comparative study of routing protocols in wireless sensor networks by abid afsar khan malang palsapi

Comparative Performance Study of Routing Protocols in Wireless Sensor Network

By

Abid Afsar Khan Malang Palsapi

فی) س ل نگ ف ل سر خان م د اف (عاب

This thesis is submitted to the Faculty of the Computing Griffith College in partial fulfillment of the requirements for the degree of

Master of Science

in

Computing

Under the supervision of Dr. Faheem Bukhatwa

October 2012

Dublin, Leinster

Copyright 2012,Abid Afsar

i

Table of Contents

Sr.

No.

TITLE Page

No.

1. Introduction 1

1.1 Background 1

1.2 Problem Definition 2

1.3 Motivation for Current Work 3

1.4 Research Question 4

1.5 Research Methodology 5

1.6 Aims and Objectives 7

1.7 Assumption 8

1.8 Thesis Contribution 8

1.9 Related work 9

1.10 Organization of the Thesis 9

Summary 10

2. Routing Protocols in IP-Datagram Networks 11

2.1 Introduction 11

2.2 Routing 11

2.3 Proprieties of Routing Protocols 12

2.4 Classification of Routing Protocol 12

2.5 Goals of Routing Protocol 15

2.6 Conventional Routing Protocols 16

2.6.1 Adaptive Routing Protocols 16

2.6.2 Open Shortest Path First (OSPF) 17

ii

Sr.

No.

TITLE Page

No.

2.7 Enhanced Interior Gateway Routing Protocol (EIGRP) 22

2.7.1 Link-state-based Routing 22

2.8 Border Gateway Protocol or Path Vector- based Routing 24

2.9 Advance Routing Approaches 26

2.9.1 Self-Adjusting Routing Protocols 26

Summary 30

3. Routing Protocols in Wireless Ad-Hoc and Sensor Network 31

3.1 Introduction 31

3.2 Classification of Wireless Network 31

3.3 Conventional Ad-Hoc Routing Protocol 33

3.3.1 Optimal Source Routing (OSR) 34

3.3.2 Wireless Routing Protocol (WRP) 39

3.3.3 Global State Routing (GSR) Protocol 40

3.4 Wireless Sensor Network (WSNs) 41

3.4.1 Network Characteristics, Design Objectives 41

3.4.2 Network Design Objectives 43

3.4.3 WSNs Network Design Challenges 45

3.5 Routing 47

3.5.1 Routing in Wireless Sensor Network 47

3.5.2 Classification of Wireless Senor Network Routing Protocols 49

Summary 68

4 Design of Simulation Experiments 69

4.1 Introduction 69

iii

Sr.

No.

TITLE Page

No.

4.2 Scalable Ad-Hoc Network Simulator (ShoX) 70

4.3 Architecture of ShoX 71

4.4 ShoX Key Features 74

4.5 ShoX Configuration 75

4.6 Metrics 76

4.6.1 Simulation Parameters 77

4.7 Experimentation Design and Setup parameters 78

4.7.1 Experiment No.1 –Design of Small Network Topology 78

4.7.2 Experiment No.2 – Design of Medium Network Topology 80

4.7.3 Experiment No.3- Design of Large Network Topology 82

4.7.4 Experiment No. 4- Design of Simulation Time variation 84

4.7.5 Experiment No. 5- Design of Nodes Deployment Area variation 85

4.7.6 Experiment No. 6- Design of Interference Handler Model Variation 86

Summary 87

5 Implementation and Results Analysis 88

5.1 Introduction 88

5.2 Implementation 88

5.3 Experiment 1: Small Nodes Scenario 88

5.3.1 Case 1, 2, 3 & 4: Measurement of packet drop ratio in small number

of stationary and mobile nodes using OSR and Rumor routing

protocols

89

5.4 Experiment 2- Medium Nodes Scenario 92

iv

Sr.

No.

TITLE Page

No.

5.4.1 Case 1, 2, 3 & 4: Measurement of packet drop at hop in 25 stationary

nodes using OSR and rumor routing algorithm

93

5.5 Experiment 3- Large Nodes Scenario 96

5.5.1 Case 1, 2, 3 and 4: Measurement of packet drop at hop in 49

stationary nodes using OSR and rumor routing algorithm

97

5.6 Experiment No. 4- Simulation Time Variation 100

5.6.1 Case 1, 2, 3 and 4: Measurement of drop packet ratio/rate at

stationary topology using OSR and rumor routing algorithm

101

5.7 Experiment No. 5- Network Deployment Area Variation 105

5.7.1 Case 1, 2, 3 and 4: Measurement of drop packet ratio/rate at

stationary topology using OSR and rumor routing algorithm

105

5.8 Experiment No. 7- Interference Handler Model Variation 109

5.8.1 Case 1, 2, 3 and 4: Measurement of packet drop ratio at stationary

topology using OSR and rumor routing algorithm

109

5.9 Simulation Results and Performance Analysis 113

Summary 116

6 Conclusion And Future Work 117

6.1 Introduction 117

6.2 Conclusion 117

6.2.1 Reflection of our work on research question 118

6.3 Future Work 120

Bibliography 121

v

Sr. No. List of Tables Page

No.

3.1 Seven WSN’s routing protocols categories 50

4.1 Simulation Parameters Table 77

4.2 Parameters Table-Exp 1- Case 1: Rumor Routing Stationary Nodes 79

4.3 Parameters Table Experiment 1- Case 2: Rumor Routing Mobile Nodes 79

4.4 Parameters Table -Experiment 1- Case 2: Rumor Routing Mobile Nodes 79

4.5 Parameters Table -Exp1- Case 2: Rumor Routing Mobile Nodes 80

4.6 Parameters Table -Exp 2- Case 1: Stationary nodes using rumor routing 81

4.7 Parameters Table -Exp 2- Case 2: Mobile nodes using rumor routing 81



4.10 Parameters Table -Exp 3- Case 1: Stationary nodes using rumor routing 83

4.11 Parameters Table -Exp 3- Case 2: Mobile Nodes using rumor routing 83

4.12 Parameters Table -Exp 3- Case 3: Stationary Nodes using OSR routing 83

4.13 Parameters Table -Exp 3- Case 4: Mobile Nodes using OSR routing 83

4.14 Parameters Table -Experiment 4- case 1-Simulation Time 84




4.18 Parameters Table -Experiment 5- case 1-Deployement Area 85

vi

Sr. No. List of Tables Page

No.

4.19 Parameters Table -Experiment 5- case 2-Deployment Area 85



4.22 Parameters Table Experiment 6- Case 1- Interference handler model 86

4.23 Parameters Table -Experiment 6- Case 2- Interference handler model 86



5.1 Parameters of IEEE802.11g WLAN standards 91






vii

Sr. No. List Of Figures Page

No.

1.1 Typical WSNs Components overview 1

1.2 (a) Represent small topology, ( b) Represent medium topology, (c)

represent large topology

4

1.3 (a) Represents dense network in a smaller area, while figure (b)

Represents sparse network of the same size but in large area

5

1.4 Bar chart representation of simulation pause time 6

1.5 Represent interference and communication range 6

2.1 Classsfication of routing proctols startegies in IP-Datagram networks 13

2.2 Shortest path finding mechanism 18

3.1 Block diagram of wireless network classification 33

3.2 Ad-hoc routing protocols classification chart 34

3.3 OSR single node movements 36

3.4 Classification of spinal routing algorithm 39

3.5 Hierarchical diagram of WSNs routing protocols classification 49

3.6 Rumor routing chart representation 52

3.7 Query is originated and query source is looking for the path to reach

to the event

53

3.8 Agents aggregating to multiple events 54

3.9 Represents the greedy approach from node x to node y, because

these are located in close neighborhood.

60

3.10 In Greedy Forwarding Routing the data packet is forwarded to a

neighbor that which is located in a close proximity

60

viii


No.

3.11 When greedy routing gets stuck in topology 61

3.12 When there is a hole in the network 61

3.13 Generic view of different faces of planner graph 62

3.14 Generic view of source and destination at planner graph 62

3.15 Represents base station, cluster head, and cluster 67

3.16 Different level of hierarchies 68

4.1 ShoX starting view 70

4.2 ShoX Configuration Panel 71

4.3 ShoX Architectural View 72

4.4 Network topology of 10 nodes 78



5.1 Relative performance comparison in small stationary topology of 10

mobile nodes

92

5.2 Relative performance comparison in small mobile topology of 10

nodes

92

5.3 Relative performance comparison in small stationary topology of 25

nodes

96

5.4 Relative performance comparison in small mobile topology of 25

nodes

96

5.5 Relative performance comparison in large stationary topology of 49

nodes

100

5.6 Relative performance comparison in large mobile topology of 49

nodes

100

5.7 Relative performance comparison at different simulation time 104

ix


No.

5.8 Relative performance comparison at different simulation time 104

5.9 Relative performance comparison at deployment area of 300 x 400

m2

108

5.10 Relative performance comparison at deployment area of 300 x 400

m2

108

5.11 Relative performance comparison at minimum SNR interference

model

112

5.12 Relative performance comparison at minimum SNR interference

model

112

Sr. No. List of Equations Page

No.

3.1 Relation between Radio Range and Grid Size

r 2

+ (2r)2 ≤ R

2

Where r ≤ R∕√5 [8]

56

Disclaimer

I hereby certify that this material, which I now submit for assessment on the programme of

study leading to the Degree of Masters of Science in Computing at Griffith College

Dublin, is entirely my own work and has not been submitted for assessment for an

academic purpose at this or any other academic institution other than in partial fulfillment

of the requirements of that stated above.

Signed: _____________________ Date: ___________________

x

Abstract

Modern wireless sensor network can be expanded into large geographical areas via cheap

sensor devices which can sustain themselves with very a low power usage. The networking

capability enables these sensor nodes to incorporate, collaborate and coordinate with each

other and this is a fundamental shift in the field of networks which differentiates sensor

network nodes form other networks such as IP-datagram, Ad-Hoc and so on.

Currently, routing in the wireless sensor network faces multiple challenges, such as new

scalability, coverage, packet loss, interference, real-time audio and video real time streaming,

harsh weather environments, energy constraints and so forth. Network routing can be called

an amalgamation of routing protocol and routing algorithm. The job of the routing protocols

is to provide a cohesive view of network nodes topology while routing algorithm provides the

intelligence in terms of optimal path calculation.

We set out to conduct a detailed study of routing protocols in a IP-datagram, wireless ad-hoc

and sensor network, and also accomplished routing protocols comparison against the chosen

network performance factor dropped packet ratio.

Routing protocols play an important role in modern wireless communication networks.

Routing protocols’ performance can be measured by a number of factors such as packet

dropped rate and so forth.

Rumour and Optimal Spinal Routing algorithms are compared using ShoX simulation and the

results and analysis are based upon the simulation experiments.

xi

Dedication

I dedicate my thesis to my beloved family, especially

To my late father:

Haji. Muhammad Imran Khan ( Mamaan Khan),

To my mother,

To my late grandfather and grandmother,

To my uncles:

Haji. Muhammad Afsar Khan

Haji. Muhammad Irshad Khan

Haji. Tahir Muhammad Khan

xii

Acknowledgments

In the name of Allah, most Gracious, most Compassionate.

First of all, I would like to offer my special thanks to Allah for the guidance and assistance in

this thesis.

I wish to offer my special thanks to my supervisor Dr. Faheem Bukhatwa for his advice and

support during the writing of this thesis.

I would like to pay special thanks to my family for their prayers and encouragement during

my studies.

Finally, I would like to give special thanks to my all weather friends Kalpesh Nahire (Nasik

India), M. Tahir Azam (Chakwal Pakistan), Sumit Walia (Delhi India), Asif Ali (Derby UK),

Dr. M. Asim ( JMU Liverpool UK), Shah Fahad (Swat Pakistan), and M. Imran Akhtar

(Haripur Pakistan).

Abid Afsar Khan Malang Palsapi

Griffith College Dublin

October 2012


1

Chapter 1

Introduction

1.1. Background

Wireless communication is rapidly growing in our day to day life because it is easy to

deploy and is more flexible. In particular, the wireless sensor network (WSN‟s) is one of the

latest innovations in the field of wireless communication. It consists of tiny mote which hold

a small battery, CPU and sensor. Today, sensor motes are widely used by the military in the

battle field as a means to closely monitor enemy activities. Sensor networks are also used in

healthcare, wide-life, temperature monitoring and many other applications. A diagrammatic

components overview of WSNs is shown below,

Figure 1.1. Typical WSNs components overview

The above diagram represents a number of WSNs components, such as sensor node

architecture, base station, deployment area, and sensor nodes event region which are

represented in blue and green colours. Routing is an important issue in a wireless sensor

network and a number of routing protocols were proposed however, an efficient routing

algorithm remains an issue

The recent development of a wireless sensor network has led us to an innovative use of

small sensory nodes which operate with a very low power in extreme environmental

conditions. The group of small sensory nodes are randomly deployed in a sensor field.

Theses nodes have the ability to organise themselves automatically and to detect


2

neighbouring nodes to from an ad-hoc network. The nodes of the WSNs can plan what sort

of sensing data to receive, send, or query some particular event. The query concept in sensor

networks leads us to new ways of routing, for instance clustering sensor nodes, redundancy,

and so forth.

Today, there are number of sensor motes commercially with low prices ranging from 50$ to

100$ per single mote. It is further expected that the price will go down in the next five years.

