MVC: Modified VIKOR Model based Clustering Protocol for …njavaid.com/Tauseef-Thesis.pdfMVC:...

MVC: Modified VIKOR Model based

Clustering Protocol for WSNs

By

Tauseef Shah

Registration Number: -REE-049/ISB

MS Thesis

In

Electrical Engineering

COMSATS Institute of Information Technology

Islamabad – PakistanSpring, 2013

CIIT/FA11



A Thesis presented to


In partial fulfillment

of the requirement for the degree of

MS (Electrical Engineering)

By

Tauseef Shah

CIIT/FA11-REE-049/ISB

SPRING 2013


ii

Engineering).



A post Graduate Thesis submitted to Department of Electrical Engineering as

partial fulfillment of the requirement for the award of Degree of M.S

(Electrical

Name Registration Number

Tauseef Shah CIIT/FA11-REE-049/ISB

Supervisor

Dr. Safdar H. Bouk

Assistant Professor, Department of Electrical Engineering

Islamabad Campus

COMSATS Institute of Information and Technology (CIIT)

Islamabad

Co-Supervisor

Dr. Nadeem Javaid

Assistant Professor,

Center for Advanced Studies in Telecommunications (CAST),

COMSATS Institute of Information and Technology (CIIT)

Islamabad

Spring, 2013

iii

Final Approval

This thesis titled



By

Tauseef Shah


Has been approved

For the COMSATS Institute of Information Technology, Islamabad

External Examiner: __________________________________

(To be decided)

Supervisor: ________________________

Dr. Safdar H. Bouk/Assistant Professor,

Department of Electrical Engineering CIIT, Islamabad

Co-Supervisor: ________________________

Dr. Nadeem Javaid/Assistant professor,


CIIT, Islamabad.

Head of Department:________________________

Dr. Raja Ali Riaz / Associate professor,

Department of Electrical Engineering,

CIIT, Islamabad.

iv

Declaration

I Tauseef Shah, CIIT/FA11-REE-049/ISB hereby declare that I have produced the work

presented in this thesis, during the schedule period of study. I also declare that I have

not taken any material from any source except ref erred to wherever due that amount of

plagiarism is within acceptable range. If a violation of HEC rules on research has

occurred in this thesis, I shall be liable to punishable action under the plagiarism rule of

the HEC.

Date: ___________

_____________

Tauseef Shah


v

Certificate

Date: _________________

Supervisor: ________________________

Dr. Safdar H. Bouk/Assistant Professor,

Department of Electrical Engineering CIIT, Islamabad

Co-Supervisor: ________________________

Dr. Nadeem Javaid/Assistant professor,


CIIT, Islamabad.

Head of Department:________________________

Dr. Raja Ali Riaz / Associate professor,

Department of Electrical Engineering,

CIIT, Islamabad.

It is certified that Tauseef Shah, CIIT/FA11-REE-049/ISB has carried out all the

work related to th is thesis under my supervision at the Department of Electrical

Engineering, COMSATS Institute of Information and Technology, Islamabad and the

work fulfills the requirements for award of MS degree.

vi

DEDICATION

Dedicated

To

My Family

Teachers and Friends

Whom prayers and attention enable me to reach this

extent

.

vii

ACKNOWLEDGMENT

Tauseef Shah


I thank to Almighty ALLAH for His Blessing and Guidance in completing this project

in time.

.

I would like to admit the great and unconditional academic support and

encouragement from my family. This success is all because of their prayers and

help in my university career.

I would also like to specially thank my highly regarded teacher and supervisor of

thesis Dr. Safdear Hussain Bouk and co-supervisor Dr. Nadeem Javaid for their

utmost help and precious guidance and expert advices during execution of the

work and throughout my studies at COMSATS Institute of Information Technology,

Islamabad.

I am extremely obliged to my research fellows , Imran Israr , Aziz-ur-

Rehman, Mohammad Mateen Yaqoob and Haad Akmal, who helped me in some

way or the other in completing my thesis.

At the end my gratitude is for my parents and friends who supported me morally

to reach this stage.

.

Mansoor Mustafa,

viii

ABSTRACT

MVC: Modified VIKOR Model based Clustering Technique

for WSNs

Stability and lifetime of Wireless Sensor Networks (WSNs) mainly depend on energy

of each node in the network. Hence, it is necessary for WSNs to be energy efferent.

There are different methods to preserve energy in WSNs and clustering is one of those

methods. It is a technique to divide whole network into small blocks, each having a

managing node, called cluster head (CH) and rest act as members. CH is responsible to

provide communication bridge between members and the base station. In this paper, we

propose a distributed clustering scheme that uses multiple criteria i.e. residual energy,

node density, distance to the Base Station (BS) and average distance between a node

and its neighbors, to select a CH. Modified VIKOR method is used to outrank the

potential nodes as CHs. The realistic multi-hoping communication model is used,

instead of single hop as in previous schemes. Simulation results show that our

purposed technique performs much better than those previous methods in terms of

energy efficiency and network life time. Our proposed scheme has less CH deformation

and control overhead.

.

ix

Table of Contents

1 Introduction 2

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2 Evaluation of WSNs 8

2.1 Brief History of WSNs . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.2 Introduction of WSNs . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.3 Challenges in WSNs . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.3.1 Energy constrained . . . . . . . . . . . . . . . . . . . . . . . 11

2.3.2 Network Deployment . . . . . . . . . . . . . . . . . . . . . . 11

2.3.3 Self-Management . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3.4 Wireless Networking and Communication Channel . . . . . . 12

2.3.5 Hardware Limitations . . . . . . . . . . . . . . . . . . . . . 12

2.4 Difference between Traditional Networks and WSNs . . . . . . . . . 12

2.5 Sensor Node architecture . . . . . . . . . . . . . . . . . . . . . . . . 13

2.5.1 Sensing Subsystem . . . . . . . . . . . . . . . . . . . . . . . 14

2.5.2 Processing Subsystem . . . . . . . . . . . . . . . . . . . . . 14

2.5.3 Communication Subsystem . . . . . . . . . . . . . . . . . . . 14

2.5.4 Power Subsystem . . . . . . . . . . . . . . . . . . . . . . . . 15

2.6 Application of WSNs . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.6.1 Military Application . . . . . . . . . . . . . . . . . . . . . . 16

2.6.2 Environmental Application . . . . . . . . . . . . . . . . . . . 16

2.6.3 Health-Care Applications . . . . . . . . . . . . . . . . . . . . 17

2.6.4 Home applications . . . . . . . . . . . . . . . . . . . . . . . 18

2.6.5 Traffic control . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3 Overview of Routing Techniques and Related Work 21

3.1 Overview of Routing Techniques . . . . . . . . . . . . . . . . . . . 21

x

3.1.1 Location Based Routing Protocols . . . . . . . . . . . . . . . 22

3.1.2 Flat and Data Centric Routing Protocols . . . . . . . . . . . 22

3.1.3 Hierarchical Routing Protocols . . . . . . . . . . . . . . . . 23

3.2 Existing Clustering Protocols for WSNs . . . . . . . . . . . . . . . . 23

3.2.1 LEACH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2.2 Multi-Hop LEACH . . . . . . . . . . . . . . . . . . . . . . . 24

3.2.3 CEEC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

3.2.4 Cluster Head Selection in Wireless Sensor Networks under

Fuzzy Environment . . . . . . . . . . . . . . . . . . . . . . . 27

4 Proposed Clustering Protocol 30

4.1 Energy Model for Wireless Sensor Node . . . . . . . . . . . . . . . . 30

4.2 Proposed Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.2.1 Neighbor discovery . . . . . . . . . . . . . . . . . . . . . . . 32

4.2.2 Cluster Head Selection . . . . . . . . . . . . . . . . . . . . . 32

4.2.3 Sensor Nodes Communication . . . . . . . . . . . . . . . . . 36

5 Simulation and Results 41

5.1 Simulation Environment . . . . . . . . . . . . . . . . . . . . . . . . 41

5.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

5.2.1 Network Lifetime . . . . . . . . . . . . . . . . . . . . . . . . 42

5.2.2 Network Stability . . . . . . . . . . . . . . . . . . . . . . . . 43

5.2.3 Energy Consumption . . . . . . . . . . . . . . . . . . . . . . 43

5.2.4 Number of CHs per round . . . . . . . . . . . . . . . . . . . 44

5.2.5 Control Overhead (Hello) Packets . . . . . . . . . . . . . . . 44

5.2.6 Packets Sent to Base Station . . . . . . . . . . . . . . . . . . 45

6 Conclusion 48

References 48

xi

List of Figures

2.1 Crossbow Sensor Node . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Basic Architecture of WSNs [35] . . . . . . . . . . . . . . . . . . . . 10

2.3 Architecture of WSN node [36] . . . . . . . . . . . . . . . . . . . . 13

2.4 Architecture of WSN node . . . . . . . . . . . . . . . . . . . . . . . 16

2.5 Architecture of WSN node . . . . . . . . . . . . . . . . . . . . . . . 17

2.6 Environmental Application of WSNs [37] . . . . . . . . . . . . . . . 18

2.7 Health-care Applications [37] . . . . . . . . . . . . . . . . . . . . . . 18

3.1 Network Layout of LEACH . . . . . . . . . . . . . . . . . . . . . . 25

3.2 Network Layout of Multi-hop LEACH . . . . . . . . . . . . . . . . 26

3.3 Network Layout of CEEC . . . . . . . . . . . . . . . . . . . . . . . 27

4.1 Radio Model [5] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.2 Neighbor Discovery Process . . . . . . . . . . . . . . . . . . . . . . 33

4.3 CH Selection Process . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.4 Communication Model . . . . . . . . . . . . . . . . . . . . . . . . . 38

4.5 Flow chart of Proposed Protocol . . . . . . . . . . . . . . . . . . . . 39

5.1 Network Stability and Lifetime . . . . . . . . . . . . . . . . . . . . 42

5.2 Network Stability and Lifetime . . . . . . . . . . . . . . . . . . . . 43

5.3 Energy consumption per round . . . . . . . . . . . . . . . . . . . . 44

5.4 Total No. of Cluster heads per round . . . . . . . . . . . . . . . . . 45

5.5 Number of hello packets . . . . . . . . . . . . . . . . . . . . . . . . 46

5.6 Number of packets to base station . . . . . . . . . . . . . . . . . . . 46

xii

List of Tables

4.1 Radio Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . 30

5.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 41

xiii

Chapter 1

Introduction

1

Chapter 1

Introduction

In recent years WSNs have got interest due to the development in micro-electronics.

