2. R. Ramalakshmi and S. Radhakrishnan, “An Ego-Centrality and Contact-

Duration based Backbone Routing Protocol for Mobile Opportunistic

Networks”, Ad Hoc Networks - Elsevier. (Under Review)

3. R. Ramalakshmi and S. Radhakrishnan, “ACO-based Connected

Dominating Set for Energy-Efficient Pressure-Aware Routing in Underwater

Acoustic Sensor Networks”, Computer Communications - Elsevier. (Under

Review)

VITAE

Mrs.R.Ramalakshmi was born on 16th April 1976 to Mr.S.Ramar and

Mrs.R.Krishnammal, at M.Pudupatti in Virudhunagar district. She completed her

schooling in the year 1993. Mrs.R.Ramalakshmi received her under graduate

degree B.Sc (Computer Science) from Madurai Kamaraj University (Tamilnadu,

India) in the year 1996 and got 14th University rank. She received her first post

graduate degree, MCA (Computer Applications) from Madurai Kamaraj University

and obtained first class with distinction in the year 1999. She also received

her second P.G degree in M.E (CSE) in the year 2007 from Anna University

(Tamilnadu, India) and obtained first class with distinction.

Mrs.R.Ramalakshmi worked at Election Department, Secretariat,

Tamilnadu as a Programmer from June 1999 to May 2000. She then worked at

Ayya Nadar Janaki Ammal College, Sivakasi as a lecturer in the Department of

Computer Applications from June 2000 to May 2001. She joined at Arulmigu

Kalasalingam College of Engineering, Srivilliputhur as a lecturer in the Department

of Information Technology in June 2001 and continuing her service in Computer

Science and Engineering Department from December 2006, as Senior Assistant

Professor, after the college has got the deemed university status (Kalasalingam

University). During these 15 years of teaching, she has successfully taken up a

number of teaching and administrative assignments. She has received a Young

Scientist Fellowship and she is also a life member of ISTE and CSI.

Currently she is specializing in the area of routing in Mobile Ad hoc

networks, Wireless Sensor Networks and focusing on application of connected

dominating set for her doctoral work.

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