One of the leading manufacturers is the Crossbow Inc., which makes Intel motes. There are

number of development projects is in progress, such as Smart Dust Project which are going

to optimise a large number of motes into a single chip, operating system called TinyOS

which is currently freely available and being developed for a senor network. A

programming language called EmStar is used for the development of sensor nodes, a

communication interface called NesC is developed for sensor nodes and finally a database

called TinyDB was developed to transfer data between heterogeneous sensor networks.

1.2. Problem Definition

The rationale of this thesis is to investigate efficient routing protocols which can

successfully deliver data packets in sensor network. Wireless network protocols which are

currently in place have suffered from a number of issues such as drop packets, address table

overhead, topology convergence, throughput, data packets delay, routing overhead and so

on. In WSNs there are number of issues which pose new challenges for the wireless sensor

network. As we are aware that WSNs mote consist of a small battery with limited power

and because of this limitation it gives birth to a new set of problems such as routing, and

scalability in WSNs.

The motes perform their operations by the sensor on board but it‟s quite challenging to route

data when the intermediary motes lose battery power at transit or have low power, and it

further leads to problems such as topology adjustment, mote data recovery and back-up and

so forth. Therefore we believe that to route sensing data properly we need to have intelligent

WSNs routing protocols which give better strategies in terms of energy and scalability

WSNs routing is still a challenging area for the researcher. One problem is the energy in

WSNs, so there is an open option to make routing decisions on the basis of energy

awareness. The core issue involved with wireless senor network routing is related to

rigorous resource constraints allocation in terms of computation, storage and energy

consumption which make it not only interesting but challenging.


3

In the event of a problem the associated routing algorithm does not have an intelligence to

adjust them dynamically and it is also termed as non-adaptive routing protocols.

In carrying out our research we found that a large amount of work has been done in the area

of routing in WSNs, but these are largely individual contributions on specific routing

protocols. We feel that there is a need for a detailed level of study in the area of WSNs

routing protocols. There are a number of routing protocols proposed for WSNs routing but

unfortunately the consideration and need for an efficient, intelligent routing mechanism was

not taken on board.

1.3. Motivation for Current Work

• Routing is an important research area in wireless communication networks and

particularly needs more research interest due to emerging technologies such as a

wireless sensor network, network on chip and so on

• The wireless sensor network brought new opportunities and challenges, because of its

non-infrastructure-based network, and can be deployed in a large territory having harsh

environmental conditions, where sensory devices can be left unattended. We can also

study environment-related events in real time with more precision and accuracy

• The WSNs-based network is further evolving towards a multimedia-based network

which involves heavy traffic, such as live video monitoring of a remote event and so on.

Therefore, the WSN is facing significant new challenges such as drop packet, routing

overhead, packet delay, and so forth

• number of routing algorithms were proposed but there is an urgent need for a comparative

study on these protocols which will be used as a guide tool for researcher and developer in

the routing domain.

1.4. Research Questions

The research is focused on the comparison between some routing protocols. After some

search and studies the choice of protocols was narrowed down to two: Rumour and OSR

protocols. The research question can be outlined by one main question and further clarified

by four sub-questions. The main question for this research is the following:

Q. Which of the two routing protocols: rumour protocol or OSR protocol, performs better

under different circumstances?


4

The main research question is further defined as,

Q1. Which protocol performs better in small, medium, and large network topology?

Figure 1.2. (a) Represent small topology, (b) represent medium topology, (c) represent large

topology

Q2. Which protocol performs better with a different sized deployment area?

The node deployment area variants are explained with the help of the following two

scenarios, where the geographical areas are different but the number of nodes remains the

same.

(a) (b)

(c)


5

Figure 1.3. (a) represents a dense network in a smaller area, while figure (b) represents a

sparse network of the same size but in a larger area

Q3. Which protocol performs better at different simulation times?

The different variant of simulation times, such as 20, 60, and 100 seconds and so on

Figure 1.4. Bar chart representation of simulation time

(a) (b)


6

Q4. Which protocol suffers more from interference?

The interference between nodes A, C and B is illustrated as,

Figure 1.5. Represents interference and communication range

Moreover, the drop packet ratio will be used as a network performance evaluation metric for

the aforesaid scenarios.

1.5. Research Methodology

Initially, we start our research by investigating the wire network routing protocol in detail

which is detailed in Chapter 2. But later on we stick to wireless network routing protocols

because it is an area which is rapidly expanding due to their low cost, high mobility,

scalability, easy deployment and so forth.

Furthermore, our work consists of two parts. First, we design some proposed network

scenarios and stimulate these by using the Scalable Ad-Hoc Network Simulator (ShoX) for

the OSR routing algorithm and observe the performance and behaviour of OSR alone.

Second, we apply the same network model to rumour routing algorithm using the ShoX

simulator and judge the performance and behaviour of rumour alone. Furthermore, with

respect to the evaluation comparison of OSR and rumour this is based on a metric such as

the dropped packet ratio. ShoX simulator is used for the evolution and comparison of

proposed protocols.

1.6. Aims and Objectives

The main aim of this thesis is to research the problem of routing in wireless communication

networks particularly wireless sensor networks, and the constraints involved with routing

such as packet drop, for example.


7

Our second aim is to perform a detailed study of routing protocols which are either deployed

or proposed for WSNs.

Third, a comparative study and analysis of routing protocols can be done by means of

network simulator. Network performance can be measured against metric like the dropped

packet ratio using ShoX simulator. Furthermore, we also aim to provide a precise guideline

tool to the developer and researcher with regard to a selection of efficient routing protocols

in the future.

1.7. Assumption

Our assumption is based on routing protocols and their use in wireless sensor network

application, which is listed as,

• We further assume that our sensor network is homogenous by nature, where all nodes

are of equal size

• WSNs nodes assume that they know their local information, and are aware of their close

neighbours for the purpose of packet forwarding.

• Wireless sensor routing protocols assume that they know the destination address in

advance

• WSNs nodes can be mobile and stationary depending on the sensor application scenario.

• WSN motes for sensor application can be placed an unattended environment and we also

assume that these nodes have the features of being self-organising and fault-tolerant.

• WSN nodes are deployed in an outdoor plain environment and environmental and

atmospheric conditions are assumed at a normal level.

• WSN nodes transceivers use single channel and wireless antenna, are Omni-directional

and propagate isotropic signals in all direction

• We also assume that mobile nodes choose their speed randomly

• We further assume that the collision model we use is CSMA, 802.11g


8

1.8. Thesis Contribution

The core contributions of this thesis are as follows:

First, the thesis provides a detailed study of routing protocols which includes wireless

routing protocols and their architecture, design challenges, and other constraints.

Second, a comparative study of routing algorithms wireless sensor network can be presented

for the selected list of protocols in the WSN routing domain.

Third, an extensive number of simulation experiments and analysis can be performed using

ShoX simulation.

Fourth, the thesis would be a guideline tool for the selection of an efficient routing protocol

and would alleviate the work of the developer and researcher in the routing domain in the

near future.

1.9. Related Work

The routing has a significant function in computer networks both in terms of resource

management, traffic management and routing. There are a number of routing strategies

which are used such as the shortest path algorithm, optimal routing and so forth. The

network‟s performance level depends on the number of routing factors such as throughput,

packet overhead, delay, congestion, and so on. [AX5]

Routing has been an interesting subject for last twenty years or more and significant

material is available on the subject. Routing has experienced different types of loops such as

forwarding loop, information loop and trace-rout loop and many of the routing algorithms

have a pre-avoiding loops mechanism. [AX6]

The wireless senor network consists of tiny sensory nodes and these have the capability to

maintain the network topology dynamically. The sensory nodes can be mobile but this

depends on their application. Routing in a wireless sensor network is challenging because it

does not involve a proper infrastructure. Energy, hardware and software resources are

limited which makes routing difficult. The network performance can be measured by a

number of factors such as throughput, network delay and so on. A number of routing

protocols was proposed for the wireless sensor network such as AODV, OLSR, DYMO and

many more. [AX7]


9

1.11. Organization of the Thesis

Our work is broadly planned as follows the thesis consists of six chapters. Chapter 1

presents the background of the thesis and problem definition. Chapter 2 presents the IP-

datagram routing protocols in detail. Chapter 3 presents routing protocols in a wireless

sensor network. Chapter 4 discusses design and simulation. Chapter 5 presents the

simulation results and analysis and finally Chapter 6 presents the conclusion of the whole

thesis, future work and bibliography.

Summary

In this chapter we introduced our project and the main statements of our research question

and an overview to wireless sensor network. In the next chapter we will discuss IP-datagram

protocols in details.


10

Chapter 2

Routing Protocols in IP-Datagram Networks

2.1. Introduction

This chapter presents a detailed study of routing protocols in the context of a best effort IP

network.

2.2. Routing

Routing is the product of routing protocols and routing algorithms. The routing protocol

gives the overview of the topology of the entire network while the routing algorithm adds

the power of intelligence to how one computes the path between multiple network nodes.

[swapnil]

Firstly, we will perform a detailed study of routing protocols. In the literature there is an

abundance of material available on routing and it is an extensive area of study in itself. In

data communication routing is the core feature of guiding and directing in large networks.

Furthermore, routing provides an optimal path according to the metrics mentioned.

Routing is broadly divided into two main classes: adaptive routing and non-adaptive

routing.

In static routing a route is not maintained automatically and needs to be updated manually.

In a situation where the algorithm fails to update, the only way to recover it is to restart (?)

the algorithm manually in order that it can accommodate itself within the specified network

link requirements.

In addition, there are more routing classes such as delta routing, multi-path routing and

hierarchal routing and so on.

2.3. Properties of Routing Protocols

There are a number of properties which routing protocols possess and they have a wide

impact on today‟s inter-connection networks. The author discusses this under the following

headings:

Connectivity: It is the responsibility to assign a route for a packet coming from a source

node to a destination node.


11

Adaptivity: The property guarantees that there should be an alternative path for each

and every packet in case of link or network device failure.

Fault Tolerance: The property sates that the fault tolerance can be attained by applying a

storing-and–forwarding technique to some nodes in two or more phases, while it can

also be accomplished through adaptivity but it is not always applicable and true.

Deadlock or live-lock freedom: The property states that there should be no blockade and

superfluous traffic in the network.

2.4. Classification of Routing Protocols

A „routing algorithm‟ can be defined as a methodology through which a node makes a

decision about its neighbouring route for the purpose of sending a packet to an expected

destination. A Routing Table stores the local link information and is updated from time to

time.

Ignasi Paredes Oliva defines the routing as “Driving packets from source node to destination

node in a network”. Routing algorithms play a pivotal role in today‟s computer networks.

And also with the exponential increase in computer networks and distributed systems and

other web applications it is obvious that the network traffic management and routing are the

core issues. [BX5] The central job of the routing algorithm is to generate a path for the

network packets. There are a number of routing algorithms mentioned in the literature, but

our study will focus on the routing algorithms which are of particular use for computer

inter-connection networks. We also focus on those routing algorithms which are important

for future studies in relation to modern computer inter-connection networks.

Jose, Sudhakar, Lionel state that routing algorithms can be classified in many ways; broadly

classify the routing algorithms according to the number of destination packets which need to

be delivered. A packet can be sent uni-cast or multi-cast.


12

Figure 2.1. Classsfication of routing proctols startegies in IP-Datagram networks

In data communication networks a routing algorithm can be classified as uni-cast,

broadcast, multi-cast and any-cast.

Alternatively, Sharam classifies routing algorithms as flooding, static and dynamic routing.

1. Uni-cast Routing

In a uni-cast routing algorithm a packet is destined only for one specific destination from a

source.

2. Broadcast Routing

In broadcasting a message is broadcast to all available links in the network, the reason being

that the medium of communication is shared between all the network nodes.

The N point-to-point algorithm is a broadcast algorithm and it sends packets to every

destination. The limitation is that it wastage of bandwidth and have the pre-knowledge of all

destinations.


13

The next broadcast algorithm is „flooding‟. The packet is sent to every host in the network.

There is a strong likelihood that some hosts will receive dual packets and because of this

mechanism the duplication arises in the network but the algorithm can detect the

duplication of packets.

3. Multi-cast Routing

The main goal in the multi-cast routing is to send packets to a designated number of network

hosts but not all.

4. Any-cast Routing

The main goal in the any-cast routing is to send packets to a particular single designated

computer in the network.

According to Mischa Schwarz the routing algorithm is divided into four classes,

Firstly, the routing path selection function and routing table creation can be performed in a

centralised and decentralised or distributed approach.

Secondly, the routing mechanism can follow an adaptive approach. n this approach the

routing path information is updated when changes occur in the network topology. The

routing mechanism adapts itself according to the topological, traffic changes taking place in

the network. It dynamically responds to all sorts of traffic conditions happening in the

network.

Thirdly, a class of routing algorithm uses cost as a metric to link nodes for the purpose of

routing path selection. In regard to cost calculation there are a number of parameters usually

used such as network link bandwidth, link length, speed, expected delay, latency, level of

security and so on. The cost can be a combination of multiple parameters such as link

bandwidth and delay.

Fourthly, it is one of the best known and popular types of routing algorithms studied in the

literature which works on the basis of performance and it is called „least-cost path

algorithm‟ or „shortest-path algorithm‟. In the shortest path algorithmic approach the least

cost path can be defined as the linear sum of the hops between source and destination.

Furthermore, it also involves adaptive routing which includes full adaptive and partial

adaptive algorithms because in these algorithms the link cost can be dynamically changed

due to delay, error condition or speed and so on. The shortest path algorithmic mechanism is

widely used in the routing of datagram communication networks.