Applications of WSNs include atmospheric sensing, like temperature, pressure,

and humidity etc, natural disasters, like earthquake monitoring and military ap-

plications like battle field monitoring [1]. WSNs consist of tiny battery operated

electronic devices called sensor nodes. Besides sensing, these nodes have memory,

processor and possess communication capabilities. Sensor nodes are deployed in

the field either strategically or randomly. In case of random deployment these

sensor nodes have to be self organized in ad-hoc fashion. Replacement or recharge

of battery is not possible once sensor nodes are dispersed in field [2][3].

Network lifetime and stability of WSNs depend on energy consumption of sensor

nodes. Lesser the energy consumption, longer will be the network lifetime. Sensor

nodes consume energy in processing and communication with BS or other sensor

nodes. For prolonging network lifetime and stability, WSNs should be energy

efficient. Energy efficiency can be achieved by different means, like intelligently

designing of MAC and routing protocols. Routing protocols can be flat, Minimum

Transmission Energy (MTE) or hierarchical protocols. In flat routing protocols

sensor nodes directly send data to BS. In MTE each sensor node sends data to its

neighbor node, therefore load at sensor nodes near to base station is much greater

than other sensor nodes, resulting in shorter lifetime [4]. In hierarchical protocols,

whole network is divided in small number of blocks, called clusters. Each cluster

consists of multiple numbers of sensor nodes. Inside each cluster a CH is selected

to perform management and routing tasks for that cluster [5].

Flat and MTE transmission protocols perform better in small networks. If network

size is large, then these types of protocols are not efficient. To overcome this

deficiency, hierarchical or clustering protocols are designed for large networks [5].

2

In recent years researchers are focusing on design of energy efficient hierarchical

protocols. Suitable CH selection is a major constituent of hierarchal protocols. CH

performs a major role in network stability and lifetime. Many routing protocols

are designed in this regard. Most of the previous designed clustering protocols

consider single criterion for CH selection [6] or CH is selected randomly based on

probabilistic model [5]. Single criterion includes residual energy, distance from BS

or density of sensor node etc. Considering only one criterion may not be sufficient

in many specific cases, for example in a clustering protocol which considers residual

energy as CH selection parameter, worst case arises when a sensor node with high

residual energy located far away from BS, selected as CH. In this case this selected

CH require large amount of energy to forward data to BS, resulting in shorter

network lifetime. Similar kind of situation arises when considering distance form

BS as a CH selection parameter and a sensor node near to BS selected as CH having

very small amount of residual energy. Hence by analyzing these bottlenecks it is

observed that single criterion is not sufficient enough to prolong network stability

and lifetime.

In this thesis we design a distributed multi-hop energy efficient clustering proto-

col based on four criteria, including residual energy, number of neighbor sensor

nodes, distance form BS and average distance between neighbor sensor nodes.

Proposed protocol is based on modified VIKOR model [7]. VIKOR is an abbrevi-

ation in bosnian language for a technique called VIeKriterijumska Optimizacija I

Kompromisno Resenje, whose English meaning is Multicriteria optimization and

compromise solution. The modified VIKOR method was developed for multi-

criteria optimization of complex systems. The compromise solution, compromise

ranking-list and weight stability intervals is determined by this method for prefer-

ence consistency of the compromise solution obtained with the given weights. The

main aim of this method is to focus on ranking and selecting one alternative from

a set of alternatives, if there is some contradiction between different alternatives.

It introduces the multi-criteria ranking index based on the specific measure of

closeness to the ideal solution [9].

1.1 Motivation

Earlier research in WSNs clustering, including LEACH and M-LEACH are based

on some random value or single criteria for CH selection [5][8]. In most of the

previously proposed protocols for example CEEC, Centralized algorithm is used, in

which BS elects the CHs [6]. Also in previous schemes, cluster heads are changing

3

in every round. The goal of the thesis is to use Multi-criteria to select CHs which

is better than single criteria in terms of performance. Centralized schemes and

frequent CH change increase processing overhead. Hence previous schemes are

not much energy efficient. The VIKOR method is a popular method applied in

multi-criteria analysis. Although it is a famous method but in some particular

situations error would occur in calculation, for example when a criterion has same

value for all sensor nodes. To overcome the calculations errors occurring in VIKOR

method, modified VIKOR is introduced in [7]. In our proposed scheme we will

use modified VIKOR method for the selection of cluster head.

To improve communication model, we introduce two level hierarchal communica-

tion. In first level CH collects data from the sensor nodes. In second level, CHs

either sends that data directly to BS or transmit to the nearby CH. The decision

of transmitting data either to BS or other CH is taken on the basis of distance

between CH and BS.

1.2 Problem Statement

In most of the existing clustering techniques CH is selected randomly and based

on single criterion. They use centralized scheme, means BS performs CH selection

process. Also they use single hop communication model. In previous protocols CH

is changing in every round. The bottlenecks of previous protocols are discussed

below.

• Problems with single criterion:

– Residual energy is considered mostly

– Other information, like sensor nodes density, sensor nodes location etc,

are not considered

– So optimal CH is not selected.

• Problems with centralize algorithm:

– Increased CH selection time

– Increased control overhead

– Increased processing overhead, maximize energy consumption

• Problems with single hop communication:

4

– CHs that are far away from BS consumes more energy in communication

with BS

– Shorter network lifetime and stability period

• Problems with frequent CH change:

– increased processing over head. Results in shorter lifetime of sensor

nodes.

In this thesis we improve the deficiencies in existing clustering protocols. We de-

sign and simulate a new clustering protocol which is based on four criteria, includ-

ing residual energy of sensor nodes, number of neighbors of sensor nodes, distance

form BS and average distance of nodes form their neighbors. For this purpose we

use modified VIKOR model for CH selection. In order to reduce control overhead,

we avoid quick deformation of CHs. Reduction in control overhead minimizes en-

ergy consumption due to which network life increases and stability improves. In

our proposed scheme we use realistic communication model by introducing multi-

hop communication model in inter-cluster communication mechanism.

1.3 Research Methodology

In this work we comprehensively analyse the bottlenecks in existing clustering pro-

tocols, and design a new clustering scheme based on multiple criteria. Different

weights are assigned to each criteria according to their importance. Each node

share all four criteria value with all its neighbors, then rank index value is calcu-

lated for each node. The node with highest rank, elect itself as CH. The elected

CHs remain CHs until their rank index value differ from any other node value

by 0.1. (threshold value). In intra cluster, nodes with 5m range of CH directly

communicate to CH, other nodes perform multi-hoping with neighbor nodes. Sim-

ilarly, in inter cluster CHs within 20m range of BS directly communicate with BS,

remaining CHs perform multi-hoping with neighbor CHs.

1.4 Thesis Organization

Remaining thesis is organized as follows:

Evaluation of WSNs is presented in Chapter 2, including history of WSNs, detailed

introduction of WSNs, basic WSN architecture and common applications. This

chapter gives basic overview of field.

5

Chapter 3 discusses overview of general routing techniques, types of routing pro-

tocols. Related work about our proposed protocol is also presented in this chapter.

At the end we discuss some well known routing protocols in the field of WSNs.

In Chapter 4, our propose protocol is discussed in detail. We describe all phase

along with mathematical formulae and equations. Detailed flow charts of proposed

protocol are also given in this chapter.

Chapter 5 discusses simulation parameters and simulation results of our proposed

scheme. In this chapter we compare the results of proposed scheme with previous

clustering techniques, and analyze the improvements in our proposed scheme in

terms of network lifetime and stability.

Chapter 6 concludes the manuscript of our thesis report.

