23

CHAPTER 2

TERRAIN INVESTIGATIONS OF ROUTING PROTOCOLS

IN WIRELESS SENSOR NETWORKS

2.1 Introduction

In this Chapter, we focused on static, distance vector and on demand based routing

protocols of wireless sensor networks over linear and service life estimator battery models.

The impact of different wireless sensor networks routing protocols has been judged for

average jitter, first and last packet received, total bytes received, average end to end delay,

throughput and energy consumption. For optimal performance of wireless sensor networks,

challenging issues like energy consumption, network routing, localization, coverage and

physical environment are need to be addressed. Low power and inexpensive nodes are

required to meet the performance goal of the wireless sensor network system. Analytical

modeling and real performance prediction of WSN is extremely critical to measure. This

Chapter emphasized towards the network routing protocol estimations with two battery

models in order to achieve the optimal results for the proposed scenario.

2.2 Bellman-Ford Routing Protocol - Static Protocol

This protocol is based on the Bellman-Ford algorithm also called Bellman-Ford Moore

algorithm. It computes a shortest path tree (SPT) and calculates the minimum path for all

vertices in a weighted digraph [Richard, 1958] through a single source vertex from each

router to other routers in a routing area. In contrast to Dijkstra algorithm, it was slower but

more versatile, as it handles the negative edge weights. For many applications, we need

24

negative cycled graphs; hence it becomes useful [Ford et al., 1962]. In case of negative

cycled graphs, early detection is possible through Bellman-Ford algorithm but correction is

not possible for the same [Moore et al., 1959]. For the implementation of the list where the

nodes based on first come first serve principle, Bellman-Ford is surely beneficial. Cheng et

al. [1989] analyzed the Bellman-Ford algorithm for its extended version without bouncing

effect. A wireless sensor network evaluation with loop free Bellman-Ford protocol was

reported by Baharloo et al. [2009].

2.3 Routing Information Protocol - Distance Vector Based

A widely used protocol for wireless networks is routing information protocol (RIP) which

is suitable for both local and wide area. It is similar to open shortest path first (OSPF)

protocol. It can be categorized as an interior gateway protocol (IGP) using a distance vector

routing algorithm. Hedrick [1988] proposed the initial stage of this protocol in 1988 which

was further refined by Malkin [1997]. Advanced techniques such as OSPF and OSI

protocol IS-IS have been supported by routing information protocol as reported in reference

[Malkin, 1998]. As far as the merits of RIP are concerned, it is easily configurable, support

load balancing and loop free. On the contrary, RIP can measure maximum fifteen hops and

shows slower performance when used for a very large scale networks [Ghaleb et al., 2011].

2.4 Dynamic Source Routing Protocol - On Demand Based

Dynamic source routing (DSR) protocol is an on demand routing protocol and specifically

designed for the multi-hop wireless networks [David, 1994]. The major difference between

this protocol and other on demand routing protocols is that it is beaconless and does not

25

require periodic beacons. The DSR protocol provides a lot of characteristics like self-

configurability, self-adaptability that makes it network efficient [Johnson et al., 1996]. The

DSR protocol allows the dynamic discovery of the source node and destination node in the

network. It constitutes order lists of nodes that contain all the information about the data

packet life cycle like beginning stage, intermediate stage and final stage. From the

functionality point of view, the DSR contains two mechanisms namely - route discovery

and route maintenance. In the first mechanism, a node wishing to send a packet to a

particular node obtains a source route from that node and route will be discovered only if it

does not already exist. In the second mechanism, a node detects the route with earlier

discovered route which is subjected to the inclusion of network topology change. Route

maintenance mechanism is required only in case of packet transmission breakage between

nodes. Route maintenance mechanism and route discovery mechanism are demand specific

in their nature i.e. on demand type. As compared with other protocols, the DSR requires no

periodic packets and no periodic routing advertisement like link status or neighboring

packet detection [Broch et al., 1999]. These properties take packet overhead to a minimum

value correspond to the stationary nodes. When the nodes are mobile, the routing packet

overhead scales automatically to the required number of tracks as needed. This allows the

routing protocol to behave appropriately in both the conditions either nodes are static or

dynamic.