14

In performance base routing algorithms the next category involves the use of a number of

techniques to mitigate the time delay, network utilization and also use an estimated metric

between source and destination. It further evaluates the performance between source and

destination by using multiple paths.

2.5. Goals of Routing Protocols

[Tanenbaum], suggested that the following properties in relation to routing algorithms need

to be taken into consideration in order to reach a solution to the routing problem:,

Correctness: The property states that an algorithm should be able to accommodate itself

within topological variation and network problematic conditions. The algorithm should

also be able to find the optimal path in any circumstances for the network.

Simplicity: The property states that an algorithm needs to be simple, efficient and easy

to implement.

Robustness: It is crucial that the network should have the capability to work in a

situation like node failure, path congestion, like failure and so forth.

Stability: The property states that after specified runs of the time-frame window an

algorithm needs to be in a stable position.

Fairness: The property states that for the delivery of a packet, the packet delivery time

schedule must be followed.

Optimality: The property states that an optimal path should be found from a source to

destination. The optimal path depends on the network parameter. It is not compulsory

that an optimal must always be the shortest path, for instance a longest path can be

considered an optimal path because of less buffer delay for example.

Scalability: The property states that an algorithm needs to be capable of improving and

giving its best performance as the network resources expand and traffic grows.

In the commuter network literature the routing algorithm can also be classified into non-

adaptive routing and distributive adaptive routing.

We further categorise the routing algorithm into two main groups, which are stated as,

There are a number of routing algorithms which exist in the network routing domain but our

research focuses on the set of conventional routing algorithms and an advance self-adjusting

Q- learning routing algorithm and routing algorithm.


15

2.6. Conventional Routing Protocols

2.6.1. Adaptive Routing Protocols

In today‟s global computer networks two routing algorithms are used, the shortest path and

distance vector. These algorithms provide the foundation for many routing protocols which

are deployed on the internet today. The routing information protocol (RIP) and interior

gateway routing protocol (IGRP) are built upon the distance vector algorithm‟s updated

version. OSPF are built on the basis of the shortest path algorithm while EIGRP is the basis

of the on dual routing algorithm. BGP is also based on path vector algorithm which is an

improved version of distance vector algorithm.

2.6.2. Open Shortest Path First (OSPF)

The Open shortest path first (OSPF) is a link state routing protocol. The protocol was

developed on the basis of Dijikstra shortest path first algorithm. The link state routing

strategy is also used in the IS-IS routing protocol. In the link-state base routing each

network node develops its own map of network topology.

OSPF is an intra-domain routing protocol and operates inside a single autonomous system

and is the most dominant interior gateway protocol and is largely used in large

organizational networks. OSPF is compatible with the variable link subnet mask VLSM and

CIDR IP-subnet addressing mechanism.

The core concept in OSPF is that every network node has knowledge of the whole network

topology.

OSPF Areas

OSPF divides the network routing domain into several areas and the initial area is called

Area 0. A backbone is required when a packet is forwarded into another routing area.

OSPF Network Classes

The OSPF routing protocol divides the network into a number of classes such as non-

broadcast multi-access, multi-access, point to point and so on.

OSPF Timers

OSPF uses a number of timers, for instance if links are down for less than 30 seconds and

then recovered again so in this case it is not noticed. When a link goes down for half or less


16

than half an hour, then in this case OSPF floods LSA for both events including both the up

and down status of a link.

The advantages and disadvantages are described as follows:

1. Advantages

• Performs fast convergence.

• Provides loop free routing paths.

• It is scalable and does not restrict network to hop counts limits.

• Updates occur every 30 seconds and there are no other updates.

• OSPF sends hello packets for the purpose of checking the status of the link and does

not send a full routing table.

2. Drawbacks

• OSPF is very expensive in terms of memory and computational power.

2.6.1.1. Shortest Path Routing Algorithm

There are a number of algorithms in the network literature for finding the shortest path

algorithm, but our study will only focus on the Dijiksta algorithm in this shortest path

algorithm domain. Let s is source and u is the destination, and the shortest path from s to u

can be described as,

Figure 2.2: shortest path finding mechanism

1. Dijikstara Algorithm

The Dijaskrta algorithm in the literature is also termed as the shortest path or Greedy

Algorithm. It is the most accepted algorithm because of its enormous use in today‟s internet

and because of its simplicity and ease of use. In terms of its route computation it uses cost

metric. The cost metric differs as it can be computed by a number of distinct metrics such as

delay, bandwidth, data rate and financial cost and so forth. The route optimization is the


17

core functionality of the Dijikstar algorithm. It optimises the routing to an optimal path by

performing computation at each node of network. [Firat]

Furthermore, a known limitation of the Dijikstara algorithm is that it selects an optimal path

irrespective of the future network needs such as congestion on a path or link failure and it is

obvious that current global networks are dynamic and distributive in nature. For example, if

there is a queue of packets at of the middle network node buffer, and as a result the packets

are delayed for a particular destination, in this situation it would be better to go for an

alternative path but there is no guarantee that an alternative path would be the shortest and

as a result it can achieve the shortest time delivery but not the shortest path. [F. Tekiner

et al].

Graphical Representation of Dijikstra Algorithm

The Dijikstra algorithm can be graphically represented as; in the networks each node

computes its routes to other connected nodes. The scenario is represented with a diagram

where each node is considered as an edge and link as a vertex, a vertex connecting two

edges. In relation to the cost of the vertices it is taken as non-negative.

Let us consider that V is the set of all edges in the digraph, the array of the cost metrics of

the vertices is c[s, w], and the array to least cost path for any node is D[w] which is to be

computed. In the first step, the value for the shortest path to any edges in the digraph can be

assumed as infinity i.e. D[w] =∞. Furthermore, let us consider that there are no edges in the

digraph and every edge s starts its computation for the shortest path cost D[w] to connect to

other neighbour w ∈ (V-s), with a minimal c[s, w] value. Next, it computes the routes for

the other edges v ϵ (V-s-w), and is a neighbour of w, consequently the resultant equation

becomes:

D[v]= min(D[v], D[w]+c[w, v])

Where c[w, v] is the vertices cost metric connected to w, the relaxing nodes process is

continued until all the digraph edges are visited. On completion of the process all the edges

have the shortest path to each other node in the network.

Furthermore, to make sure not to recalculate the path for visited nodes, for this purpose

these edges are marked to mitigate the chance of recalculation of the path. In case there is

an e number and n number of vertices in the digraph the computational complexity for the

Dijikstra shortest path algorithm is:


18

O(e log N), and N is the number of Nodes. [Firat][F. Tekiner et al].

Dijikstra Algorithm Pseudo-code

Merin stated the Pseudo-code for Dijikstra algorithm is: which are quoted as “

Procedure Dijsktra (V: set of vertices 1... n {Vertex 1 is the source}

Adj[1…n] of adjacency lists;

EdgeCost(u, w): edge – cost functions;)

Var: sDist[1…n] of path costs from source (vertex 1); {sDist[j] will be equal to

the length of the shortest path to j}

Begin:

Initialize

{Create a virtual set Frontier to store i where sDist[i] is already fully solved}

Create empty Priority Queue New Frontier;

sDist[1]←0; {The distance to the source is zero}

forall vertices w in V – {1} do {no edges have been explored yet}

sDist[w]←∞

end for;

Fill New Frontier with vertices w in V organized by priorities sDist[w];

End Initialize;

repeat

v←DeleteMin{New Frontier}; {v is the new closest; sDist[v] is already correct}

for all of the neighbors w in Adj[v] do

if sDist[w]>sDist[v] +EdgeCost(v,w) then

sDist[w]←sDist[v] +EdgeCost(v,w)

update w in New Frontier {with new priority sDist[w]}

endif (end of?)

endfor (one word or two?)

until New Frontier is empty”. [Merin].


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2.6.1.2. Related Algorithm

1. A* Algorithm

A star is an algorithm from a graph tree class and it computes the path from a source node to

a destination node and for the computation of a path it uses a heuristics estimate h(x). It

computes the best path to a given node on the basis of these estimates. It visits every node to

find the best path to fulfil the heurists estimate criteria.

2. Prime Algorithm

A prime algorithm is also termed a DJP algorithm. I, it computes the minimum shortest path

in a connected weighted graph. It works on the concept of searching for subset edges that to

make a tree in order to minimize total weighted edges in the tree. [Merin]

2.7. Enhanced Interior Gateway Routing Protocol (EIGRP)

EIGRP is an interior gateway routing protocol and was developed by Cisco. The EIGRP

protocol is built on the basis of a Diffusion Update Algorithm (DUAL). EIGRP does

optimization in terms of topological changes, computational power and memory

consumption.

EIGRP Tables

Neighbours Table: In this table it stores the routing information associated with their close

neighbours interface.

Advantages

i. Provides a fast convergence mechanism

ii. Guaranteed loop-free routing path

Diffusion Update Algorithm (DUAL)

J.J.Garcia Luna Aceves developed the DUAL algorithm at the Stanford Research Institute.

The DUAL algorithm has the capability to respond dynamically to topological changes and

automatically adjust the routing table entries on each individual router. The DUAL

algorithm performs diffusion computation and guarantee loop free routing.


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2.7.1. Link-state-based Routing

The link state routing algorithm is the replacement of the distance vector routing algorithm

in the early 1970‟s by ARPANET. Distance vector routing has suffered from two major

problems which are delays and count-to-infinity problems. Today, a number of variants of

the link state routing algorithm are widely deployed in internetworks.

Tenanbuam describes central points and the philosophy of the link state algorithm as,

First, LS algorithms discover the location of the neighbour node and like to learn their

address.

Second, calculate the cost or delay to each of its neighbours.

Third, to build a packet and inform the other neighbours what it has learned.

Fourth, distribute the packet to all other neighbour routers.

Fifth, calculate the shortest path to each neighbour.

ink state routing algorithm works on a mechanism, a router or node advertising its local

links to its neighbour unlike the distance vector algorithm‟s incremental distance

computation.

Furthermore, the link state algorithm uses a topological database. It is also termed as a

routing table. The topological database is the core source for network mapping; it further

forms a graph of the interconnected networks. In the graph each node and its interconnected

link is represented in the network. In the network each node retains and tallies its routing

table entries with the neighbour router, and attached links.[Firat]

It uses Dijikstra Shortest path algorithm for the least cost path computation, and looks after

the routing table or topological database. In regards to LSA exchanges it uses a static

algorithm called flooding; it sends the incoming packets to each outgoing link except its

own link. The known disadvantage of the conventional flooding algorithm is the duplication

of packets; an alternative technique for more practical flooding is also used and is called

„selective flooding‟. [Tenanbuam].

Link-state Routing Protocols Pseudo-code

Firtat et al (No reference?) describes the pseudo-code for the link state algorithm which is

quoted as,

Set Dij to infinity for all j not equal to i


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Loop N

Find the node K not in N for which Dik is smallest

Add K to N

For each j not in N

If Dik + Dkj < Dij then

Set Dij to Dik+ Dkj

Set next_hop(i,j) to next_hop(i,k) concattensted with link from k to j

2.8. Border Gateway Protocol (BGP) or Path Vector- based Routing

The Path vector routing algorithm was the replacement for the previous DARPANET

distance vector algorithm. The core reasons for the development of the path vector

algorithm were to use the vector to eradicate the problem of the loop unlike the distance

vector algorithm. It belongs to a class of inter-domain routing algorithms, in relation to

routing p; the border gateway protocol (BGP) is built on the base over path vector routing

algorithm.

In BGP the best-path algorithm always received a multiple number of paths to the same

destination, then further the best-path algorithm making decision on which path must be

installed in the routing table.

Path vector algorithm mechanics

Firstly, of all BGP choose the valid path as is the best path and then further compare it with

the path that comes first in the list and continue until the valid path is finished.

Next it follows the rules stated for the election of best path, which are the following:

Rule 1: Pick the path with the highest weight

Rule 2: Choose the path with the highest local preference LOCAL_PREF

Rule 3: Preference will be given to the path that was locally originated via a network

Rule 4: Preference would be given to the path which has the shortest autonomous path

AS_PATH

Rule 5: Preference would be given to the shortest path which has the lowest origin type

Rule 6: Preference would be given to the path which has the lowest multi-exit discriminator

(MED)


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Rule 7: Preference would be given to eBGP over iBGP paths

Rule 8: Preference would be given to the path which has the lowest BGP metric to the

subsequent BGP hop.

Rule 9: In case of multi-path make certain that if is there is any need for installation in the

IP routing table for multiple paths.

Rule 10: In a case where if both are external paths then preference would be given to the

path which came first in the order.

Rule 11: Preference will be given to the path which comes from the BGP router and has the

lowest router ID.

Rule 12: In the case where the router ID is similar for multi-path, then preference will be

given to the path which has a minimum cluster list length.

Rule 13: Preference would be given to the path which has a lowest neighbour address.

BGP beast Path selection routing algorithm flow chart representation:

Limitation of Path Vector Routing Problem

Analysis and measurement showed that a packet forwarding loop exists in the inter-domain

routing. But the exact reason behind the looping problem has not yet been discovered

because of the size and convolution of the internet. Paxion performed a number of end-to-

end trace-route experiments in 1994 and 1995 and found that a transient loops exists in the

internet.