6

Chapter 2

Evaluation of WSNs

7

Chapter 2

Evaluation of WSNs

2.1 Brief History of WSNs

Military is the major reasons for the development of the WSNs. It is started in

1978, when Defense Advance Research Projects Agency (DARPA) organized the

distributed sensor nets workshop [10]. The major focus in that workshop was to

cop the research challenges in sensor networks, including network technologies,

signal processing techniques and distributed algorithms. Then in 1980s DARPA

started Distributed Sensors Networks program (DSN). This was then followed by

Sensor Information Technology (SensIT) program. The concept of Wireless In-

tegrated Network Sensors (WINS) [11] started by University of California at Los

Angeles with the collaboration of Rockwell Science Center. In 1996 WINS pro-

duced a smart sensing system which was integrated with multiple sensor nodes,

interface circuits, DSPs, wireless radio and micro-controller on a single CMOS

based chip. Another project which focused on the design on extremely small sen-

sor nodes called motes. This project was design by University of California and

knows as Smart Dust project [12]. The main purpose of this project was to show

that integrated sensor devices can be as small as dust particles. Energy con-

sumption minimization was the main focus of the PicoRadio [13] project, which

was developed Berkeley Wireless Research Center (BWRC). Which produce such

low-power sensing devices with very small energy consumption that the can be

power from environmental energy sources for example vibrational or solar energy.

The micro-Adaptive Multi-domain Power-aware Sensors (AMPS) project, devel-

oped by MIT, also focused on low power software and hardware components for

sensor nodes. This included micro-controllers capable of dynamic voltage scaling

and some techniques for redesign of data processing algorithms to reduce energy

consumptions requirements at software level [14]. Most of the previous research in

8

the field of wireless sensor nodes was conducted by educational institutes, however

in last few years a number of commercial efforts have also appeared. The main

contributors of this commercial research are Worldsens [15], Crossbow [16], Senso-

ria [17], Dust Networks [18] and Ember Corporation [19]. Crossbow sensor node

is shown in fig. 2.1.Varieties of sensor devices produced by these companies are

available to be purchased and deployed in different application scenarios. Differ-

ent tools for programming, management maintenance, Sensors data visualization

and analysis are also provided along with the sensor node nodes devices by the

companies.

Figure 2.1: Crossbow Sensor Node

2.2 Introduction of WSNs

Wireless sensor networks is a growing technology in recent years which has a large

number of application, including environmental sensing, industrial sensing and

health care diagnostic etc. The advancements of these areas help the researchers

to enhance the performance of WSNs. Large number of sensor nodes, deployed

in a certain are for a specific purpose, bring themselves together to form a WSN.

WSNs are usually deployed in an environment to monitor static or dynamic events.

The measurement of static events (such as temperature, humidity etc) is very easy

to carry out. On the other hand, dynamic events are typically non-cooperative

event is the movement of an unwanted vehicle in a battle field and the movement

of whales in the ocean. They are not easy to monitor and they are not stable

as they go up and down. Therefore, it is highly difficult to study energy saving

schemes for sensing of dynamic event. For example, a forest monitoring appli-

cation involves static monitoring approach whereas a target tracking application

involves a dynamic monitoring approach [20]. After sensing the monitored data

these sensor nodes send the data to a central controlling device. Besides, some

of the sensor nodes are directly connected to controlling devises, in most of the

situations sensor nodes are connected remotely and tirelessly to centralized pro-

cessing stations. Since in most of the applications sensor nodes are deployed in

9

remote locations without human interference, they are usually inaccessible after

deployment. Hence, a wireless sensor node not only sense data but must also be

capable of processing of data and communication with coordinator. So it must

have processing, communication and storage components. Sensor node not only

sense its own data but also collects and aggregates data from other sensor nodes

in the network, therefore sensor nodes not only communicate with the BS but also

with other sensor nodes in there communication range, which allows the sensor

nodes to cover large geographical area. Figure 2.2 shows the basic architecture of

WSNs.

Figure 2.2: Basic Architecture of WSNs [35]

The sensing, processing and communication require a specific amount of energy

which is provided by limited power supply unit of sensor. Since power supply unit

provide small amount of power as they are mostly equipped with AA or AAA

battery. Hence efficient power utilization in sensor nodes is very difficult because

sensor once deployed in a field cannot be recharged. Therefore energy is the major

issue in WSN. Therefore different techniques are used for efficient utilization of

energy to increase the lifetime of sensor and WSN.

2.3 Challenges in WSNs

Due to small size of sensor nodes, types of communication channel, deployment

scenarios and environmental conditions, WSNs have many constrains. These con-

straints impact the design of a WSN, leading to protocols and algorithms that

10

differ from their counterparts, like ad-hoc networks, in other distributed systems.

This section describes the most important challenges in a WSNs.

2.3.1 Energy constrained

As illustrated above, the energy is a major concern in WSNs, because they are

powered through batteries, which must be either replaced or re-energized (e.g.,

using solar power) when exhausted. For most of WSNs applications, none of the

options is suitable as they will simply be superfluous, once their energy source is

washed-out because sensor nodes are deployed in areas which are not accessible.

Battery recharge is notably a matter of concern as the strategy applied to energy

consumption is affected. In case non rechargeable batteries are employed, a sensor

node should be able to maneuver till its mission time is over or the battery is

replaced. The life of the mission depends on the type of application, as scientists

monitoring significant movements may require sensor nodes that have a life span

of several years whereas a sensor in a battlefield scenario may only be needed for

a few hours or days. As a consequence, the first and often most important design

challenge for a WSN is energy efficiency.

2.3.2 Network Deployment

Predetermined deployment of sensor is not required in several WSNs applications.

Networks deployed in remote or inaccessible areas like, sensor nodes serving the

evaluation of battlefield or disaster areas could be thrown from airplanes in the re-

quired places. While some of the sensor nodes might not carry on because of being

dropped from height might get damaged, so they may not be able to start their

sensing activities. On the contrary, surviving sensor nodes will autonomously start

functioning a variety of setup and configuration steps, including the establishment

of link with neighboring sensor nodes, locating their positions, and initialization

of their sensing responsibilities [21]. There can be difference in mode of operation

of sensor nodes based on information such as, a sensor nodes location and the

number of identities of its neighbors. All this may determine the amount and type

of information it will produce and forward on behalf of other sensor nodes.

2.3.3 Self-Management

In most of situations, inaccessible places that are out of human reach require the

deployment of wireless sensor nodes, hence these sensor nodes cannot be repaired

11

or managed once deployed, as in battlefield. Support and maintenance is not

possible in such scenario. So, sensor nodes should have the ability of self-managing

so that they can configure themselves, operate and cooperate with each other, and

are adaptable to environmental changes [22], can withstand topological changes

and failures without any human interaction [23].

2.3.4 Wireless Networking and Communication Channel

Sensor network designer faces number of challenges due to the dependence on

wireless networks and communications. As the power of radio frequency signals

weakens while it propagates through the hindrance filled medium. As a fact,

relationship between the power consumption and the distance between BS and

sensor nodes has nonlinear traits. So, for efficient transmission it is better to

divide longer distances into several shorter distances that results in providing

better strategy in handling the challenge of supporting multi-hop communications

and routing.

2.3.5 Hardware Limitations

Creating smaller, low cost and more efficient devices is the main purpose of sensor

design. It is needed to execute a dedicated application with more efficient energy

consumption by the sensor nodes. Therefore typical sensor nodes have relatively

low processing speeds and less storage capacities. Integration of many desirable

components like GPS receivers is excluded just because of the need for small

form factor and efficient resource utilization. The design of many protocols and

algorithms executed in WSNs is affected by such limitations of hardware. For

example routing tables with entries of each potential destination may be too large

for small memory of a sensor. So, sensor nodes memory can store only a small

amount of data, such as a list of neighbors. Limited hardware resources compel

to design algorithms and protocols in such a way that they operate efficiently.

2.4 Difference between Traditional Networks and

WSNs

It becomes clear from the previous section that many design choices in a WSN

differ from the design choices of other systems and networks. Some of the main

difference between traditional networks and WSNs are discussed in this section.

12

Traditional networks are general purpose networks, which serve many applica-

tions. Whereas WSNs are single purpose networks and serving one application,

like WSNs are used to sense any environmental condition.

In traditional networks the primary concern is network performance and latencies.

However in WSNs, main constrains is energy. Sensor nodes are designed and

deployed in such manner that it consumes minimum amount of energy.

Traditional networks are mostly designed and engineered according to plans. Whereas

WSNs are mostly deployed in ad-hoc fashion i-e without any proper planning.

In traditional networks devices and networks are operated in controlled and mild

environment, whereas WSNs are usually operated in harsh conditions.

Maintenance and repair of traditional networks is much easier than WSNs, because

in WSNs physical access to sensor nodes is very difficult or even impossible. Hence

component failure in traditional networks is addressed through maintenance and

repair, where as in WSNs it is addressed in design of network.

In traditional networks, it is possible to have centralized management, and it

is possible to obtain global information about network, whereas in WSNs most

decisions are made localized without support of central management.