2.5 Ad Hoc on Demand Distance Vector Routing Protocol

The AODV routing protocol basically extends Bellman-Ford distance vector algorithm

concept in a relative manner. The AODV routing protocol was specifically designed for the

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highly dynamic wireless networks [Perkins et al., 1994; Perkins et al., 2003; Sklyarenko,

2006]. But the un-predictable topology change in wireless sensor network by node failure

makes them virtual dynamic networks. Hence, reactive routing protocols represent an

adequate choice for event driven or periodic data driven WSN applications especially.

Being a reactive type of protocol, routes are created only when required. The AODV

routing protocol stores one entry per table and a sequence number as similar to the

traditional approach of routing to maintain up to date routing information. The AODV

ensures loop free routing in the different situations. This protocol stick towards the time

based state information with each node so that any node that is not recently used should be

treated as dead node. The AODV routing protocol constitutes the traditional concept of

routing table. It stores parameters such as routing information, next hop address, a sequence

number and node usages. It is because of the fact that the node maintains a specific time

span thereafter its entry should be discarded [Chearon et al., 2010]. In case of any link

failure, the neighboring node should be notified about it. In AODV, routing can be

determined by two cycles: query and reply. This protocol uses four control messages

namely: Routing request message (RREQ), Routing reply message (RREP), Routing error

message (RERR) and HELLO message. During the execution, first a node broadcasts a

RREQ message to another node, after that the RREP message is received in the unicast

manner. Further in case of link failure, an error message RERR conveyed to the

neighboring nodes [Sundararajan et al., 2010]. The HELLO message is used for evaluation

and detection of the links between the various nodes.

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2.6 Dynamic on Demand Based Protocol

One of the simple and fast routing protocols for multi-hop networks is a dynamic mobile

ad-hoc network on demand routing protocol (DYMO) [Chakeres et al., 2007; Chakeres et

al., 2010]. It discovers the routes in an on demand fashion and offering enhanced coverage

for dynamic topologies in the wireless networks. Similar to AODV, the source sends a data

packet with a RREQ message to discover the route. The DYMO router waits for a route

after issuance of the RREQ message. If during the waiting period route is not obtained, it

may issue another RREQ. It uses an exponential back off mechanism to reduce the

congestion in the network. Data packets are buffered which are still to be routed as per the

predefined size whereas older packet being discarded accordingly. A RERR message is

issued if a data packet cannot be delivered to the destination due to missing route. In each

DYMO router, little state information like the active source and destination is maintained

because the applicable devices such as WSN have memory constraints. Next sections

summarized the brief description of battery models used for our proposed WSN evaluation.

2.7 Linear Battery Model

This model uses coulomb counting technique as its basis for operation. The coulomb

counting technique accumulates [Rakhmatov et al., 2003; Pedram et al., 2002; QualNet

4.5.1 Wireless Model Library, 2008] the dissipated coulombs from the beginning of the

discharge cycle. It estimates the remaining capacity by measuring the difference between

the accumulated value and a prerecorded full-charge capacity. In the variable load

condition, this method might lose accuracy as it ignores non linear discharge effect. The

battery is discharged in a linear fashion as a function of discharge current load.

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2.8 Service Life Estimator Battery Model

This battery model uses modular approach [Rakhmatov et al., 2003; Pedram et al., 2002;

QualNet 4.5.1 Wireless Model Library, 2008] and can estimate the service life of a battery

operated node with time varying load for an event driven scenario. On the underlying side,

this battery model deploys the tightly coupled component methodology as suggested by

Sarma and Rakhmotav [2003]. For the evaluation, Rakhmotav model remains the most

accurate model than other models by using partial differential equation. For estimation

purposes, one can utilize the following equations (2.1) to (2.3) under constant load [Pedram

et al., 2002]:

(2.1)

Where L denotes life time, m reflects observed lifetimes, and represents objective

specific parameters. The battery voltage changes with time from open-circuit value (Vopen)

to some cutoff value (Vcutoff) for a mentioned load. The observed lifetime denotes that

battery voltages reaches Vcutoff and predicted time denotes the time for which equation (2.1)

holds for a given set of constant loads correspond to the observed lifetimes. To achieve the

objective to match predicted lifetime closely to observed lifetimes this is hard as for

equation (2.1). Another way is to fit the load value for a given set of observed lifetimes.

Assume be the fitted value for I (k) and according to reference [Rakhmatov et al.,

2003]

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(2.2)

One can employ a standard least-squares estimator method to find matches I(k) as closely

as possible for all. The selection should be as per the following model parameters.