2.9. Advance Routing Approaches

2.9.1. Self-Adjusting Routing Protocols

The core disadvantage of a conventional routing algorithm is that of human intervention or

algorithm designer supervision in the case of some unusual event or occurrence in the

operation of an algorithm such as failure or link congestion and so forth and consequently,

these algorithms are not able to adjust dynamically.

Moreover, the conventional routing algorithm has a lack of intelligence and it needs human

help and supervision for adjustment in problematic conditions and network topological

changes.

The problems of routing have been studied in the research domain for a long time. The

routing problem comes within the scope of the NP-complete problem. The problem is very


23

similar to sale-man route optimization problem. The need for an intelligence routing

algorithm to adapt and adjust changes dynamically is introduced. With this approach not

only does it boost the performance presentation of an algorithm it also improves the

adaptability for associated changes in the network.

With an advance learning routing approach the algorithm can automatically adopt and

maintain its performance for an event which is not described by the algorithm designer. This

routing learning base algorithm is more appropriate for a communication network where

topological variances are unforeseen and unpredictable. Nevertheless, the introduction of

this sort of algorithm involves a couple of constraints such as, more time is needed for

learning and also in terms of memory storage.

2.9.1.1. Reinforcement Learning Routing Protocols

Reinforcement learning (RL) is a form of learning in which we interact with an environment

by carrying out some action and as a result learn from that action. In this regard it is the

opposite of the traditional teaching method. Furthermore, RL is a form of learning where an

agent undertakes trial and error and trail interaction in a dynamic environment. Although,

RL is an old field but it has attracted much attention in the last decade from machine

learning and the artificial intelligence domain.

The central concept of RL is the methodology of an agent reward and punishment approach.

There is no pre-information stored for an agent, but it learns an input from interaction with

the environment and after processing the input data it returns the output to the environment.

As a result the state of the environment is updated. There is a possibility that an agent may

take further action, but this is totally dependent upon the input. The aforesaid methodology

is very useful in a situation where there is no supervised learning for an agent.

The RL mechanism would not be called an algorithm or protocol but in reality this is a very

powerful initiative for the solution of some particular class of problems. The RL

methodology can be further divided into two main streams of mechanisms i.e. a genetic

algorithm and genetic programming, and statistical techniques and dynamic programming.

[David Kelley].

In addition, a reinforcement learning base routing algorithm does not require the pre-

knowledge of the network topology and network traffic pattern and is even capable of

working out the best routing policy without the need for any centralised routing control

system. [Michael Littman et al].


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However, the approach of reinforcement learning can be very highly-priced in terms of

space and storage but there are different variants of reinforcement learning algorithms

available. The one that we are selecting is the Q-learning algorithm and, in terms of space, it

requires a little bit more space for the representation of a full routing policy. [Michael

Littman et al].

2.9.1.1.1. Q- Routing Protocols

The Q-learning is a mechanism of solving a problem on the basis of the reinforcement

learning philosophy; it is used for the solution of the problem which involves a poly-

hierarchy of decisions. Q-learning can also be called an incremental edition of dynamic

programming for the aforesaid problem.

In relation to an adaptive routing problem, the Q-learning mechanism working in a model

base make-up and the routing organizer does not have pre-knowledge of the topology in this

type of environment. Meanwhile the routing organiser has the priority to minimise the

average packet delivery timeframe and particularly in the case of a dynamic environment

where changes always occur it is extremely difficult to work out an optimal routing policy.

In Q-routing each individual in the network is a configured Q-routing algorithm mechanism.

Q-Routing Protocols Mechanics

The core steps of algorithm are quoted from David Kelly‟s research paper, and are as

follows:

Step 1: Initialization with 0‟s or random values

Q(s,a) for all s S and for all a A(s)

Step 2: Reiterate for each occurrence

Step 3: Initialise

Step 4: Reiterate for each step occurrence

Step 5: Choose a from s using a policy derived from Q

Step 6: Take action a, observe resultant state s‟ and the reward r

Step 7: Q(s,a) Q(s,a) + [r + maxa‟Q(s‟,a‟)- Q(s,a)]

Step 8: s s‟; Until s is terminal.


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2.9.1.2. Ant Colony Optimization (ACO) Routing Approach

Three are numerous insects living on the surface of the earth and one of them is the ant

family. Ant species‟ categories are numbered at 9000. These ants have unique intelligence

and smart characteristics which enable them to live in large groups in vast numbers, and

they can literally be found everywhere across the globe. An Ant Colony Optimization

Algorithm (ACO) is an algorithmic technique through which we can work out the reduced

path in the graphs.

In the recent past, the ant‟s model of organization and its associated interaction with the

environment were researched and computer scientist and engineers. The focus of attention

was the ant‟s unique features, such as their distributed control mechanism, fault tolerance

approach, environment base interactive communication, individual level automaticity, and

self-organization, strategies for collectivism and cooperation and the emergence complex

behaviours, and a set of unique skills at each individual ant level. The aforesaid features of

the ant societies make them an inspiration for a new multi-agent system and a way forward

for the developments of new algorithms. [Gianni Di Caro]

2.9.2.1.1. Ant-Net Adaptive Routing Protocol

The protocol is useful for situation where there are simultaneous changes occurring in the

network topology such as in the internet. The Ant-Net is an adaptive routing algorithm and

is based on the ant colony optimization techniques. The Ant-Net algorithm uses the concept

of stigmergy. While stigmergy is a communication mechanism where communication is

taking place between the individuals and the local environment it is also being modified

during the communication timeframe. To find the shortest path real ants make their routing

policy and decisions on the basis of local information which in this case is the pheromone

path dropped by the ants which has already passed through the same path.

Ant Net Protocol Mechanics

The Ant-Net uses two same groups of mobile artificial agents, which are forward and

backward.

Step 1: A forward agent is issued at regular intervals from source node S and a destination

node D is picked at arbitrary way, and the destination node fully meets the quality

requirements of the traffic pattern.


26

Step 2: The forward and select function-making decisions on the basis of information

available in the routing table, and the selection of a node is based on the associated

probability, decency, and in the case where if all neighbours are visited then a random

uniform approach is followed for the selection of the next hop neighbour and is irrespective

of the local queue.

Step 3: In a situation where a link is not accessible, then forward waits in a local queue and

is served on the basis of FIFO. [BX16]

Summary:

In this chapter we discussed both conventional and advance routing protocols in terms of IP-

datagram routing protocols. In next chapter we will discuss routing protocols in terms of

wireless Ad-hoc and sensor network in details.


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Chapter 3

Routing Protocols in Wireless Ad-Hoc and Sensor Network

3.1. Introduction

The chapter presents wireless ad-hoc and sensor routing protocols in detail. Study involves

both conventional and advance routing algorithm. Our study will focus on routing

mechanisms and different strategies.

3.2. Classification of Wireless Network

Data communication networks are rapidly evolving. In the past decade the wireless network

has begun to reach its peak point because of its consistent usage in several applications, and

products. Peter et al further classify the evolving network into four categories which are,

peer-to-peer, mobile Ad-hoc and wireless sensor network.

Wireless Ad Hoc Network

The ad-hoc networks fulfil both definitions, because they can be prepared from resources

which are currently available and are structured according to the need of each specific user.

Ad-hoc networks are also called „mesh networks‟ for the reason that the structure of the

network is organised in a manner designed to discover a pathway for a data to be routed

from source to expected destination.

Mobile Ad-Hoc Networks (MANET)

MANET can be considered a type of Ad Hoc wireless network. This MANET type of

network does self-configuration of mobile routers and the related hosts with their connected

links. As a result, it makes an arbitrary topology, and the routers are fully independent at

times and accommodate themselves in an arbitrary environment where the wireless network

topology is rapidly and unpredictably changing.

Wireless Sensor Network (WSN)

WSN is a collection of small sensor nodes which are dispersed in a large geographical area

for the purpose of monitoring and recording real world physical events and the nodes send

collected data consistently to their central base station. Nowadays, this is widely used for


28

measuring environmental conditions such as temperature, humidity, pressure, air speed and

many more.

Mobile Wireless Sensor Network

It is a version of the wireless sensor network and the nodes in this sort of network are

mobile.

Ad-Hoc Wireless Sensor Network

It is a version of wireless sensor network in which the nodes can organise themselves

automatically when any change occurs in the topology.

Peer-to-Peer Network

A type of computer network having no consideration of clients and server and the same

equal nodes can function both as server and client. [CX2].

The above listed networks types and their interconnection with other networks is

diagrammatically represented below:

Figure 3.1. Block diagram of wireless network classification


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3.3. Conventional Routing Algorithm in Ad-Hoc Network

Wireless Routing protocols can be classified in a number of ways, such as, node centric,

geocentric, data-centric, and QoS routing protocols. They can also be classified as reactive

or proactive. [CX10]

Wireless ad-hoc network protocols consist of two main classes which are „wireless sensor‟

and „Mobile Ad-Hoc Network‟ (MANET). In wireless networking the routing can be node-

centric. The argument is that the destination is investigated by calculating the number of

nodes. It is also important to note that communication in a wireless sensor network is not

always node-centric. The wireless sensor network is found to be more geo-centric or data-

centric. [CX10]

Furthermore, we are investigating distinct routing protocols which are related to wireless

networks as a whole. Below is a chart representation of traditional ad-hoc and WSNs

routing protocols.

Figure 3.2. Ad-hoc routing protocols classification chart


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3.3.1. Optimal Spine Routing (OSR) Algorithm

3.3.1.1. Introduction

The spine based routing algorithm was developed to minimise the overheads involved with

the shortest path routing algorithm and non-optimal routes computation in an on-demand

routing algorithm.

OSR is a spine-based routing algorithm and it stores global routing information in spinal

nodes and computes the shortest path between any two nodes. When a link comes alive an

add wave is generated while when a link comes off, as a result a delete wave is propagated.

The add wave effect is propagated but involves a slight delay while a delete wave is

propagated without delay. When a delete wave is in process and at the same time an add

wave is generated automatically so they cancel each other out and their effect is not

propagated. Consequently, the joint effect of add and delete waves ensures only stable

information is passed to spinal nodes. The OSR can be classified as the minimum weight

path routing algorithm. [CX24]

3.3.1.2. OSR Detail

To find an optimal path OSR use a spine (virtual backbone) and the routes are kept up-to-

date using query requests to sources. However, due to high overhead OSR is not a practical

solution to the problem of routing.

The topology is represented using undirected graphs G= (V,E), in conjunction with m edges

and n nodes where V represents hosts in an ad-hoc network and E represents the wireless

radio transmission range between the hosts. When changes occur in the topology, the

changes are associated with either V or E. Topological changes occur usually when a node

is inserted, deleted, or when an edge is inserted or deleted. A node can move from one part

of G to another part of G.

OSR collects global topological information on the spine graph G and the information is

passed to all spine nodes which further trigger the computation of the shortest path which is

based on the local knowledge of the spine graph G. Typically, the path is computed by

means of spine nodes which do not go across the spine. In OSR the spine is constructed

using an approximation algorithm called a minimum connection domination set (MCDS),

while MCDS findings path comes under NP-Complete problem. The MCDS algorithm we

are using for spine construction is distributive in nature. There is a possibility that MCDS


31

nodes can be unknowingly the interior nodes of a maximum leaf spanning tree. Spine can be

considered as the interior nodes of the tree.[CX24]

For instance, OSR single node movements using MCDS are calculates as,

Figure 3.3. OSR single node movements

The figure shows that the MCDS algorithm computes that nodes 3 and 6 are in the minimal

domination set (MDS) and a further MST connects to node 5 to sub-graph C. Further, we

assume that nodes know their id and their neighbours id, connect edges and the degree

involves.[CX24]

3.3.1.3. Generic key phases involves in any spine base routing algorithm

Phase 1-Spine Construction

The whole network consists of a single spine which is computed on the basis of global

topological latest information or maybe partial information.

Phase 2 -Aggregation of states into spine nodes

The aggregation consists of two steps, first non-spine nodes aggregated to their relevant

denominators and secondly, spine nodes aggregated with other spine nodes in the spine.


32

Phase 3 -Route Discovery

Route discovery can be performed in three ways, first fully localised information, second,

use a probe based technique and third a combination of both stated techniques.

Phase 4 -State Maintenance

Sate maintenance is consist of two steps, first, maintenance can be done by means of even

based updates , second, maintenance can be done by means of periodic updates. [CX24]

3.3.1.4. OSR Routing Algorithm Mechanics

First calculate the spine C, where C⊆ V.

Second, collect information from non-spine nodes and pass it to spine nodes.

Third, broadcast the resulting topology to all spine nodes.

Fourth, compute the shortest path by using global topology information received in step

three.

Fifth, all the sources receive latest routes updates.

Sixth, an event-related update is broadcast for the purpose of performing maintenance in a

situation like node insertion or deletion.

Seventh, periodic updates are broadcast to make sure that the topology has the latest

information available.[CX24]

3.3.1.5. Core Issues with Spine-Based Routing

• Spine maintenance (at global level)

The spine maintenance issue determines how the spine is constructed and how the spine can

be maintained during spine nodes movements or during mobility

• State level maintenance (at node level)

State level maintenance determines what information is collected form the whole domain

and what information is broadcast to spine nodes.