2.5 Sensor Node architecture

A typical wireless network consists of a small battery powered device called sen-

sor nodes. The four basic components of sensor nodes are Sensing subsystem,

power subsystem, communication subsystem and processing subsystem [24]. Fig-

ure 2.3 given below explains the single node architecture. The explanation of each

subsystem is given as follows:

Figure 2.3: Architecture of WSN node [36]

13

2.5.1 Sensing Subsystem

Sensors are the components that are responsible for creating a link between com-

putational environment and the real world. Therefore, sensors are considered a

crucial component of WSNs sensor node architecture. Sensors are small hardware

devices that are capable of reading data of a certain type and reacting to a certain

changes in the environment in which they are designed to work. The sensor reads

the data in a particular environment, and then sends this data to be processed

further. Transducers are used in the sensors subsystem to read this signal. The

data is sent in the form of analog signals, which is converted into digital form

using Analog to digital convertors (ADC) [25]. A sensor node consists of ADC

and micro-controllers for processing. Each sensor is designed separately according

to the application for which it is used. An important factor of consideration when

choosing a sensor node is the consumption and size of the sensor node battery.

2.5.2 Processing Subsystem

Along with a memory unit and convertors, another important component of sensor

node subsystem is the processing unit. The data is processed by the communica-

tion interface. This data is used to study the networks performance. The purpose

of this unit here in a WSN architecture is to adapt the routing information and it

can also help in aligning the topology [26]. Data gathering, data acquisition and

processing of the retrieved data are also responsibilities of this subsystem. Data

fusion also occurs in this subsystem as data packets arrive from different sensor

nodes are all combined to make a single data packet. This subsystem reduces the

energy used for transmission between the user and sensor.

2.5.3 Communication Subsystem

The transmission of data is the responsibility of this subsystem. The signals are

carried from sensors to end users through BS and sensor nodes use radio frequen-

cies for this purpose. BS here is responsible for communication between user and

WSNs. The number of BS can vary for each network depending on the area,

number of sensors nodes and requirements of the network. In any network, each

sensor node can coordinate and communicate with all the other sensor nodes.

The two types of communications between sensor nodes are infrastructure and

application. Communication for building, maintaining and optimizing a network

is called infrastructure communications. Even a static sensor node network needs

14

infrastructure communication, as due to some environmental changes such situa-

tions can occur when sensor nodes fail and topologies vary. In such a situation,

the topology needs reconfiguration for which communication infrastructure is re-

quired. The data gathered by the sensor nodes, which is sent to the monitoring

end for further processing is called application communications. The amount of

energy required for a single packet to transmit a packet is fixed and it depends on

the distance. For longer distance, the energy required will be higher. Therefore,

to reduce the energy required, shortest path is selected for transmission of packets.

This communication is called application-based communication [27].

2.5.4 Power Subsystem

The power subsystem is composed batteries. All of the above subsystems need

power to perform their activities and tasks. The power consume by these sub-

system is restricted and carefully managed by power subsystem because of energy

constrains in sensor nodes. The power subsystems are normally equipped with

AA batteries or AAA batteries. This subsystem also contains power regulator. In

most of the cases the power consumption is minimized by turning the sensor in

sleep and awake mode. Power subsystem performs the task of the turning mode

of sensor nodes.

2.6 Application of WSNs

WSNs may consist of a several number of sensor nodes and these are application

specific. The various type of sensors include infrared, thermal, acoustic infrared,

seismic, radar and visual. Due to these various types of sensors WSNs can be used

for various applications such as monitoring of physical and atmospheric conditions

like temperature, humidity, pressure, noise level and movement. It can also be used

to measure characteristic like size and speed of object. The implementation of

wireless and communication and micro-sensing combines gives life to a whole new

line of applications. These applications can be termed into two main categories

[28] and four further sub-categories shown in fig.2.4.

The two main categories of applications of WSNs are Tracking applications and

monitoring applications. Tracking applications include tracking of objects, hu-

mans and animals. While the monitoring applications include monitoring of envi-

ronment, power, health, etc.

15

There are some other commercial and non-commercial applications as well, which

are discussed here in detail.

Figure 2.4: Architecture of WSN node

2.6.1 Military Application

Sensor networks have a very wide scope when it comes to military applications

due to their self-organization, ease of deployment and fault tolerance. The wire-

less networks are used in many areas such as military command, intelligence,

communications, computing, surveillance, targeting and control system. Military

applications of wireless networks are far spread. These networks can be used to

gather intelligence and valuable information about the movement and strategy of

the enemy in a battlefield. It can also be used in surveillance and spying. More-

over, it can also be used to detect biological and nuclear attacks in a war. For

instance, a sensor based system is developed that helps to locate the location of a

hidden sniper in an area [29]. The sensor network acquires information from the

muzzle blast and shot waves to identify the location of a shooter.

2.6.2 Environmental Application

A lot of environmental applications have been developed using wireless networks.

WSN can be used to track and detect various environmental conditions such as

environmental conditions that can affect the crop and livestock. Moreover, it is

also used to track animals. WSN are capable of collecting data in a large area over

a certain amount of time. There are other more important biological and chem-

ical applications of WSNs that can help the forestry and agricultural systems.It

can be used for biological and chemical precision detection, fire detection in fire,

16

Figure 2.5: Architecture of WSN node

detecting volcano eruption and flood detection. Microscope of Redwood [30] is a

known example of environmental application of WSNs. Here, the wireless sensor

network records and monitors trees in an area in California. The sensor nodes are

used in this system to collect information such as, photo synthetically-active solar

radiation, humidity and temperature in air. These sensors are placed at different

positions on the tree at different heights. Biologists study this collected data and

read the environmental changes to prove their theories. Similarly, another envi-

ronmental application of WSNs is a system that is used to track the migration of

animals called ZebraNet system [31].

2.6.3 Health-Care Applications

With an aging population and increasing health care cost, there is an immense

need of cost effective health-care solutions to solve the challenges faced by humans.

For this purpose, wireless sensor networks have been programmed and put used

to a number of health-care applications. Body Area sensor network (BAN) is

an application of WSNs and it is used to collect physiological data from a human

body. This is used for monitoring of disables and it is used in developing interfaces

for disables as well. Another application of body sensor network is to detect the

movement of elderly people and see if they have fallen. It helps the patients to

move with freedom, while also allowing the doctor to be aware of the problem

as soon as it occurs, and sometimes even before it by reading the symptoms.

Similarly, you can also provide sensitive patients with a small sensor node, which

17

Figure 2.6: Environmental Application of WSNs [37]

will keep track of their heart-rate and blood pressure.

Figure 2.7: Health-care Applications [37]

2.6.4 Home applications

With the advance of technology, the tiny sensor nodes can be embedded into fur-

niture and appliances, such as vacuum. The sensor nodes are tiny devices that

can be used to collect tons of different type of data. These devices can be at-

tached to or placed inside your daily usage appliances such as vacuum cleaner,

refrigerators and microwave ovens. These sensors allow these devices to commu-

nicate in a room server. Using this data, you can program these devices to be

18

self-organized and self-regulated and adapted. It will help you to turn your home

server environment into a smart computing environment. ZigBee is an example of

such applications. In these application, WSNs are used to form a Personal Area

Network which is capable of monitoring and controlling of several functions such

as electricity switching control, safety control, surveillance and monitoring.

2.6.5 Traffic control

Ground transportation plays a key role in socio-economic infrastructure. It pro-

vides a link between many systems such as emergency response, supply-chain and

public health. Due to increase traffic, congestion, which cause billion gallons of

fuel and billion hours per annum. This can be avoided by building new roads,

however this solution is not feasible because of lack of free space and high cost.

Another solution is to place distributed systems which reduce the congestions.

One the most suitable distributed system for traffic control is WSN. The sensor

nodes in WSNs gather information about size, speed and densities of vehicles. If

detect congestion from these information and suggest alternative and emergency

exits.

19

Chapter 3

Literature Review and

Related Work

20

Chapter 3

Overview of Routing Techniques

and Related Work

3.1 Overview of Routing Techniques

Limited bandwidth and energy supply in sensors creates many challenges in WSNs.

To overcome these challenges, energy aware protocols are required at all levels of

protocol stack. To offer efficient power management in WSN, researchers have

been focus on areas such as system-level power awareness like radio communi-

cation hardware, low duty cycle issues and energy-aware MAC protocols. Also,

it was observed that the network layer offers a better means through which reli-

able relaying of data and energy-efficient route setup within a network can help

to maximize the network lifetime. It should be noted that routing in WSN has

much distinguishable features compare to contemporary communication and ad

hoc networks [20]. These features are as follows:

I. WSN cannot be built with global addressing such as Internet scheme Protocol

(IP) for the enormous number of sensor nodes as the maintenance of ID overhead

is high;

II. There is significant redundancy in generated data because several sensors may

gather the same data within a particular field. These redundancy needs to be

removed to increase the bandwidth utilization and also reduce energy consumption

in the network.

III. Transmission power, processing capacity and storage are constraint factors to

be considered when managing a WSN.

Due to these differences, new protocols are being researched and fashioned to

21

eliminate the problem faced in WSN. These routing protocols have been fashion

on sensor nodes characteristics alongside it application and architectural require-

ment. The various protocols can be classified as location-based, data-centric or

hierarchical.

3.1.1 Location Based Routing Protocols

Most routing technique for WSN depends on location information of sensor nodes

for estimation of distance between two specific nodes to deduce energy consump-

tion. For example, to sense a known region, through the use of location sensor,

a specified query can be sent to that known region and this will significantly re-

duce transmitted data compare to a broadcast request being sent to the entire

network [20]. In other words, the location-based protocol utilizes the position in-

formation to relay the data to the desired regions rather than the whole network.