(2.3)

is minimized.

2.9 Performance Evaluations of Routing Protocols

We conducted extensive simulations to evaluate the performance of two battery models

with Bellman-Ford, RIP, DSR, AODV and DYMO routing protocols. Simulations are

implemented on QualNet 5.0.2 software package [Scalable Network Technologies, 2003], a

discrete event simulator and capable of simulating both the wired or wireless scenarios

from simple to the complex situations. A block diagram representing the entire simulation

process is shown in figure 2.1.

Figure 2.1: Simulation block diagram for battery models with routing protocols

In the simulation model, there are 100 nodes connected to one wireless station with terrain

dimensions 1500 m × 1500 m as flat area and attitude range above and below sea level is

General

Parameters

Configuration

Set

Nodes

Topology

Place Node

and Set

Mobility

Configure

Wireless

Environment

Set

Routing

Protocol

Statistics and Packet

Tracer Configuration

Parallel

Simulation

Run Time

Optimization Output

Input

30

1500 m. The entire area is further divided into 225 square shaped cells. Both the static or

dynamic nodes are considered. This simulation is used with the IEEE standard 802.11 as

distributed coordination function (DCF) for MAC layer protocol. The propagation model

used is two-ray with 2 Mbps radio bandwidth and one communication channel with 2.4

GHz frequency. The traffic type is constant bit rate (CBR). The selection of source and

destinations for each CBR is done in a random manner. The flow of data for each source

and destination node remains constant during the lifetime of a simulated execution which

lasted for 1200 seconds. The mobility model is a random way point with the speed ranging

from 0 m/s to 20 m/s and a pause time of 30 seconds. Numbers of CBR flows are ten with

mobility interval 100 msec in all simulation sets. Table 2.1 shows the summarization of

parameters used in simulation setup.

Table 2.1: Simulation Parameters

Parameters Value

Terrain Dimensions 1500 m × 1500 m

Altitude Above Sea Level 1500 m

Simulation Time 1200 s

No. of Nodes 100

Mobility Interval 100 msec

No. of Channel 1

Channel Frequency 2.4 GHz

No. of CBRs 10

MAC Protocol 802.11

Node Placement Random

Traffic Type CBR

Data Rate 2 Mbps

Mobility Model Random Waypoint

Network protocol IPv4

Routing protocol Bellman-Ford, AODV, DSR, DYMO, RIP

Battery Models

Battery Type

Linear, Service Life Estimator

DURACELL (AA)

Battery Charge Monitoring Interval 60 s

Temperature 290 K

Antenna Model Omni directional

Path-loss Model Two-Ray

31

We have collected data for seven performance metrics namely: average jitter, first packet

received, last packet received, total bytes received, average end to end delay, total byte

received, throughput and energy consumption. The first six metrics are evaluated in all

simulation set. The energy consumption is also evaluated separately for linear (LN) and

service life estimator (SLE) battery models within the deployed scenario.

2.9.1 Average Jitter Analysis

In our evaluation, we observed average jitter accuracy of five protocols with the service life

estimator model as shown in figure 2.2. Average jitter denotes the time variable measured

between the arrival of the packets (due to the congestion of the network), timing drift or

route change.

Figure 2.2: Graph of average jitter versus routing protocols over SLE model

40 50 60 70 80 90 1000

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

Nodes

Avera

ge J

itte

r (s

)

Bellman Ford

RIP

DSR

AODV

DYMO

32

We calculated value of average jitter at ten randomly selected nodes - 47, 50, 54, 63, 74,

84, 87, 95, 98, 99 with a contestant bit rate. We assumed that at least ten percent nodes are

participating in communication and rests of the nodes are connected to a central base

station all the times. These nodes act as a representative for the entire network. We

observed that if we change the node value then we get the same resultants in all the cases.