• Route Discovery

Route discovery determines that the discovery of routes involves only local information or

that/if it is probe-based. [CX 24]


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3.3.1.6. Uses of spine

1. To track changes in the topology and compute the routes

2. To offer short-term back up to fault-tolerant routes

3. To offer multi-cast backbone for multi-cast routes

3.3.1.7. Spine- Based Routing Algorithm are Classification

Figure 3.4. Classification of spinal routing algorithm

3.3.2. Wireless Routing Protocol (WRP)

WRP is uni-cast routing protocol for the Mobile Ad-Hoc NETwork (MANET). The WRP

protocol belongs to a group of routing table-based protocols. It holds and maintains whole

network link information in the routing table. It is a proactive protocol and belongs to a

class of path finding algorithm. In relation to nodes existence, the nodes learn about their

neighbour by means of acknowledgment receipt and some other messages. In a situation

where no messages have been padded so in this scenario the nodes are required to send at

least „Hello‟ messages in an episodic manner for the purpose of ensuring network

connectivity.


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3.3.3. Global State Routing (GSR) Protocol

Global state routing (GSR) is a based on a proactive, steady and topological precise routing

mechanism. It works on the mechanics of link state algorithms in which each neighbour

passes their routing table update, whenever changes happen in the network topology.

Furthermore, each adjacent network nodes episodically send the complete routing table to

their closest neighbour. The link state topological table consists of most recent local node

connectivity updates and also its contemporary link state up-to-date information about the

full complete network topology. In terms of destination table entry, an entry is checked

according to sequence number. When a received sequence number is greater than the current

the entry is updated in the destination topological table. And for the shortest path algorithm

to determine a shortest path for each entry in the routing table and further, it depends upon

the topological table information and to perform this task a shortest path algorithm called

Dijikstra, is selected.

To narrow our research further we would particularly focus on the wireless sensor network

in more detail;

3.4. Wireless Sensor Network

A wireless senor network is the latest and fastest growing technology and is expected to

revolutionise a wide range of applications in terms of its quality and availability in the near

future. The technology has arrived because of the huge advancements over the past decades,

particularly in the field of embedded microprocessors, MEMS sensor and wireless

communication [CX9].

Is a wireless computer network which can be formed in a large span of space , having

distributed autonomous devices which consist of sensors so as to monitor the physical

environmental condition, meaning pressure, temperature, motion and so forth at a remote

location. Furthermore, according to ad-hoc network classification, WSNs can be called

infrastructure-less networks. [ K. E. Kannammal et al]

A wireless sensor network is a group of network nodes which collaborate with each other in

a sophisticated fashion. In WSNs each node has its own sort of „poly sort of memory‟, such

as data and flash memories, program; and it also accommodates devices, for instance a

microcontroller, CPU or DSP chips. It also consists of an RF transceiver and this typically


35

involves a single –Omni-direction. Moreover, nodes in WSNs have their own power source

such as batteries and solar cells and they also host a number of sensors and accumulators.

Today, a modern wireless sensor network can be expanded to large geographical areas via

cheap sensor devices which can sustain themselves with very a low power usage. The

networking capability enables these sensor nodes to incorporate, collaborate and coordinate

with each other and this is a fundamental shift in the field of networks which differentiates

sensor network nodes form other networks. It is expected that in the twenty-first century our

lives will be more impacted by the advances in wireless sensor network technology because

of the elegant work which has been done particularly in the field of sensing,

microelectronics, digital signal processing, analog, and wireless sensor wireless technology

by researchers in recent years.

The adoption of a different design paradigm followed by wireless senor network makes it

different from the conventional network paradigm such as the internet. The wireless sensor

network is application specific; hence it is designed and deployed for a particular rationale.

[F. Akyildiz]

The network architecture of a wireless sensor network is broadly divided into two types of

single-hop and multi-hop architecture.

3.4.1. Network Characteristics, Design Objectives

It is obvious that due to the non-infrastructure nature of WSNs and their application

specification has a huge impact on the network characteristics, design and performance.

3.4.1.1. Network Characteristics

WSNs have a different set of characteristics in comparison with conventional wireless

communication networks such as MANET, cellular, and so on, and these are listed as,

• Dense sensor node deployment

In WSNs the nodes are densely deployed and in terms of levels of magnitude, are far higher

than those of MANET and others.


36

• Battery-powered sensor nodes

The WSNs‟ nodes or motes are deployed in a non-friendly environment where there is no

possibility for recharging or replacement of battery

• Severe energy, computation, and storage constraints

In WSNs there is a limited amount of energy, computation and storage available because of

the size and capability of sensor motes

• Self-configurable

The sensor motes deployed in the field are mostly on a random basis and these sensor motes

have the capability to configure themselves dynamically whenever connected to a sensor

network.

• Unreliable Sensor Nodes

The WSNs motes are highly vulnerable to error and fault, because of their deployment in a

harsh or non-friendly environment

• Data Redundancy

The sensor motes are deployed in a specific area for a particular sensing task, so at the same

time multiple sensor motes are sensing data from the same area or region and as a result

there are appointed tolerable levels of redundancy

• Application Specific

WSNs motes are widely deployed for a particular application and the network design

specification is altered with an expected application.

• Many-to-one traffic pattern

In a typical sensor network application many source nodes transmitting data to a sink node

and the resultant data traffic represents a many-to-one traffic pattern.• Frequent Topology

Change

The sensor network topology is rapidly changing because of number issues such as channel

fading, node failure and damage, addition, and energy depletion.


37

3.4.2. Network Design Objectives

WSNs are conventionally application specific and the design objectives of the network

change from an appellation to application and therefore the subsequent objectives need to be

chosen for WSNs design,

•Small node size

WSNs networks nodes are usually deployed in a non-friendly environment and the size

minimization can give us advantages, such as node cost reduction, and easy deployment,

and less power consumption.

• Low node cast

It is crucial to keep the cost of the overall network as low as possible and the nodes in

WSNs are not reusable because they are deployed in a hostile environment where access to

a sensor field is not possible

• Low Energy Consumption

In WSNs it is important to reduce per node energy consumption for the purpose of

extending the life of nodes and the overall sensor network; due to the nature of network

deployment it is not easy to recharge or replace the battery

• Reliability

It is important that the network protocol should be able to present data integrity and error

correction methodology, and reliable data transmission on an error-susceptible, and time-

altering wireless channels• Self-configurability

It is crucial that WSNs can automatically configure themselves in the event of node failure

or damage, and receive topological updates that preserve a proper connectivity

• Adaptability

WSNs are highly susceptible to node failure or in the case of joining and moving would

result in topology changes and so the routing protocol should be capable of successfully

dealing with the aforementioned situation

• Channel utilization

It is crucial that the routing protocols should be capable of utilizing the bandwidth in a most

cost-effective way because the resources in WSNs are limited so as to achieve better

utilization of a channel.


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• Fault Tolerance

WSNs are typically installed in a hostile environment in an unattended way and are highly

susceptible to error and failure, so it is crucial that sensor nodes should be capable of having

auto-configuration, auto-recovery, auto-repairing, auto-testing and auto-calibrating

• Security

In WSNs it is crucial to have security procedures to secure data from any authorised attacks

and ensure the integrity of the data in a node or network

• QoS Support

In WSNs the quality of the service depends on the application used and each specific

application has its own set of QOS requirements such as packet loss and delivery latency so

it is crucial that the communication protocol should keep these requirements under

consideration for each particular application.

3.4.3. WSNs Network Design Challenges

The design of the WSNs‟ routing protocols is a painstaking job due to the nature of the

network because of the counted resources and as a result WSNs are faced with a number of

constraints, such as energy consumption, CPU, bandwidth, and memory storage. The

challenges are as follows,

• Limited energy capacity

The WSNs‟ mote consists of small battery which has limited energy capacity. In an

environment which is unfriendly or hostile battery power sustenance is a serious challenge

and gaining access to sensor motes is almost impossible. When a sensor node reaches a

specific threshold value it considers the sensor motes faulty. Ultimately these motes have an

impact on the overall efficiency of the sensor network. Consequently, it is vital that routing

protocol must have intelligence to improve the overall life span and performance of the

sensor network.

• Sensor location

Sensor location management is also a known issue in the designing of routing protocols.

Modern routing protocols usually use the global positioning system (GPS) for location

learning or alternatively using some other location learning mechanism


39

• Limited hardware resources

WSNs have limited hardware resources such as computational processing power, memory

and energy capacity and due to these resource constraints software development and the

designing of routing protocol for WSNs becomes a difficult task

• Massive and random node deployment

WSNs nodes are typically deployed in a random fashion in large numbers in an unfriendly

environment and rely on application; furthermore it also has an impact on the routing

protocols‟ performance and overall functionality. The sensor nodes are usually dropped over

an enemy territory on a large scale and if the sensor nodes are not operating in a consistent

manner then clustering of sensor s nodes is a viable option for the purpose of saving energy

and improving the performance of overall WSNs.

• Network characteristics and unreliable environment

The WSN is consistently prone to topology changes because it is highly susceptible to node

failure, node damage, energy depletion, link failure, node addition, deletion and so on. In

addition it is also vulnerable to noise, time-inconsistency and errors. Consequently, in order

to find an optimal path it is crucial that a network-routing protocol is capable of maintaining

the topological changes and increasing the size of the network, and energy consumption

level, sensor nodes mobility and their related issues such as connectivity and coverage and

application specific requirements.

• Data Aggregation

In WSNs the redundancy of the data packets is a major concern. Therefore, it is crucial that

the same packets generated from a number of nodes can be aggregated for the sake of

downplaying the extra overhead of transmission traffic. Today a number of routing

protocols use data aggregation techniques to optimise the level of transmission and also to

improve the energy efficiency.

• Diverse sensing application requirements

WSNs are used for an unlimited number of applications and each individual application has

its own specification and constraints. Currently, there is no such routing protocol which can

fully qualify or work for every application but the task of a routing protocol is to compute

an optimal path and forward data as well as ensure the accuracy to the sink node.


40

• Scalability

In WSNs scalability is crucial because the network size may rapidly grow so the network

routing protocol needs to be able to work consistently where there is no proper

infrastructure, limited sensor nodes energy resources, nodes failure or damage, unreliable

wireless link, energy depletion and so on. Furthermore, there may be a situation where

communication is asymmetric and symmetric is not possible so it is important to keep these

factors in mind when designing the routing protocol.

3.5. Routing

A wireless sensor network (WSNs) is built up from a number of small sensor network nodes

and they are connected to each other via a wireless link which does not require a fixed

network infrastructure. In regard to the nodes structure of WSNs nodes structure it has a

short transmission range, small processing power and storage capacity and also has

inadequate energy resources. WSNs routing protocols have a crucial responsibility to

ensure there is a reliable, multi-hop communication between the nodes. WSN‟s require

sophisticated routing algorithms because energy is the core issue in these networks and low

power wireless devices are necessary to ensure that there is a low consumption of

energy.[CX10]

3.5.1. Routing in Wireless Sensor Network

Initially topology-based routing techniques were used in the wireless network. In the past a

number of proposals were based on a proactive routing mechanism that used to collect

information about all the available network paths, although those paths links were certainly

not used. Furthermore, in dynamics network topologies proactive routing does not

accommodate itself properly. An alternative technique was developed called „reactive

routing‟ which only keeps those paths which are presently in use. Recently, location aware

routing is introduced in which protocols know the physical location. Furthermore, a number

of geographic- or position-based routings are also proposed. Information about physical

location is required in advance; it can be taken from GPS or on the basis of a distance

estimation of incoming signals. Geographic routing and topology-based routing also address

the centric routing algorithm. There is another class of routing algorithm called data-centric

and it is an important routing paradigm for the wireless senor network. The data-centric

algorithm uses queries for the routing operation and the queries are written out by the sink

node in order to acquire the requested data.


41

Moreover, the routing algorithm in a wireless sensor network can also be classified

according to the usage of messages. A single path routing mechanism is used when there is

merely one instance of a message existing in the network at any given time. There are some

more routing algorithm techniques for WSNs such as partial flooding and multiplexing. In

addition to single path, a multi-path, partial, flooding-based routing strategy there is a

mechanism which is called the „guaranteed delivery routing algorithmic technique‟. The

routing algorithm can also be classified according to the nodes requirements for the

management of ongoing tasks in the state information and in the literature it is known as

memorization.[CX10]

Furthermore, WSN‟s routing algorithm is broadly divided into three main classes which are

flat routing, hierarchal routing and location-based routing.[ Emmanuel Sifakis]

The algorithm used for the wireless senor network is mostly borrowed from the Ad-Hoc

network. In the early days of the wireless sensor network a number of routing protocols

were taken from the wireless ad-hoc networks and mobile wireless networks. It is evident

that these protocols were built for a general wireless network and it involves no concern for

precise communication patterns of WSNs. Hence, the developments of WSNs-oriented new

routing techniques, and the customization of existing routing protocols represent a

significant area for future research.


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3.5.2. Classification of Wireless Senor Network Routing Protocols

Diagrammatical classification of a routing algorithm is shown here:

Figure 3.5. Hierarchical diagram of WSNs routing protocols classification

Wireless sensor network routing protocols are completely different from conventional

wireless routing protocols because the network is more vulnerable to issues like abrupt

change network topology, sensor node failure or damage, unreliable wireless network link,

energy depletion and so on. Furthermore, the routing protocols of WSNs are obliged to

follow cost-effective and strict energy requirements in order to fulfil the needs of overall

routing.