An example of location based routing protocol is, Geographic Adaptive Fidelity

(GAF) algorithm. GAF is mainly designed for energy conservation. Here, the

sensor network is divided into grids and each sensor is equipped with GPS, for its

location information in a particular grid. There is a switching between the states

which means that the sensors which are not active are turned off maintaining the

constant routing fidelity simultaneously.

3.1.2 Flat and Data Centric Routing Protocols

In most sensor networks, the sensor nodes themselves are less important than the

information they generate. Therefore, in data-centric routing techniques, the fo-

cus is on the retrieval and dissemination of information of a particular type or

described by certain attributes, as opposed to the data collection from particular

sensor nodes. Since assigning global identifiers to every sensor nodes in a WSN

may appear not visible (due to sheer number) in some randomly deployed ap-

plication, data transmitted by every sensor node within a particular region has

significant redundancy with it. To reduce this redundancy, data centric protocols

were developed to select a set of sensor nodes and also utilize data aggregation

during relaying of data. An example of data centric is Flooding routing protocol

in which each sensor node receives data and then sends them to the neighbors by

broadcasting, unless a maximum number of hops for the packet are reached or

the destination of packet is achieved. Another type of Flat based routing protocol

is Sensor Protocol for Information via Negotiation (SPIN) [32]. SPINs data are

named using meta-data that highly describes the characteristics of the data which

22

is the key feature of SPIN.

The advantage of SPIN is that the topological changes are localized since each

of the sensor nodes needs to know only its single-hop neighbors. However, it

has a disadvantages of scalability (not scalable) and also, the nodes around the

base station could deplete their energy if the BS is interested in too many event.

Moreover, SPINs data advertisement mechanism cannot guarantee the delivery of

data. For instance, if the sensor nodes which are interested in the data are far

away from the source node and the nodes between source and destination are not

interested in that data, such a data will not be transmitted to the destination at

all.

3.1.3 Hierarchical Routing Protocols

Hierarchical routing protocols are based on the grouping of nodes into clusters

to address some weaknesses of at routing protocols, most notably scalability and

efciency. The main idea behind hierarchical routing is that sensor nodes commu-

nicate only directly with a leader node in their own cluster, typically referred to as

CH. These CHs, which may be more powerful and less energy-constrained devices

than regular sensor nodes, are then responsible for propagating the sensor data

to the sink. This approach can signicantly reduce the communication and energy

burdens on sensor nodes, while CH will experience signicantly more trafc than

regular sensor nodes.

3.2 Existing Clustering Protocols for WSNs

Energy consumption is the major issue in wireless sensors network. In order to

balance the energy consumption and prolong the lifetime of the WSNs many asym-

metrical clustering algorithms have been proposed for WSNs. Here we mention

some of the most relevant clustering algorithms.

3.2.1 LEACH

Low-Energy Adaptive Hierarchy (LEACH) [5] is very popular clustering protocol

designed in year 2000. In LEACH protocol the network is divided into clusters.

In each cluster one sensor node as act as CH and the remaining sensor nodes act

as member nodes. The CH in a cluster is responsible to collect, aggregate, and

forward the data from member to BS. The member nodes cannot communicate

23

directly with the BS, as CH act as router between member nodes and BS. Because

of extra responsibilities, CH consumes more energy than the member nodes and

can die early, if it remains permanent as a CH for the entire life time of network.

This issue is resolved by using randomized rotation of CH positions among nodes

in network. Hence load of energy consumption is distributed among all nodes. The

overall process of LEACH protocol is divided in rounds. Each round in LEACH

has two phases; setup phase and steady state phase. In setup phase all sensors

nodes organize themselves into clusters, where as in steady state phase normal

nodes send data to their respective CHs. Each node elects itself as CH in the

setup phase by generating a random value between (0-1). If the random is less

than a threshold T given in equation 3.1 for a sensor node it elected itself as CH

and broadcast an advertisement packet to sensor nodes in its range, otherwise

sensor node waits for an advertisement packet from other sensor node which is

elected as CH [5].

T (n) =

{

p1−p(r∗mod(1/p))

if n ǫ G

0 otherwise(3.1)

Where G is set of nodes that have not been cluster-head in previous 1/p rounds,

P suggested percentage of cluster-head, r is current round.

For proper distribution of CH responsibilities, sensor nodes which are elected as

CHs in last 1/P are not eligible for CH election process. Sensors nodes, which

are selected as CHs before 1/P rounds are eligible for CH election process. The

uniform service of each node as a CH prevents the uneven energy consumption.

The CSMA/CA protocol is used by the CH to broadcast its status. The non-

cluster nodes select their CH on basis of Received Signal Strength Indication

(RSSI). Time Division Multiple Access (TDMA) slots are assigned by each CH to

its member nodes. In steady state phase member nodes communicates with the

CH in their associated TDMA slot. The CH collects the data from its member

nodes, aggregates and compresses the collected data and transmits it directly to

the BS. Network layout of LEACH protocol is shown if fig. 3.1.

3.2.2 Multi-Hop LEACH

It is difficult for all CHs to communicate directly with the base station when the

sensor nodes deployed area is very large. High transmission power is required to

send the data from CHs to the base station, if the base station is far away from

the CHs. LEACH only assumes that all CHs are at single hop distance from the

24

0 10 20 30 40 50 60 70 80 90 1000

10

20

30

40

50

60

70

80

90

100

Figure 3.1: Network Layout of LEACH

base station, which is not a suitable approach for large geographical area. An

approach, Multi-Hop LEACH, addresses this issue [8]. Multi-Hop LEACH is an

enhancement of LEACH, which reduces the energy consumption of the CHs in

large WSNs. Like LEACH protocol, the formation of clusters and selection of the

CHs is done in the setup phase. However, During steady state phase, the non-

cluster head nodes send their data to the CHs. The CHs aggregate and transmit

that data towards through other CHs.

The most feasible and energy efficient path is selected for that CH which is far

from the base station. The method used for the selection of intermediate CH is the

distance between the CH and the base station. The CH closer to the base station

receives the data from the other CH which is far from base station. This helps to

save energy of those CHs which belong to the clusters with larger distance from the

base station as higher transmission energy cost is required for communication with

larger distances. Figure 3.2 represents the network layout of multi-hop LEACH.

3.2.3 CEEC

Centralized Energy Efficient Clustering (CEEC) [6] routing protocol is centralized

algorithm for cluster-head selection and its performance is much better in het-

erogeneous environment as compared to homogeneous environment. An advance

heterogeneous network model is proposed for CEEC, in which sensor nodes with

different energy level are deployed in separate regions. In CEEC, BS performs

25

0 20 40 60 80 100 120 140 160 180 2000

50

100

150

200

250

Figure 3.2: Network Layout of Multi-hop LEACH

central clustering formation in network, with help of central control algorithm of

CEEC. Advance central control algorithm considers four factors for selection of

cluster-heads, initial energy of sensor nodes, residual energy of sensor nodes, and

average energy of each region and location of sensor nodes. Operation of CEEC

is based on rounds, with adjustable duration. Each round is divided into Network

Settling Time (NST) and Network Transmission Time (NTT).

During Network Settling Time (NST) suitable cluster heads are selected by BS,

with the help of central control algorithm. In central control algorithm, BS calcu-

lates three different average energies for normal, advance and super sensor nodes

to obtain separate cluster-heads for all regions. BS knows the initial energy of all

sensor nodes for the first round and it can simply calculate the average energies

for first round. After first round, sensor nodes provide their residual energy infor-

mation to BS. Another significance of the protocol is that sensor nodes provide

their residual energy information along data packets transmitted in NTT. In this

way centralized algorithm calculate weight-age of nodes for selection of CH. Best

selected CHs in CEEC are called First Selected Cluster Heads (FSCHs).

During Network Transmission Time (NTT) real communication take place towards

the BS. In NTT all sensor nodes send their data to their Current Cluster Heads

(CCHs), in assigned time slots. Cluster-heads receive the data from its cluster

and aggregate the data. Figure 3.3 represents the network layout of multi-hop

LEACH.

26

0 50 100 150 200 250 3000

50

100

150

200

250

300

Figure 3.3: Network Layout of CEEC

3.2.4 Cluster Head Selection in Wireless Sensor Networks

under Fuzzy Environment

In [34] authors have proposed CH selection technique which much is based on

multiple criteria. In this technique, Fuzzy -TOPSIS system of MCDM, is used.

Three criteria, which are residual energy, number of neighbor and distance form

BS, are consider in this technique. In this paper centralized algorithm is used,

means BS is taking decision for CH selection on this basis of sensor node values

of all criteria. They assign different weights to each criterion. This technique uses

the concept of fuzzy membership function. Fuzzy membership functions are used

in fuzzy systems to assign relative importance to each criterion. CH selection is

divided into five steps. In step 1, values obtained from each sensor node about its

every neighbor is converted into normalize values. Note that there are some criteria

whose larger value is suitable for a sensor node to be selected as a CH e.g. residual

energy and number of sensor nodes. These criteria are called Positive criteria, and

are normalized using formula for Positive Ideal Solution (PIS). On the other hand,

the criteria with smaller values are appropriate for a sensor node to be selected

as a CH, e.g. distance from BS. This type of criteria are called Negative criteria,

and normalized by using formula for Negative Ideal Solution (NIS). In step 2,

respective weights are assigned to each criteria and fuzzy membership function

is determined. On this basis of weights and fuzzy membership function, weight

matrix is calculated. In step 3, PIS and NIS is calculated form weight matrix.