The average jitter values of the Bellman-Ford protocol outperform other protocols for

nodes 47, 50, 54,74,82,98. In the context of the Bellman-Ford protocol, a novel analytical

model for wireless sensor network based on the Markovian general distribution with one to

k serves (M/G/1/k) queuing system and Bellman-Ford routing strategies to predict average

message latency was reported by Baharloo et al. [2009]. We extended similar work towards

the estimation of Bellman-Ford protocol performance based on average jitter, packet

delivery, throughput and end to end delay for the same in contrast with other protocols. RIP

protocol overweigh other protocols for the node value 63, 87 and 95. DYMO performs

better in case of node ninety-nine as compared to rest of the protocols. Ghaleb et al. 2011

made a comparative analysis among DSR and RIP protocol and investigated that RIP

outperforms DSR in case of average jitter. Our results show good agreement with the

results reported in reference [Ghaleb et al., 2011]. It is further observed that initially

Bellamn-Ford protocol reflect minimum average jitter than other protocols and at last

DYMO protocol overweigh other protocols.

2.9.2 Packets and Total Bytes Reception Analysis

Secondly, we estimated the time span for the first packet and last reception in all the

protocols as shown in figure 2.3 and figure 2.4.

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Figure 2.3: Graph of first packet reception versus routing protocols for SLE model

We can rank them in order of first packet reception as (i) Bellman-Ford (ii) AODV (iii)

DSR (iv) DYMO (v) RIP. As far as last packet reception is concerned, the small change in

sequence observed as (i) Bellman-Ford (ii) DSR (iii) RIP (iv) DYMO (v) AODV. Again,

Bellman-Ford outperforms rest of the protocols at the first node i.e. node 47, as depicted in

figure 2.4 whereas DYMO outperform other protocols for last node i.e. node 99 in case of

first packet reception analysis. The AODV protocol analysis also shows good performance

in the above said evaluation. We analyzed that functionality involved in a specific protocol

for its operation is responsible for the same. We observed that for a small network,

Bellman-Ford outperforms other and in the case of large networks, DYMO shows better

behavior.

40 50 60 70 80 90 1001

1.5

2

2.5

3

3.5

4

4.5

Nodes

First

packet

Receiv

ed (

s)

Bellman Ford

RIP

DSR

AODV

DYMO

34

Figure 2.4: Graph of last packet reception versus routing protocols for SLE model

We estimated the total bytes received at the nodes after the deployment of these five

protocols as shown in figure 2.5. It has been observed that DSR outperforms rest of the

protocols because of the byte destruction rate remains quite less than other protocols. As

shown in figure 2.5, the order of error proneness during bytes transmission for these

protocols can be mentioned as (i) DSR (ii) DYMO (iii) AODV (iv) RIP (v) Bellman-Ford.

This is due to the fact that lesser the bytes received during the transmission depict lesser

error proneness of these protocols. In the context of AODV protocol, three optimizations

were studied by Lee et al. [2003]. Theses optimization includes ring search, a query

localization protocol and local repair mechanism. Bai et al. [2006] made a comparison

40 50 60 70 80 90 10024

24.01

24.02

24.03

24.04

24.05

24.06

24.07

24.08

Nodes

Last

Packet

Receciv

ed (

s)

Bellman Ford

RIP

DSR

AODV

DYMO

35

among AODV and DSR protocols and proposed a new protocol incorporating features of

these two protocols.

Figure 2.5: Graph of total byte reception versus routing protocols over SLE model

We have already evaluated the performance of the dynamic source protocol in wireless

sensor network in the reference [Verma et al., 2011]. Also, we have presented the

behavioral assessment of the AODV protocol over temporal constraints in wireless sensor

network in the reference [Verma et al., 2012]. A cross layer design pattern of AODV for

multi-hop flow of the wireless network was suggested by Chou et al. [2013]. We extended

this work towards the evaluation two battery models with five different routing protocol

including AODV and DSR.

40 50 60 70 80 90 1001.06

1.08

1.1

1.12

1.14

1.16

1.18

1.2

1.22

1.24x 10

4

Nodes

Tota

l B

yte

s R

eceiv

ed

Bellman Ford

RIP

DSR

AODV

DYMO

36

2.9.3 Throughput Analysis

This refers to the number of delivered packets per unit of time within the network. Figure

2.6 shows the throughput of Bellman-Ford, RIP, DSR, AODV and DYMO for our

proposed simulation model. Raghuvanshi et al. [2010] reported that an average throughput

remains highest for DYMO protocol and energy consumption remains least when used with

25% duty cycle at MAC in contrast with AODV routing protocol.