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WSNs routing protocols are broadly divided into seven categories which are stated in a

tabular form as follows,

Routing Category List of Routing Protocols

Flat/ Data centric Routing Protocols Rumour, information direct, EAD,

COUGAR, ACQUIRE, Directed

Diffusion, Gradient based routing, Home

agent based Dissemination, SPIN

Hierarchical Protocols TEEN, HEED, LEACH, PEGASIS,

APTEEN

Mobility Protocols Data MULES, TTDD, SEAD, Dynamic

Tree based Data Dissemination, Joint

Mobility and Routing

Multipath Protocols Braided Multipath, N to 1 Multipath

Discovery, Sensor Disjoint Mutipath

Heterogeneity Protocols CHR, IDSQ, CADR

Quality of Service (QoS)Protocols Energy aware routing, SPEED, SAR

Geographic Routing Protocols GAF, GPSR, GEAR, energy aware

routing

Table 3.1. Seven Categories of wireless sensor routing protocols

The objective behind the development of the routing algorithm is not only to reduce

overheads, increase throughput and minimise end-to-end delay but the other important goal

is the consumption of energy usage in a wireless sensor network.[M.Hadjila and M. Feham ]

Routing is one of the significant tasks in a wireless sensor network, and for this reason a

large amount of research material is available on this topic. The routing algorithm

constructed for IP networks and MANET is not working properly in the wireless sensor

network domain in comparison with the IP network that sends packets in a wire connection

and there is a slight chance the packet could be damaged but in the wireless sensor network

it is not the same. ire-less sensor network is one of the most significant technological

advances in this century. In the past decade it received an enormous focus from academia

and the industry across the globe. [Sshio et al]. The Wireless Sensor Network (WSN) is a

form of distributed wireless network. It is an amalgamation of a number of the latest

technologies, such as micro-mechanical technologies, distributed signal processing and an

embedded system, integrated microprocessor and wireless communication, ad-hoc

networks‟ routing protocols and so forth. [Shio et al.]


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The wireless sensor network technology consists of self–organised nodes which are widely

deployed in environmental conditions, wireless communication, military purpose, data

processing and so forth at a very low price. Nevertheless, WSN‟s technology requires a

capable mechanism for data processing and forwarding.

The core philosophy of WSN is that each node in the network has limited power which is

sufficient for the whole proposed project, for instance a node is sensing for military

surveillance or environmental monitoring and so forth.

In WSN routing protocols find the route between nodes and ensure the consistent

communication between the nodes in the network. The nodes are deployed in an ad-hoc

structure irrespective of vigilant planning and engineering. [Shio et al].

In WNS networks the routing contradicts the approaches taken in conventional wireless

communication networks. The reason behind this is that it does not have a proper

communication infrastructure, the node link is unreliable and on top of all these issues, there

is a tight energy consumption constraint and the routing protocol needs to work under these

adverse conditions. Currently, a number of wireless routing protocols are being developed in

the wireless communication domain. The routing protocols for the wireless sensor network

can be divided into seven categories which we will now discuss.

3.5.2.1. Rumour Routing

3.5.2.1.1. Basics

Rumour routing algorithm transmits queries when an event occurs in the network and data

can be retrieved from that particular node, while the routing mechanism is independent of

geographic information or addressing scheme such as IP addressing and so on. Rumour

routing is a trade-off between flooding overhead and delivery consistency and can also be

adjusted to other parameters. Rumour routing can be used in a situation where geographic

routing is not applicable or the coordinates don‟t match.[CX25]

An event is a sort of an abstract form of information from a specific group of sensor nodes

and furthermore an event is considered to be a localised phenomenon in a precise region.

A query can be explained as being a request for information for the purpose of accumulating

data. When a query arrives at its destination the gathered data is passed to the query

originator and when the query originator is satisfied with the collected data then the shortest


45

path can be discovered between source and destination. [CX 25]. Rumour routing can be

useful if it fulfils a certain threshold which is shown in the graph below:

Figure 3.6. Rumour routing chart representation

Nevertheless, finding the shortest path is not a priority for some applications. The specific

application may be interested in taking a small amount of data or may be interested in doing

some more attentive sensing at some targeted nodes. So for the stated situation, flooding

each query would not be an efficient option in comparison with delivering it on a non-

optimal path. On the other hand, flooding can be more useful in a situation where a

particular application involves a smaller number of events and loads of queries.

Additionally, the cost of flooding cannot be reduced when the queries per event generate a

high amount of data.[CX25]. The query origination and query source path finding

mechanism is shown in the image as,


46

Figure 3.7. Query is originated and query source is looking for the path to reach to the event

3.5.1.1.2. Rumour path finding mechanism

Rumour routing use agents to find the path to an event area. On finding the path agents store

the path at nodes as a state. An event node creates agents by the allocation of a path of

length 0 to themselves. The agents are created probabilistically the reason is that many

nodes watching the same event and trying to creating path to event node which as a result

create huge overhead. The agents travelling to limited number of hops. On returning agents

amalgamate their own routing table with the event table of visited nodes. To find a path to

multiple events the agents‟ priority is to find an aggregate path to both events. [CX25]

Figure 3.8. Agents aggregating to multiple events


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3.5.1.2. Related Routing Algorithm

3.5.1.2.1. Data-Centric and Flat Architecture Protocols - Flooding and Gossiping

In WSNs the flooding and gossiping are the known two approaches used for routing and it

flooding performs routing operation which does not require any particular routing algorithm

or topological adjustment. In the flooding routing mechanism broadcasting is the core

technique used. Each and every sensor node on receiving a data packet immediately

broadcasts to their neighbouring sensor nodes and the process does not stop until the packet

reaches its final destination or the data packet maximum threshold value reaches its

designated limit.

Gossiping works on a slightly different basis from its flooding counterpart. It is a modified

version of the flooding mechanism. On receiving a data packet a sensor node randomly

selects one of its neighbours and the data packet is forwarded to that neighbour only and

next the node which received data packet selects and forwards the packet to another node

and the process continues.

In relation to implementation the flooding mechanism is quite easy to implement. But at the

same time it has a number of drawbacks, such as implosion which is usually caused due to

the duplication of packets, overlap which is caused when two neighbours in the same region

send packets to the same sensor node and it is resource blind because there is no appropriate

consideration in regards to energy usage and so it guzzles a huge amount of energy. On the

other hand the gossiping mechanism mitigates the implosion problem in a random fashion to

select a neighbour‟s sensor nodes and then it forwards the packets to its neighbour.

Moreover, due to the aforementioned approach it causes a delay in the propagation of data

in the wireless senor network nodes.

3.5.1.3. Geographic Adaptive Fidelity (GAF) Routing Protocol

The GAF is a location-based and an energy aware routing protocol, initially developed for

an ad-hoc network but it is also widely used for wireless sensor networks. In the literature it

is also coined as a position-based routing protocol.

The goal of the GAF is to provide an optimised performance to a wireless sensor network by

means of assessment of the similar nodes according to their forward packets.


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GAF Operations Mechanism

In GAF the network is divided into virtual grids, and to locate each node on the grids a GPS

-based information is required and nodes which are equivalent in terms of their position in

the grid and are assumed to have the same cost for packet routing. Furthermore, in order to

save energy in the case of nodes which have the same cost for packet routing some of them

are put to sleep in a specified grid. Therefore, the GAF obviously boosts the network life

span in regards to energy usage. Meanwhile, the GAF algorithm can also be assumed to be a

hierarchical routing algorithm.

In terms of nodes communication in the grid, in each particular grid a single node can be

represented as a leader which is involved in communication with the base station on behalf

of the other nodes.

Optimal Path Calculation

The GAF calculates the optimal path on the basis of residual or cost energy cost level. And

the optimal is used for the data transmission. In location-based routing the routing

information is predicted from the strength of the signal. The GAF algorithm assumes that all

nodes existing in the same grid are equal in terms of data routing cost.

Moreover, GAF divides the grid into a sub-, small, equal size grid according to the condition

of the power and radio transmitters used by the sensor.

Furthermore, the election process decides which node can remain active and which nodes

have to be turned off. The nodes which are in sleeping mode can be awakened at any time

when required to perform duties like monitoring of communication jobs such as data

delivery to base station and so on. Data aggregation is not permissible particularly when the

data passing operation is in progress between two different grids. [BX17].

Relation between Radio Range and Grid Size

Suppose R is radio ranges which cover up the entire size of a grid. And in a virtual grid let r

is the square unit size; the longest possible distance between the two adjacent grids is the

longest diagonal linking two grids. Our resultant equation becomes

r 2 + (2r)

2 ≤ R

2 --------------------------Equation 3.1

Where r ≤ R∕√5 [8]


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GAF Operational States

The GAF consists of three operational states for a node inside the grid,

Discovery State

In this state GAF discovers neighbours within a grid

Active State

In this state, GAF nodes participates in the routing process

Sleep State

In this state GAF, some of the nodes which project the same cost in the grid are put into the

sleeping state. As a result the radio transceiver module of the node is turned off and an

amount of energy is conserved.

In GAF a node stays active for a time Ta and in time Ta active node in the grid broadcasting

to other correspondent nodes. The time for sleeping Ts depends on active node time Ta. And

in the discovery state each node sending packets at regular intervals Td. [CX5]

The GAF transition states and routing table are visually illustrated stated as,

GAF Pseudo-code

Step 1: Divide Grid into sub-virtual grid

Step 2 Switch to discovery mode (node?)

Step 3: Discovery of all neighbours

If (neighbour node are equivalent) then

Select single active node (leader) and put remaining nodes in sleep mode

Endif

Step 2: Perform routing operation on active nodes (leader)

Path finding (join active nodes)

If (active node fail) then

Change sleeping node to active state in the grid

Endif

Step 3: loop back to Step 1 for next grid


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Step 4: connect to base station

Step 5: exit

Advantages

1. GAF conserves energy during sleeping mechanism

Disadvantages

1. Only then the active node participates in communication and ultimately this can

influence the accuracy of the data.

2. GAF depends on information from GPS which can limit their functionality where

GPS is not available

3. If a grid consists of a single node then energy conservation cannot be balanced, and

if there is a packet with low energy virtual grids then the network may suffer from

network partitioning problem.[CX6]

4. It does not provide a data aggregation feature which typically exists in hierarchal

routing algorithms.

3.5.1.4. Greedy Perimeter Stateless Routing Protocol (GPSR)

Introduction

GPSR is a beacon-based routing geographic algorithm. In GPRS the neighbour routing table

is updated via periodic beacon messages. The GPRS routing mechanism is based on the

position of a node and the destination of the packet in order to take routing packet

forwarding decisions.

Protocol Details

GPSR protocol functions in two modes which are greedy and perimeter. In greedy mode,

GPRS makes greedy forwarding decisions in the network topology on the basis of its

immediate neighbours‟ available information, while in perimeter mode it is required of

GPRS particularly in a situation where a packet is passed on to a region where the greedy

forwarding mechanism is not achievable then the algorithm recovers the region by routing

all around the perimeter of the region. The phenomenon is called „face routing‟.

The neighbourhood table is constructed by means of periodic beacon broadcast messages,

which comprises an ID and sending node position.


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The B is a beacon interval parameter, and it is assumed that the time between two beacon

messages would be uniformly distributed when the B values is in the range of 0.5B, and

1.5B respectively. Furthermore, a time out interval is set as 4.5B, which is four times of the

maximum time interval, and a node is deleted from the neighbourhood table after it reaches

the time out interval i.e. 4.5B. It further clarifies, that a node can be deleted from the

neighbourhood table if it misses the beacon message three times in a row.

GPRS also includes an explicit beacon; if it is a scheduled regular data packet it delays the

number of bacons because of a piggy-backing service.

It is necessary for network interfaces to operate in a promiscuous mode, for the purpose that

every node in the transmission territory can receive packets apart from its associated

receiver and so forth.

In addition, on the sending of data packet a node resets the beacon timer. And the greedy

mode can be used when the node is found nearer to the destination D in the neighbourhood

table. Conversely, when a node is away from destination D as a result the perimeter mode is

used which works on the concept of face routing and the routing process would be

continued until a node reaches a point which is closer to D than the node P which is already

inside the perimeter mode. In regard to perimeter mode operation, it can be operated in both

graphs which are the Relative Neighbourhood and Gabriel.

3.5.1.4.1. Modes of GPSR Protocol - Greedy Forwarding

There are number of strategies which exist for routing such as single-path, multi-path,

flooding and so on. It uses local information for the routing of a packet and in each repeat

step the packet is nearer and nearer to the destination. Each node forwards the packet to a

node which is more optimal according to the local information available. The more optimal

node would be a node which is closer to the destination and involves minimum distance and

whole phenomenon which can be called a „greedy approach‟.


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Figure 3.9. Represents the greedy approach from node x to node y because these are located

in a close neighbourhood.

Figure 3.10. In Greedy Forwarding Routing the data packet is forwarded to a neighbour that

is located in close proximity

•Drawback

The known drawback of a „greedy routing strategy‟ is it fails in a situation where there is no

close neighbour exist to the destination and the forwarding node is stuck and not able to

move ahead. To recover from this problem it uses a strategy called „perimeter mode‟ or

„face routing‟, or it simply goes back to step one to commence a successful greedy routing

strategy.