Then in step 4, separation measure is calculated also from weight matrix. Finally

27

in step 5, rank index is calculated. Then the sensor nodes with higher rank value

from their neighbor are elected as CHs, and other sensor nodes join these CHs.

Because of the centralized algorithm processing time and hello overhead increases,

as every sensor node has to send its information to BS in each round. Hello

overhead also increases because of CH changing in every round.

28

Chapter 4

Proposed Clustering Protocol

29

Chapter 4

Proposed Clustering Protocol

In this chapter we discuss our proposed protocol in detail along with mathematical

equations, explanations and flow diagrams. Next section describes the first order

radio model used in wireless sensors.

4.1 Energy Model for Wireless Sensor Node

First order radio model is assumed by mostly energy efficient routing protocols

as given in [5]. We also adopt this radio model to analyze realistically the pro-

posed protocol with other clustering protocols. Energy dissipation values indicate

the hardware energy consumptions during transmission, reception and aggrega-

tion of data. EeleTX and EeleRX denote the energy consumptions values to run

transmitter and receiver circuitry per bit. Radio dissipates ǫamp for transmission

amplifier in order to obtain suitable Eb/N0.

Energy values used in selection of suitable Eamp and Eafs are given in Table 4.1.

These values are extensively adopted in previous research works.

Table 4.1: Radio Characteristics

Parameter value

Data Aggregation Energy cost 50pj/bit jTransmitter Electronics EeleTx 50 nj/bitReceiver Electronics EeleRx 50 nj/bitTransmit amplifier Eamp 100 pj/bit/m4Transmit amplifier Eafs 100 pj/bit/m2

We are using radio model of sensor node is shown in Fig.4.1, used in [5]. Energy

dissipation of an individual sensor node depends upon the number of transmissions,

30

L bit packet Transmit

ElectronicsTx Amlifier

Receiver

Electronics

L bit packet

ETx(d)

EeleTX *L Eamp *L*d2

EeleRX *L

d

Figure 4.1: Radio Model [5]

number of receptions, amount of data to transmit or receive, and distance between

transmitter and receiver. But in most of cases, only transmission energy cost is

considered during performance analysis of protocol. Energy dissipation cost for

transmitting and receiving given in [5] is given in equation 4.1 and 4.2 respectively.

ETX(L, d) =

L× EeleTX + L×Eafsd2 If d < do

L× EeleTX + L×Eampd4 If d ≥ do

(4.1)

ERX = L× EeleRX (4.2)

ETX(L, d) is transmitting energy cost, ERX is receiving energy cost, L data bits

to be transmitted, Efs is free space transmission Model, Emp is Multi-path trans-

mission Model.

4.2 Proposed Scheme

In this section, we describe the details of our propose scheme for CH selection

using distributed algorithm. In WSNs CHs are selected using one or more criteria.

These criteria include residual energy, distance of node from BS and node density

31

etcetera. However considering all of these criteria is very difficult job because a

node might have high residual energy but its node density will be low, another

node might have low residual energy but might have high node density. Therefore

we adopt modified VIKOR [7] method, which is multi-criteria technique to select

a suitable CH. According to this method each node evaluates itself with respect

to its neighbors and decides to become CH or not. Four criteria to be consider by

a node for CH selection are residual energy, node density, distance from BS and

average distance from its neighbor nodes. Our proposed scheme consists of three

phases, which are described below in detail:

4.2.1 Neighbor discovery

Here we explain neighborhood discover technique of a node. Initially when nodes

are randomly deployed, they don’t have any information about their surroundings

and therefore cannot take part in CH selection process. In order to discover neigh-

bors, every node broadcast its ID in a HELLO message in its intra-communication

range and listens to medium for short period of time for similar messages. each

time when a sensor node receives this HELLO message, it creates and updates its

neighbor table by storing neighbor IDs from this HELLO message. The sensor

nodes in our proposed scheme are static, therefore no need of neighbor discovery

in every communication cycle. After completing neighbor discovery, sensor nodes

calculate average distance from its neighbor and also calculate its distance from

the BS. The sensor node then broadcast an information packet which contains in-

formation about sensor nodes energy, number of neighbors, average distance from

its neighbor and sensor nodes distance from BS. In our proposed scheme neighbor

discovery process is performed after few communication cycles in order to check

for dead neighbors and to update routing table accordingly. Performing neighbor

discovery process after few communication cycles decreases HELLO over head, re-

sulting in lower energy consumption. Overall neighbor discovery process is shown

in fig.4.2. In the next section, we present detail description of CH selection in our

proposed scheme.

4.2.2 Cluster Head Selection

Comprehensive explanation of CH selection process of our proposed scheme is

given in this section. Based on information packet received, sensor node calcu-

lates its CH value (CH V al) and share this (CH V al) with all of its neighbors.

Following are the steps involve in calculating (CH V al) of a sensor node.

32

Start Start

Round = 1Round = 1

Broadcast

Hello packet

Broadcast

Hello packet

If Round =

Neighboring

round

If Round =

Neighboring

round

Listen to the

medium for

Hello packets

Listen to the

medium for

Hello packets

Create and update

neighbor table

Create and update

neighbor table End End

NoYes

Yes

No

Figure 4.2: Neighbor Discovery Process

Step 1: Every sensor node compares its each criterion value ′V ′ with every neighbor

and determines the node with maximum value and the node with minimum value

for that criteria. We represent maximum value with Positive Ideal Candidate

(PIC), and minimum value with Negative Ideal Candidate (NIC). Formulae for

calculating PIC and NIC are taken form [7] and modified according to our model

are shown in equation 4.3 and 4.4 respectively.

PICkj(k = 1, 2, ..., N) = max[Vij|(j = 1, 2, ..., c)

i = 1, 2, ...(n + 1)] (4.3)

NICkj(k = 1, 2, ..., N) = min[Vij|(j = 1, 2, ..., c)

i = 1, 2, ...(n + 1)] (4.4)

Where k is the number of sensor node, N is the total number of sensor nodes,

i is number of neighbor (it includes the sensor node it self), j is the number of

criteria, n is the total number of neighbor, c is the total number of criteria and

max valk andmin valk are the maximum and minimum values of kth sensor node’s

33

jth neighbor.

Step 2: We assign weight ′w′ to all four criteria according to their importance.

Since residual energy is major criterion for selection of CH, so we assign 0.4 weight

to it and 0.2 to each other three criteria; i.e. number of neighbor sensor nodes,

distance from BS and average distance between sensor node and its neighbors.

Step 3: In this step each sensor node calculates the distance of each criterion (j)

to the ideal solution (MAX/MIN) and then computes sum of all the distances

to obtain the final value. For some criteria like, residual energy, greater values

provides better chances for a sensor node to become CH therefore its distance is

calculated to the positive ideal solution by equation 4.5 while criterion like distance

to BS low value provides better chance for a sensor node to become CH, so its

distance is calculated from negative ideal solution by equation 4.6. Equation 4.7 is

used to calculate regret measure. These equations are taken form [7] and modified

according to our model.

S∗

k =

c∑

j=1

[(PICj − Vkj)/(PICj −NICj)] (4.5)

S−

k =

c∑

j=1

[(Vkj −NICj)/(PICj −NICj)] (4.6)

Rk = maxj [wj ((PICj)/

(PICj −NICj))|j = 1, 2, .., c] (4.7)

Where Sk represents the distance of sensor node k to the ideal solution and Rk

shows the regret measure. After computing these Sk and Rk values, sensor node

broadcast this information to its neighbors and listens to channel for similar in-

formation. The ranking of a sensor node with its neighbor depends on the above

information.

Step 4: Each node calculate its CH value (CH val) and its neighbor CH val from

the broadcast information using following equations, which are taken and modified

from [7].

34

CH valk =

v(Si − Smin)/(Smax − Smin)+

(1− v)(Ri −Rmin)/(Rmax −Rmin) Smax 6= Smin & Rmax 6= Rmin

(Ri −Rmin)/(Rmax −Rmin) Smax = Smin & Rmax 6= Rmin

(Si − Smin)/(Smax − Smin) Rmax = Rmin & Smax 6= Smin

constant Rmax = Rmin & Smax = Smin

Smin = min[(Si)] (4.8)

Smax = max[(Si)] (4.9)

Rmin = min[(Ri)] (4.10)

Rmax = max[(Ri)] (4.11)

where i = 1, 2, 3.......(n + 1) and v is the weight for strategy of maximum group

utility and 1− v is the weight of individual regret. Value of v is set to 0.5.

Step 5: After calculating CH val, node will then compare its CH val with its

neighbors. If CH val of the nodes is less then its any neighbor, it elect itself as

CH and broadcast advertisement packet to all of its neighbors. If the node CH val

is greater then at least one of its neighbors, it will wait for advertisement packet

from its neighbor node which has lowest CH val.