Figure 2.6: Graph of throughput versus routing protocols over SLE model

In our evaluation, we extended this work a bit intricate level and evaluated the DYMO and

DSR protocol in contrast with AODV, RIP and Bellman-Ford. We observed that DYMO

and DSR exhibits the maximum throughput than other protocols for most of the nodes,

whereas Bellman-Ford shows the decrement in behavior for throughput in approximately

40 50 60 70 80 90 1003900

4000

4100

4200

4300

4400

4500

Nodes

Thro

ughput

(bits/s

)

Bellman Ford

RIP

DSR

AODV

DYMO

37

all the cases. We analyzed that the enhanced mechanism involved in DYMO and DSR

protocols with lowers the route failure rate, resulting in better throughput all the time as

compared to other protocols in the scenario.

2.9.4 Average End to End Delay Performance

This refers to the time from source to destination node taken by a packet across the

network. This includes transmission resultant, propagation and processing delay or all

possible delays which can occur during packet transmission. Figure 2.7 shows the average

Figure 2.7: Graph of average ETE delay versus routing protocols SLE model

end to end delay of Bellman-Ford, RIP, DSR, AODV and DYMO. We observed that

Bellman-Ford exhibits the lowest end to end delay for most of the times. At the end point,

40 50 60 70 80 90 1000

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Nodes

Avera

ge E

nd-t

o-E

nd D

ela

y (

s)

Bellman Ford

RIP

DSR

AODV

DYMO

38

i.e. node 99, the AODV protocol shows better behavior than other protocols and exhibits

lower end to end delay. It has been observed that end to end (ETE) delay in the figure 2.7

does not show an increasing trend. This is due to a constant number of CBR sources used in

our model. We again observed that for smaller networks, Bellman-Ford outperforms than

the other protocols because of the shorter average route even if used in congested networks.

On the other hand, the AODV performs better due to less congestion in the larger networks

even if consumes more average route length. Manju et al. [2013] reported that there exists a

trade-off between delay and throughput in case of the DYMO protocol which remains true

in our case as the delay remains most of the times. Raghuvanshi et al. [2010] shows that

DYMO exhibits better performance when using linear battery model than AODV, DSR and

Bellman-Ford in terms of throughput on the scarification of end to end delay aspect. We

extended this work by incorporating RIP protocols and using service life estimator battery

model in the comparative evolution of these protocols.

2.9.5 Energy Consumption

The energy consumption issue always remains as a major concern in wireless sensor

networks. We calculated the average energy consumption by Bellman-Ford, RIP, DSR,

AODV and DYMO protocols over linear and service life estimator models in wireless

sensor network. Figure 2.8 compares five routing protocols from energy consumption

aspect. A comparative analysis about the energy consumption with respect to sensors value

increment was reported by Chearon et al. [2010]. In our proposal, we extended this concept

towards the different WSN routing protocols by correlating these protocols with different

battery models. We observed that DYMO protocol consume minimum power in both the

cases of linear and service life estimator battery model than other protocols as shown in

39

figure 2.8. This is because of the fact that the residual battery capacity remains a maximum

in the same case. Li. et al. [2007] reported that under the linear model, DYMO protocol

represents best behavior when compared with Bellman-Ford, DSR and AODV. We further

extended this comparative analysis to RIP protocol with a service life estimator model in

our consideration. We analyzed that as far as the maximum power consumption concerned,

Bellman-Ford consumes maximum power in both the cases as it shows less residual battery

capacity. According to figure 2.8, x-axis denotes routing protocols: 1 Bellman-Ford, 2 RIP,

3 DSR, 4 AODV and 5 DYMO and y-axis represent battery capacity in mAh.

Figure 2.8: Energy consumption of LN and SLE battery models versus routing protocols

1 1.5 2 2.5 3 3.5 4 4.5 51000

1200

1400

1600

1800

2000

2200

2400

2600

2800

Rouing Protocols

Resid

ual B

att

ery

Capacity (

mA

.h)

Linear Model

Service Life Estimator Model

40

2.10 Summary

In this Chapter, we analyzed the five WSN routing protocols and two battery models in the

literature and explored them in details. We summarized that the current state of art in these

models. Moreover, the quality of services specific aspects like average jitter, packets

delivery, throughput, end to end delay and energy consumption have been analyzed for the

different routing protocols. Finally, we investigated towards the implementation and

assessment of routing protocols in our simulation model. It has been concluded that DYMO

routing protocol performance overweighs the rest of the protocols in our proposal and

service life estimator model outperforms linear model in all the cases of energy

consumption.