There are number of variants of greedy forwarding strategy such as nearest forwarding

progress(NFP), most forwarding progress within a radius (MFR), and the minimum angle

between neighbour and destination node is termed as compass routing(CR). The different

variants of greedy strategy are stated as in figure,


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Figure 3.11. When greedy routing gets stuck in topology

Figure 3.12. When there is a hole in the network

3.5.1.4.2. Modes of GPSR Protocol - Face Routing

When the greedy forwarding strategy failed it was necessary to find a new one for the

recovery and smooth transmission of a packet to its destination. new strategy was devised

in 1999 which is called face routing. In face routing a packet is forwarded on the face of

nodes beside the incident edge in conjunction with the incident edge through implying (I

don‟t know what this means). On the implementation of the right or left hand rule in the

network graph then a successor node can be found to search out in clockwise order

subsequent to the predecessor node.[CX13] Face routing uses a planner graph and also uses

different faces of the graph from source s to destination t

Figure 3.13: Generic view of different faces of planner graph


54

Figure 3.14: Generic view of source and destination at planner graph

Advantages

Face routing uses the local information rule and it guarantees data delivery on the planner

graph by using the local position-based rule and furthermore it does not require any

maintenance of state information. The aim of planning a network on a planner graph is to

ensure the data delivery guarantees otherwise it is vulnerable to loops. [CX13].

Disadvantages

It is vulnerable to loops if a network topology is not planned properly on a planner graph.

[CX13]. It completely fails when there is no possible way to go from a source s to

destination t which can be graphically represented as,

The pseudo code for combined greedy forwarding and face routing are quoted as,

“A Combined Greedy/Face-Routing Algorithm

(GFG with sooner-back procedure [15])

Variables: previous hop p, current node u, target t, first edge in recovery

mode er and distance to target dr in rec. mode

if packet in greedy mode

select next hop v according to the greedy rule

if no such neighbour exists

select next hop v in ccw. direction from (u; t)

switch packet to recovery mode

store current distance to the destination dr

and er (u;v) in the packet header

endif


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else (packet is in recovery mode)

if there is a neighbour v with jjv�tjj < dr

switch packet to greedy mode

or else

select next hop v in ccw. direction from (u; p)

(using only nodes of a GG or RNG sub-graph)

if (u;v) equals the first edge er in recovery mode

drop packet; return

endif

endif

endif

forward packet to v “ [3]

3.5.1.5. Geographic Energy Aware Routing (GEAR)

GEAR is an energy aware routing protocol and it proposes energy and geographic

information is a factor for the selection of a neighbour‟s nodes to route packets to its final

destination (sink). [DX19] The GEAR protocol further proposes that the localisation of

modules need to be installed on the sensor board such as the global positioning system

(GPS) feature or maybe some other localisation mechanism can be used for finding a

location. GEAR also has the pre-knowledge of its neighbour‟s energy residual and its

location. In addition, GEAR uses energy as a main factor for the routing decision to route

packets to a destination region. And inside geographic regions, GEAR uses a geographic

recursive forwarding algorithm to broadcast the packet to expected destination

regions.[CX20]

GEAR Working Mechanism

To route a packet to a target region the sensor node uses two types of cost, namely the

estimated cost and learning cost. The estimated cost can be defined as the expected distance

to destination node and the outstanding amount of energy on the sensor node. On the other

hand, the learning cost is the adapted version of an estimated cost and is solely responsible

for routing where there is a hole in the sensor network. The hole problem occurs in the


56

sensor network where there is no close neighbour available for a sensor node in the direction

of the target destination and when no hole exists in the sensor network then an estimated

cost is identical to the learning cost. However, in comparison with the Diffusion Protocol,

the GEAR protocol limits its interest only to the targeted region, where in diffusion its

interest is not limited to a single region.

GEAR Packet Forwarding Stages

Stage 1:

In the first stage packets are routed towards a destination. On the reception of packets by the

node then it looks around for the nodes which are located in a close proximity with the

destination node and finally the neighbour‟s node is chosen as the next hop. In this

scenario, where more than one qualified node exists then a hole would exist in the network,

so in this situation one node would be selected on the basis of learning cost techniques.

Stage 2:

In the second stage, the data packets are already forwarded inside in the targeted region and

on receiving the packets inside the region the packets are then broadcast using a recursive

geographic forwarding algorithm or a restricted flooding mechanism. In this case, a scenario

where sensor nodes are densely populated, then restricted flooding is used as a preferred

method. On the other hand in a scenario where sensor nodes are densely populated then a

recursive geographic algorithm can be used.

With regard to a geographic flooding algorithm, it divides the targeted region into four sub-

geographic regions and the same process continues until a single node is left per region.

[CX21].

Advantages

1. GEAR extends the battery life span of sensor nodes and the overall network as

compared with non-energy aware routing protocols

Disadvantages

The mechanism proposed by the GEAR protocol involves high packet overheads,

therefore its implementation in a real time environment is not a considerable

option.[CX19]


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3.5.1.6. Flooding

In WSNs the flooding is a routing protocol and performs the routing operation while it does

not require a topological adjustment. The flooding routing mechanism for broadcasting is

the core technique used. Each and every sensor node, on receiving a data packet, it is

immediately broadcast to their neighbouring sensor nodes and the process does not stop

until the packet reaches the final destination or the data packet maximum threshold value

reaches its designated limit.

However, with regard to the implementation of the flooding mechanism it is quite easy to

implement. But at it the same time it has a number of drawbacks, such as implosion which is

usually caused due to the duplication of packets, and overlap which is caused when two

neighbours in the same region send packets to the same sensor node, and it is also resource

blind because there is no appropriate consideration in regards to energy usage and so it

consumes a huge amount of energy.

This is an enhanced version of the direct diffusion routing algorithm. It is mainly used for a

situation where geographic routing algorithms are not applicable. In a directed diffusion

routing algorithm, typically it floods the query to the whole network particularly if there are

no geographical parameters given for a diffuse task. Another approach is followed called a

„rumour routing algorithm‟. Rumour routing is particularly useful for a situation to tolerate

flooding routing mechanism, unless the amount of events is small and the number of queries

is large. Moreover, rumour routing can be placed in the middle of event flooding and query

flooding.

The rumour routing algorithm was proposed by Braginsky and Estrin and the core idea is to

pass on user queries to a node that examines specific and certain events, instead of flooding

the whole network to obtain information on the subject of happening events.[ N.

NARASIMHA DATTA AND K. GOPINATH] [Kemal Akkaya , Mohamed Younis].

However, there are a number of limitations in the routing algorithm which need to be

explored, such as the random nature of the path finding approach. Alternatively, another

approach is to use local information for finding interesting events in a faster and more

efficient manner. The next alternative is to use a probabilistic parameter which takes local

information so as to find an optimal performance or by studying the entire system hierarchy

for the particular events pattern in a mentioned timeframe.


58

3.5.1.7. LEACH

The LEACH is a cluster-based routing protocol and was developed by Hienzelman. It

performs the cluster heads election by the use of a probabilistic approach for the purpose of

rotating among each other so as to guarantee a good local energy balancing mechanism.

The cluster heads collect data from their associated nodes, and before transmission to the

base station it makes sure that data integration and fusion function is performed over the

data. In terms of data collection it uses a periodic approach in a centralised style.

Furthermore, the LEACH is principally tailored to an application which involves consistent

monitoring in a wireless sensor network. In spite of LEACH‟s achievements in terms of

network age increase and extension, at the same time it involves a few restricted

conjectures. In this regards, one of the conjectures which LEACH presents states that every

node transmits with satisfactory power to the anticipated bas station. Consequently, LEACH

is not tailored for larger network regions. [Emmanuel Sifakis]. The network model and

cluster head (CH) and cluster member (CM) are represented as below,

Figure 3.15. Represents base station, cluster head, and cluster

The different level of hierarchy involved in routing in LEACH is represented in the figure

below, as,

Figure 3.16. Different level of hierarchies


59

3.5.1.8. TEEN

The TEEN is abbreviated for Threshold Energy Senor Network protocols. TEEN is a

modified version of APTEEN routing protocols. In regard to its operation, members of

cluster heads receive a hard threshold from cluster heads, and this threshold is used for

sensed variables. In addition, the soft threshold makes a minute change in a sensed variable

value, and the aforementioned threshold value prompts the sensor to correspond with the

value calculated. And particularly the hard threshold in the case of a reduction in

transmission numbers it directly corresponds to the data and also ensures that the sensor

parameters are on a specific region. On the other side, a soft energy threshold only makes a

reduction in communication transmission, and only when some changes are received. In

relation to its limitations the core disadvantage is when no threshold is prompted is a result

no communication happens between the nodes. [Emmanuel Sifakis].

Summary

In this chapter we discussed routing protocols in ad-hoc and sensor network in details and in

the next chapter we will construct design for our proposed experiments.


60

Chapter 4

Design of Simulation Experiments

4.1. Introduction

In this chapter presents detailed study of a proposed simulation and evaluation of the

experiments which we will perform using the simulation.

Simulation is a scientific technique which can give us particular results, advance behaviour

of a particular operation in the real world system over time. Simulation is a built-in tool

which can show the real behaviours of the proposed system model. With simulation we can

evaluate the performance and other features of the system by investigating the interior

architecture of the system which is used for overall optimization. However, with simulation

we can observe, estimate, and assume the convolution of the real world system from the

results of the simulation.

Furthermore, an extensive simulation work for the evaluation of proposed routing algorithm

performance. It can be done through a number of simulation scenarios based on different

network metrics and topologies.

A number of network simulators are currently in the networking research filed. A survey has

been conducted by Akhtar and has noted a number of 42 network simulators such as

(Scalable ad-hoc network simulator) ShoX, OMNeT++, NS2 , OPNET, GloMoSim, J-Sim,

SENS, SENSE,Qual-Net.[M.Hadjila et al]. Nevertheless, we propose to use ShoX which is

a discreet event-driven simulator for the evaluation of routing algorithm performance in

sensor networks.

As we mentioned above there are number of simulation available for modelling of systems

and among these ShoX for further thesis study.

4.2. Scalable Ad Hoc Network Simulator (ShoX)

ShoX is an intelligent tool used for the simulation of a large heterogeneous network. ShoX

hosts some specific ad-hoc and WSNs protocols. ShoX is developed on the basis of a

discrete event system (DES) and it stimulates the behaviour of the system in the form of a

model and further processes it into a user-stated process. Moreover, ShoX provides a wide

and rich resourceful simulation environment for the modelling of a system, and can be


61

analysed in terms of discrete event system. ShoX is developed in Java programming

language while XML language can be used for network configuration. ShoX source code

and libraries are openly available. ShoX can be used for the evaluation of routing algorithms

under different network parameters such as packet drop rate, hop count and so on.

ShoX starting display screen and configuration panel are shown in the following screen-

shots,

Figure 4.1: ShoX starting view

Figure 4.2: ShoX Configuration Panel


62

4.3. Architecture of ShoX

ShoX is a discrete event simulator and is available in a single package which does not

require a manual package installation like other network simulations. The architectural

view of ShoX is stated in the map as,

Figure 4.3: ShoX Architectural View


63

ShoX architecturally consists of the following components

Event Queue

Event queue is an event queue at global level where all network events such as packets,

messages, nodes movements; timers and so on are placed.

Simulation Manger

Simulation manager is the core module of the ShoX, where it manages events queues and

assigning events to be delivered on the basis of their priority to the specified network nodes.

It is also responsible for updating of simulation time of upcoming events.

Packets

ShoX packets are considered as special events, when a packet is created at some further

layer further it is sent down the stack until it reaches the physical layer.

Event

vent can be any action in ShoX, like node movements, update messages, timers, packets and

so forth. Each event has its own time stamp, while it specifies the delivery time and, unique

id.

Layer

ShoX supports all OSI layers, and there is a special artificial layer in ShoX which exists

down the physical layer called AirModule. It can further support new layers in the stack.

Air Module

Air module manages radio signal propagation particularly receiving, listening, and sleeping,

off states at nodes

Interference Handler

It is an abstract class and making decision based on its implementation, while it has the

ability to completely ignore the interference or on the other side it will discard packets on

occurrence of interference.


64

Movement manager

Movement manager is responsible for the mobility of nodes whether it is stationary or

mobile. There are number of mobility models which exist in ShoX such as NoMovement,

RandaomWalk and so forth.

Energy Model

nergy model manages the consumption of energy for different network events such as the

sending and receiving of packet transmission, optimal path finding, and so on. There are

number of energy models available in ShoX, the most basic energy model is

BasicEnergyManger. BasicEnergyManger is an abstract class and is solely responsible for

energy consumption at ShoX

Physical Model

hysical model deals with communication taking place at a physical layer. There are a

number of physical models available in ShoX, such as SimplePhysics, UnitDisck and so on

Node

ode is a network node which can be a small wireless sensor node, or another device used in

a wireless sensor network

Traffic Generator

raffic generator module is responsible for traffic generation in ShoX. There are a number of

traffic generators available in ShoX, such as OneTimeRandomTrafficGenerator,

RandomTraffic, ExponentialRandomTraffic and so on

4.4. ShoX Key Features

First, house wireless sensor routing protocols, and also vendor devices models with source

code.

Second, it provides an integrated GUI-based debugging and analysis.

Third, it supports discrete event, hybrid and optional analytical simulation.

Fourth, it is based on object-oriented modelling.

Fifth, it provides an interface for integrating external objects files, and libraries

Sixth, it provides a hierarchical modelling environment.

Seventh, it provides realistic application modelling and analysis.


65

Eighth, it provides a fully parallel kernel simulation for 32-bit and 64-bit

Ninth, ShoX is freely available for universities and colleges for the purpose of academic

research and development.

4.5. ShoX Configuration

Initially, ShoX simulation can be configured with proposed simulation scenarios. ShoX is

composed of the following configuration model,

ShoX GUI configuration panel can be accessed from a class i.e. net.sf.shox.visual.ShoX.