Every sensor node checks that whether its is the round immediately after de-

ployment or not. If its not the round immediately after deployment, sensor node

predicting CH’s CH val by calculates the remaining energy of neighbor’s and CH’s

by calculating their energy consumed on transmission. The remaining energy of

neighbor’s and CH’s is calculated from their last know energy and distance from

BS. All other criteria remain same as all the sensor nodes are static. Sensor node

compare its own CH val with the CH’s CH val. If difference between sensor

node’s and CH’s CH val is greater than threshold, the sensor node broadcast a

notification message with all criteria information and and waits for its neighbor’s

information. After receiving neighbor’s information, sensor node perform all of the

above steps. If difference between sensor node’s and CH’s CH val is not greater

than the threshold, sensor node wait for a specific period of time for notification

from other node. The sensor node broadcast its criteria information if receive

35

notification from other sensor node and get associated with new CH otherwise it

remain associated with the current CH as there no new CH elected. The flowchart

of CH election process is show in fig. 4.3.When CHs are elected they send an

advertisement packet to their entire neighbors. Nodes which receive this adver-

tisement packet will send a join request message to the CH in order to join that

respective cluster. CH, in response assign TDMA slots for node communication,

and sends an acknowledgment packet to node.

4.2.3 Sensor Nodes Communication

After CH selection and nodes association with their CHs, nodes communication

starts. Nodes send their sensed data to their associated CHs in assigned TDMA

slots. The data received from the nodes are collected, aggregated and amplified

by the CH. CH then forward this collected data to the BS as shown in fig. 4.4. In

our proposed scheme there are two types of communication operations. These are

inter-cluster communication and intra-cluster communication. In Multi-hop inter-

cluster communication, when whole network is divided into multiple clusters each

cluster has one CH. This CH is responsible for communication for all nodes in the

cluster. CH receive data from all nodes at single-hop and aggregate and transmit

directly to BS or through intermediate CH. In Multi-hop inter-cluster communi-

cation when distance between CH and BS is larger than 10 meter then CH use

intermediate cluster-head to communicate to BS.Through multi-hop communica-

tion, energy consumption is minimized which in turn increases overall network

lifetime and stability. Flowchart of proposed protocol functionality is show in fig-

ure 4.5. Packets to BS and other simulation results are discussed in next section.

36

start

If round= 1

Predict

CH_val

Diff. b/w CH and

any sensor node

CH_val

> 0.1

Wait for

notification

Notification

with all

criteria

information

Notification

Received

Remain

Associated

Broadcast

criteria

Information

Wait for

neighbor

Information

Calculate

CH_val

Less then its

neighbour

Become CH

Wait for CH

broadcast

message

Broadcast CH

information

Broadcast

Information

End

Yes

Yes

Yes

No

Yes No

No

No

Figure 4.3: CH Selection Process

37

Figure 4.4: Communication Model

38

Node Deployment

Neighbor Discovery

Sharing of Information

(four criteria values

among neighbors)

CH

Send Join Request

Broadcast CH

advertisement

Receive CH

advertisement

Receive Join

Request

CH selection based on

Modified VIKOR

Yes No

Start

Receive TDMA slot

from CH

CHs assign and broadcast

TDMA slots to member nodes

Receive data

from nodes

Receive data

from nodes Send sensed data in assigned

slot

Send sensed data in assigned

slot

Aggregate received data

from member nodes

Aggregate received data

from member nodes

Base StationBase Station

End

Communication

Phase

Distance >

10 m

Intermediate

CH

Intermediate

CH

Aggregate all

data and

transmit to

BS

Aggregate all

data and

transmit to

BS

Non CH node

Figure 4.5: Flow chart of Proposed Protocol

39

Chapter 5

Simulation and Results

40

Chapter 5

Simulation and Results

The performance of our proposed scheme is compared and analyzed in this Chap-

ter. We compare and analyze our proposed scheme with fuzzy-TOPSIS based

[34] routing protocol. Simulation of previously proposed scheme and our proposed

protocol is done in MATLAB.

5.1 Simulation Environment

In our simulation 100 number of sensor nodes is randomly dispersed in a field of

100m × 100m. The BS is located at the top of the field. In our simulation we

made some assumptions. First is that sensor nodes are continuously monitoring

the environment and always have data to be sent to the BS. Second is that wireless

channel is free of signal collision and interference. Simulation parameters are given

in Table 5.1.

Table 5.1: Simulation Parameters

Parameter ValueNetwork Area 100m x 100mNumber of Nodes n 100Base Station Position (50,100)Initial Energy 0.5 JData Aggregation Energy 50pj/bit/reportData Packet Size 4000 bitsHello Packet Size 200 bits

41

5.2 Simulation Results

We evaluate fuzzy-TOPSIS based protocol and and our proposed protocol by the

network lifetime, stability of the network, packets to BS, network energy and

hello overhead packets. Following graphs show simulation results of our purposed

scheme compared with previous schemes.

5.2.1 Network Lifetime

In fig. 5.1, the network lifetime is shown which depicts that our proposed schemes

outstrip the previous protocol. This is because of minimizing hello overhead by

restricting the CH selection to some threshold. Furthermore, the multi-hope com-

munication model that we used in our protocol, also play a vital role in increasing

the network lifetime. Figure 5.1 shows that previous fuzzy-TOPSIS based proto-

col dies out around 1100 rounds. Where as in our protocol all sensor nodes die

put around 2400 rounds. This proves that our protocol perform much better that

others.

0 500 1000 1500 2000 25000

10

20

30

40

50

60

70

80

90

100

Number of Rounds

Nu

mb

er

of

Alliv

e n

od

es

Fuzzy Based ModelProposed Model

Figure 5.1: Network Stability and Lifetime

42

5.2.2 Network Stability

Number of dead sensor nodes per round in the network are shown in fig.5.2 .It is

clear from graph that in fuzzy-TOPSIS based protocol, first node dies around 750

rounds where as our proposed scheme first sensor node dies around 1400 rounds.

This show that our protocol surpass previously proposed protocol in terms of sta-

bility as well. Reason for this is that in previous fuzzy-TOPSIS based method, BS

is performing CH selection process, which does not depends on geographical con-

ditions of nodes, where as in our purposed scheme every node itself take decision

for CH, considering the knowledge of neighbor nodes.

0 500 1000 1500 2000 25000

10

20

30

40

50

60

70

80

90

100

Number of Rounds

sg

g


Figure 5.2: Network Stability and Lifetime

5.2.3 Energy Consumption

Energy consumption of network per round is shown in Fig. 5.3. It is observed

that our proposed scheme consumes less energy than previous scheme as residual

energy per round is much higher in our protocol as shown in fig. 5.3. A major

constituent of energy consumption is communication process. Almost 70 percent

of whole network’s energy is consumed in communication. So a proper communi-

cation model is very much necessary for any energy efficient clustering protocol.

In our proposed scheme we use multi-hop communication in both inter and intra-

cluster communication, this is the main reason of lower energy consumption in

43

our proposed scheme is much less than previous fuzzy-TOPSIS based scheme. In

previous schemes, energy variation at different points is observed and it due to

re-selection of CHs in each round. This CH re-selection is avoided in our proposed

scheme by introducing the CH changing threshold and that is also the main reason

of less energy consumption in our proposed scheme.

0 500 1000 1500 2000 2500−5

0

5

10

15

20

25

30

35

40

45

50

Number of Rounds

Re

sid

ua

l E

ne

rgy p

er

Ro

un

d


Figure 5.3: Energy consumption per round

5.2.4 Number of CHs per round

Figure 5.4 shows the number of CHs per round. It can be analyzed from the

fig. 5.4 that in previous fuzzy-TOPSIS based protocol, number of CHs are almost

12 percent of the total number of sensor nodes. This is because of centralized

algorithm used in this protocol. In our protocol, however distributed algorithm is

used but still the number of CHs remain constant. This is because of the efficient

CH selection technique.

5.2.5 Control Overhead (Hello) Packets

The control overhead or Hello packets are the control signals required for any type

of data processing in WSNs. Lager the control overhead packets, greater will be

44

0 500 1000 1500 2000 25000

2

4

6

8

10

12

14

16

Number of Rounds

Nu

mb

er

of

CH

s P

er

rou

nd


Figure 5.4: Total No. of Cluster heads per round

the energy consumption, Hence for any energy efficient clustering protocol it is

necessary that these packets should be minimized. In our proposed scheme CH

change is very rare and we use distributed algorithm for selection of CH. These

are main reasons for minimization of control overhead packets. Figure 5.5 shows

comparison of our proposed scheme with previous fuzzy-TOPSIS model. It can

clearly observed that control overhead packets in our proposed scheme are very

small as compared to previous fuzzy-TOPSIS model. This is because less changes

in CHs per round, which decreases the control overhead tremendously as shown

in fig. 5.5 and increases overall network lifetime.

5.2.6 Packets Sent to Base Station

Number of packets to BS is are shown in fig. 5.6. Total number of packets sent to

BS in our protocol is much higher than other protocols as dipicts in fig. 5.6. This

is because of extended lifetime and stability of network.

45

0 500 1000 1500 2000 25000

2

4

6

8

10

12x 10

4

Number of Rounds

Nu

mb

er

of

He

llo

Pa

cke

ts p

er

Ro

un

d


Figure 5.5: Number of hello packets

0 500 1000 1500 2000 25000

0.5

1

1.5

2

2.5

3x 10

4

NUmber of Rounds

Nu

mb

er

of

Pa

cke

ts t

o B

S

Fuzzy Based Model

Proposed Model

Figure 5.6: Number of packets to base station

46

Chapter 6

Conclusion

47

Chapter 6

Conclusion

In this thesis we design a new routing protocol, which overcomes the deficiencies

in the existing routing protocols. Our protocol is based on four criteria, including

residual energy, number of nodes, distance from BS, and average number of nodes.

WE apply modified VIKOR method for selection of cluster head, which overcomes

the bottlenecks in simple VIKOR method. Our proposed protocol is based on

distributed algorithm. We control frequent change of CH in our proposed scheme.If

the index value of any node is greater than index value of CH plus specic threshold,

then the CH will no more be eligible to act as CH, and nodes will perform re-

election process within the cluster. Due to this number of control overhead packets

in proposed protocol is very small. To improve communication model, we will

introduce two level hierarchy for cluster head selection. In first level CH will be

selected on the basis of modified VIKOR method, while in next level CH will be

selected on basis of its location and residual energy.

We perform MATLAB simulation to compare the results of our proposed scheme

with LEACH and previously proposed fuzzy based centralized clustering model.

All results show that the network performance of our proposed scheme is much bet-

ter than perviously proposed clustering techniques. This improvement is achieved

using distributed algorithm, using multi criteria for CH selection and using two

level hierarchy model.

48

References

[1] Shah, T., N. Javaid, and T. N. Qureshi. “Energy Efficient Sleep Awake Aware

(EESAA) Intelligent Sensor Network Routing Protocol.” In Proceedings of the

15th IEEE International Multi Topic Conference (INMIC12), 2012.

[2] J.ElsonandD.Estrin. “An Address-Free Architecture for Dynamic Sensor Net-

works”. Technical Report 00-724, Computer Science Department, USC, Jan-

uary 2000.

[3] D. Estrin, R. Govindan, J. Heidemann, and S. Kumar. “Next Century Chal-

lenges: Scalable Coordination in Wireless Networks” In Proceedings of the

5th Annual ACM/IEEE International Conference on Mobile Computing and

Networking (MOBICOM), pp. 263270, 1999.

[4] S.Muruganathan, Daniel C.F.MA,R.Bhasin, A.Fapojuwo “A Centralized

Energy-Efficient Routing Protocol for Wireless Sensor Networks” IEEE Com-

munications Magazine, 2005

[5] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient

routing protocols for wireless microsensor networks,” in Proc.33rd Hawaii Int.

Conf. System Sciences(HICSS), Maui, HI,Jan. 2000.

[6] Aslam, M., et al. ”CEEC: Centralized energy efficient clustering a new routing

protocol for WSNs.” Sensor, Mesh and Ad Hoc Communications and Networks

(SECON), 2012 9th Annual IEEE Communications Society Conference on.

IEEE, 2012.

[7] Chang, Chia-Ling. ”A modified VIKOR method for multiple criteria analysis.”

Environmental monitoring and assessment 168, no. 1-4 (2010): 339-344.

[8] Xiangning, Fan, and Song Yulin. ”Improvement on LEACH protocol of wireless

sensor network.” Sensor Technologies and Applications, 2007. SensorComm

2007. International Conference on. IEEE, 2007.

49

[9] Opricovic, Serafim, and Gwo-Hshiung Tzeng. ”Compromise solution by

MCDM methods: A comparative analysis of VIKOR and TOPSIS.” European

Journal of Operational Research 156, no. 2 (2004): 445-455

[10] Chong, Chee-Yee, and Srikanta P. Kumar. Sensor networks: evolution, op-

portunities, and challenges. Proceedings of the IEEE 91.8 (2003): 1247-1256.

[11] Pottie, Gregory J. Wireless integrated network sensors (wins): the web gets

physical. Frontiers of Engineering: Reports on Leading-Edge Engineering from

the 2001 NAE Symposium on Frontiers of Engineering. National Academies

Press, 2002.

[12] Kahn, Joseph M., Randy H. Katz, and Kristofer SJ Pister. Next century

challenges: mobile networking for Smart Dust. Proceedings of the 5th annual

ACM/IEEE international conference on Mobile computing and networking.

ACM, 1999.

[13] Rabaey, J., et al. PicoRadio: Ad-hoc wireless networking of ubiquitous low-

energy sensor/monitor nodes. VLSI, 2000. Proceedings. IEEE Computer Soci-

ety Workshop on. IEEE, 2000.

[14] Shih, Eugene, et al. Design considerations for energy-ecient radios in wireless

microsensor networks. Journal of VLSI signal processing systems for signal,

image and video technology 37.1 (2004): 77-94.

[15] http://www.xbow.com

[16] http://www.sensoria.com

[17] http://worldsens.citi.insa-lyon.fr

[18] http://www.dustnetworks.com

[19] http://www.ember.com

[20] Kemal Akkaya and Mohammed Younis (May,2005.). A survey on routing

protocols for wireless sensor networks and Ad hoc networks, pp325 -349.

[21] Qureshi, T. N., et al. ”BEENISH: Balanced Energy Efficient Network Inte-

grated Super Heterogeneous Protocol for Wireless Sensor Networks.” arXiv

preprint arXiv:1303.5285 (2013).

[22] Mills, K.L. (2007) A brief survey of self-organization in wireless sensor net-

works. Wireless Communications and Mobile Computing 7 (7), 823834

[23] Cerpa, A., and Estrin, D. (2004) Ascent: Adaptive self-conguring sensor net-

work topologies. IEEE Transactions on Mobile Computing 3 (3), 272285.

50

[24] M. N. Elshakankiri, M. N. Moustafa, and Y. H. Dakroury, Energy Efficient

Routing protocol For Wireless Sensor Networks, in International Conference on

Intelligent Sensors, Sensor Networks and Information Processing, 2008, pp.393

398.

[25] J. Feng, F. Koushanfar, and M. Potkonjak, Sensor Network Architecture,

Handbook of Sensor Networks, 2004.

[26] Evaluation of Routing Protocols in Wireless Sensor Networks - Arkiv EX -

Blekinge Tekniska Hgskola.

[27] L. B. Ruiz, J. M. S. Nogueira, and A. A. F. Loureiro, Sensor Network Man-

agement, Handbook of Sensor Networks: Compact Wireless and Wired Sensing

Systems, M. Ilyas and I. Mahgoub, Eds., Ch. 3. CRC Press, 2005.

[28] Jennifer Yick, Biswanath Mukherjee, Dipak Ghosal, ”Wireless sensor network

survey,” Computer Networks,Elsevier, vol. 52, pp. 2292-2330, 2008.

[29] G. Simon, M. Maroti, ”Sensor network-based counter sniper system,” in Pro-

ceedings of the 2nd international conference on Embedded networked sensor

systems, Baltimore, MD, 2004, pp. 1-12.

[30] G. Tolle, D. Culler, W. Hong, et al., ”A macroscope in the redwoods,” in

Proceedings of the 3rd international conference on Embedded networked sensor

systems, San Diego, CA, 2005, pp. 51-63.

[31] P. Zhang, C.M. Sadler, S.A. Lyon, M. Martonosi, ”Hardware design experi-

ences in ZebraNet,” in Proceedings of the SenSys04, Baltimore, MD, 2004.

[32] C.F.Chissseenni, P.Monti and A.Nucci, Energy Efficient Design of Wireless

Ad Hoc Networks, in Proceedings of European Wireless, February 2002.

[33] S. Dai, X. Jing and L. Li Research and analysis on routing protocols for

wireless sensor networks, in Proceeding IEEE, pp 407-41, 2005

[34] Azada, Puneet, and Vidushi Sharmab. Cluster Head Selection in Wireless

Sensor Networks under Fuzzy Environment. ISRN Sensor Networks, vol. 2013,

Article ID 909086, 8 pages, 2013. doi:10.1155/2013/909086.

[35] Islam, Md Motaharul, and Eui-Nam Huh. ”A novel addressing scheme for

PMIPv6 based global IP-WSNs.” Sensors 11.9 (2011): 8430-8455.

[36] Eugen Coca and Valentin Popa (2011). Third Generation Active RFID

from the Locating Applications Perspective, Current Trends and Challenges

in RFID, Prof. Cornel Turcu (Ed.), ISBN: 978-953-307-356-9, InTech, DOI:

10.5772/17534. Available from: http://www.intechopen.com/books/current-

51

trends-and-challenges-in-rfid/third-generation-active-rfid-from-the-locating-

applications-perspective

[37] ”COMSYS - Communications and Distributed systems : Wireless

Sensors Network labs” comsys.rwth-aachen.de. n.d. Web. 25 May.

2013. ¡http://www.comsys.rwth-aachen.de/teaching/ss-12/wireless-sensor-

networks-lab/¿.

52

Date post:	27-Jun-2020
Category:	Documents
Upload:	others
View:	8 times
Download:	0 times

MVC: Modified VIKOR Model based Clustering Protocol for …njavaid.com/Tauseef-Thesis.pdfMVC:...

Documents