The ShoX configuration can be found in ShoX/conf directory. The proposed test bed can be

added to ShoX by clicking on „project‟ by choosing run and the java application. The core

file of the simulator is net.sf.shox.simulator.kernal.Simulator.

Network topology

Network topology panel of ShoX can generate a number of nodes to be deployed in the

wireless sensor field. The deployment area can also be configured using SVG file or

alternatively it can be manually configured. The network topology panel provides a

configuration option called „initial node distribution‟ where we can specify node distribution

option senor nodes in a number of ways such as random node distribution and so on.

Node Behaviour

Node behaviour panel specifies a configuration level of senor node mobility but the sensor

nodes may also be set to static. The node behaviour configuration panel also specifies the

levels of application traffic.

Node Architecture

Node architecture panel represents a configuration option for a number of network layers

such as, application layer, operation layer (transport layer),network layer, data link layer

(logical link control layer, medium access control layer) and physical layer. Each network

layer has its own list of parameters which need to be set during the configuration phase of

ShoX.

Signal Propagation


66

Signal propagation panel of ShoX configures the signal propagation and its associated

parameters, such as reachable distance and interference distance. The signal propagation

panel also configures the level of interference during packet transmission.

Simulation Time

Simulation time panel configures the time for which simulation can be run to perform the

task. The time is measured in seconds. The simulation time panel also configures simulation

granularity which shows a number of simulation steps done in per second amount of time.

Simulation time panel can also be set in advance configuration option.

4.6. Metrics

The evaluation of a proposed routing algorithm such as rumour routing algorithm, and

Spine Optimal routing algorithm (SOR) in wireless sensor networks can be tested for the

following metrics,

Dropped Packet Ratio/Rate

Packet loss is the ratio of lost packets and sent packets. It occurs when a packet fails to

reach its destination.

Where Pkt fail represents failed packets, while Pkt sent represents packets sent

Average Drop Packet Ratio/Rate

Where Pkt fail represents failed packets, while Pkt sent represents packets sent


67

4.6.1. Simulation Parameters

Our simulation parameters for the proposed experiments are shown in the table as,

S.No Parameters Details

1 Area of Simulation 100 x 100 / 300 x 400 m2

2 Node Deployment Random

3 Mobility Model No Movement/ Random Walk

4 Routing Policy Rumour/ Optimal Spine Routing (OSR)

5 Traffic Type One time random traffic generator

6 Number of nodes 50: 10, 25, 49

7 Node Transmission Range 30 meter (maximum indoor ), 250 meter

(maximum outdoor) [DX5][Dx6]

8 Interference Queue Type Threshold packet manger

9 Simulation Time 20 / 60/80/100/120 seconds

Table 4.1: Simulation Parameters Table

4.7. Experimentation Design and Set-up parameters

The proposed routing algorithm can be tested against the selected network metrics, which

are packet drop rate and hop count. The network N nodes combinations chosen for the

experiments are {10, 25, 49} which can be distributed using a model randomStartPosition

in a simulated field of 100 x 100 m2

, 300 x 400 m2 (square meters). The radio signal

propagation model chosen for the experiments are UnitDisc model and Simple Physics,

where each and every node can sends packets within a range of 5 m (meter). The stationary

and mobility nodes model for the experiments used are noMovement, and RandomWalk. The

application level traffic can be generated using a random traffic model i.e

OneTimeRandomTrafficGenerator.

4.7.1. Experiment-1: Design of Small Network Topology

first scenario is based on smaller network topology and two different routing protocols

which rumour and OSR routing algorithm are evaluated for performance. And a smaller size

network topology is chosen and all other factor are set uniform and the metrics for

performance evaluation under which routing protocols can be tested are packet drop ratio,

and hop count. An addition, a list of simulation parameters is also suggested for the

aforementioned experiments which are, number of nodes with a given value 10, and


68

simulation time with a given value of 20 seconds, simulation granularity time with a given

value of 10,000 seconds and finally the mobility model is random walk and no

Movement.

Figure 4.4: Network topology of 10 node

4.7.1.1. Case 1: Stationary nodes using rumour routing

No. Of Nodes Initial Nodes

Distribution

Mobility Model Application

Traffic

Signal

Propagation

model

10 Random Start

Position

No Movement One time

random traffic

generator

Simple Physics

Parameters Table 4.2: Exp 1- Case 1: Rumour Routing Stationary Nodes

4.7.1.2. Case 2: Mobile nodes using rumour routing

No. Of

Nodes

Initial Nodes

Distribution

Mobility

Model

Application

Traffic

Signal Propagation

model

10 Random

Start Position

Random

Walk

One time

random

traffic

generator

Simple Physics

Parameters Table 4.3: Experiment 1- Case 2: Rumour Routing Mobile Nodes


69

4.7.1.3. Case 3: Stationary nodes using OSR routing

No. Of

Nodes

Initial Nodes

Distribution

Mobility

Model

Application

Traffic

Signal Propagation

model

10 Random

Start Position

No

Movement

One time

random

traffic

generator

Simple Physics

Parameters Table 4.4: Experiment 1- Case 2: Rumour Routing Mobile Nodes

4.7.1.4. Case 4: Mobile nodes using OSR routing

No. Of

Nodes

Initial Nodes

Distribution

Mobility

Model

Application

Traffic

Signal Propagation

model

10 Random

Start Position

Random

Walk

One time

random

traffic

generator

Simple Physics

Parameters Table 4.5: Exp1- Case 2: Rumour Routing Mobile Nodes

4.7.2. Experiment -2 – Medium Network Topology

The second experiment is based on medium size network topology. In this experiment two

routing algorithms which are the rumour and OSR routing algorithms, are evaluated at a

medium-sized network topology and other network factors are constant. The evolutional

metrics under which the algorithms are tested are drop packet rate and hop count. The

simulator parameters suggested for the experiment are a number of nodes with a given value

of 25, while the simulation time is 20 seconds; a simulation granularity time with a given

value of 10,000 seconds and finally the mobility model is selected as the random way point

algorithm.


70

Figure 4.5: Network topology of 25 nodes


No. Of

Nodes

Initial Nodes

Distribution

Mobility

Model

Application

Traffic

Signal Propagation

model

25 Random

Start Position

No

Movement

One time

random

traffic

generator

Simple Physics

Parameters Table 4.6: Exp 2- Case 1: Stationary nodes using rumour routing

4.7.2.2. Case 2: Mobile odes using rumour routing


Distribution


Traffic

Signal

Propagation

model

25 Random Start

Position

Random Walk One time

random traffic

generator

Simple Physics

Parameters Table 4.7: Exp 2- Case 2: Mobile nodes using rumour routing


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Distribution


Traffic

Signal

Propagation

model

25 Random Start

Position


random traffic

generator

Simple Physics




Distribution


Traffic

Signal

Propagation

model

25 Random Start

Position


random traffic

generator

Simple Physics


4.7.3. Experiment 3- Large Network Topology

The third experiment is based on a large network topology. In this case two routing

algorithms are evaluated at network topology size and other factors are constant. The

evolutional metrics under which the algorithms are tested are the drop packet rate and hop

count. The simulator parameters suggested for the experiment a number of nodes with a

given value 49, the simulation time is 50 seconds; simulation granularity time with a given

value of 10,000 seconds and finally the mobility model is selected as a random way point

algorithm.


72

Figure 4.6: Network topology of 49 nodes



Distribution


Traffic

Signal

Propagation

model

49 Random Start

Position


random traffic

generator

Simple Physics

Parameters Table 4.10: Exp 3- Case 1: Stationary nodes using rumour routing

4.7.3.2. Case 2: Mobile nodes using rumour routing


Distribution


Traffic

Signal

Propagation

model

49 Random Start

Position


random traffic

generator

Simple Physics

Parameters Table 4.11: Exp 3- Case 2: Mobile Nodes using rumour routing


73



Distribution


Traffic

Signal

Propagation

model

49 Random Start

Position


random traffic

generator

Simple Physics

Parameters Table 4.12: Exp 3- Case 3: Stationary Nodes using OSR routing



Distribution


Traffic

Signal

Propagation

model

49 Random Start

Position


random traffic

generator

Simple Physics

Parameters Table 4.13: Exp 3- Case 4: Mobile Nodes using OSR routing

4.7.4. Experiment No 4- Simulation Time Variation

In this experiment we changed the simulation time and all other parameters remain the same

as previous experiments in order to investigate the effect on routing with respect to the

network metric such as dropped packet rate and hop count. The experiment involves four

different cases two for each routing protocol.

4.7.4.1. Case 1: Rumour routing having stationary nodes

Number of nodes Simulation time Mobility Model

49 40,60,80, 100 seconds No Movement

Parameters Table 4.14: Experiment 4- case 1-Simulation Time

4.7.4.2. Case 2: Rumour routing with mobile nodes

Number of nodes Simulation Time Mobility Model

49 40,60,80,100 seconds Random Walk


4.7.4.3. Case 3: OSR routing having stationary nodes


49 40,60,80,100 seconds No Movement



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4.7.4.4. Case 4: OSR routing having mobile nodes


49 40,60, 80,100 seconds Random Walk


4.7.5. Experiment No 5- Nodes Deployment Area variation

In this experiment we changed the nodes deployment area and all other parameters are the

same. To investigate there is any effect on routing with respect to a network metric such as a

dropped packet rate and hop count.


Number of nodes Deployment Area Mobility Model

49 300 x 400 m2 No Movement

Parameters Table 4.18: Experiment 5- case 1-Deployement Area

4.7.5.2. Case 2: Rumour outing having mobile nodes


49 300 x 400 m2 Random Walk

Parameters Table 4.19: Experiment 5- case 2-Deployment Area

4.7.5.3. Case 3: OSR outing having stationary nodes


49 300 x 400 m2 No Movement


4.7.5.4. Case 4: OSR outing having mobile nodes


49 300 x 400 m2 Random Walk



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4.7.6. Experiment No 6- Interference Handler Model Variation

In this experiment we changed the interference handler model and all other parameters

remain the same to investigate if there is any effect on routing with respect to a network

metric such as a dropped packet rate and hop count. The experiment involves four different

cases two for each routing protocol.


Number of nodes Mobility model Interference handler model

49 No Movement Minimum Signal to Noise

Ratio ( SNR)

Parameters Table 4.22: Experiment 6- Case 1- Interference handler model

4.7.6.2. Case 2: Rumour routing having mobile nodes

Number of nodes Mobility Model Interference Handler model

49 Random Walk Minimum Signal to Noise

Ratio ( SNR)


4.7.6.3. Case 3: OSR routing having stationary nodes

Number of nodes Mobility Model Interference Handler model

49 No Movement Minimum Signal to Noise

Ratio ( SNR)


4.7.6.4. Case 4: OSR routing having mobile nodes

Number of nodes Mobility Model Interference Handler Model

49 Random Walk Minimum Signal to Noise

Ratio ( SNR)


Summary

In this chapter we described Scalable Ad-Hoc Network Simulator (ShoX) simulation,

metrics, designing and parameters settings for the proposed experiments which are, Design

of small network topology, design of medium network topology, design of large network

topology, design of simulation time variation, design of nodes deployment area variation,


76

and design of interference handler model variation. In the next chapter we will perform

implementation of our proposed experiments.


77

Chapter 5

Implementation and Results Analysis

5.1. Introduction

In this chapter we are articulating implementation details for each experiment which include

both OSR and rumour routing protocols.

5.2. Implementation

The routing protocols rumour and OSR which we are comparing are already implemented in

the ShoX simulator. We made slight modifications to the simulator parameters using XML

code for our proposed experiments. ShoX simulator was specifically developed for wireless

sensor and ad-hoc routing protocols. We are using the same java code which is already

written for OSR and rumour routing protocols.

The goal of our study is to compare the relative performance of the proposed routing

protocols with respect to different size of topologies, and mobility. To fairly compare each

of the proposed protocols therefore pre-generated XML-based scenarios were ported to

ShoX with an identical set of configurations. The performance metrics are drop packet rate,

average drop packet, hop count, average hop count and bit rate. Seven experiments were

performed for each routing protocol with respect to different cases and so on. The

experiments are stated as,

5.3. Experiment 1: Small Nodes Scenario

In this experiment we implemented OSR and rumour routing protocols at a small nodes

topology. Furthermore, network topology was tested for both stationary and mobile node

movement conditions. The experiment consisted of four cases, so two cases for stationary

nodes, and two cases for mobile nodes.


78

5.3.1. Case 1, 2, 3 & 4: Measurement of packet drop ratio in small number

of stationary and mobile nodes using OSR and Rumour routing protocols

5.3.1.1. Network Model

5.3.1.1.1 Network Topology

The small node scenario i.e. cases 1, 2, 3 & 4 consists of 10 nodes which are randomly

deployed in a two dimensional simulation field 100 meter square. Initially nodes are

randomly distributed using

net.sf.shox.simulator.movement.RandomStartPositions model of ShoX.

5.3.1.1.2. Network Behaviour

The node behaviour for cases 1 and 3 were chosen as stationary which used the ShoX

stationary model net.sf.shox.simulator.movement.NoMovemen. The simulation parameter was

adjusted as static.

The node behaviour for cases 2 and 4 was stationary which used the ShoX mobile model

net.sf.shox.simulator.movement.RandomWalk. The simulation parameter was adjusted as

static.

The traffic model implemented for cases 1 and 3 was

net.sf.shox.simulator.traffic.OneTimeRandomTrafficGenerator. The traffic model consists of

parameters like generator, traceFileName, traceFileMode. The speed of traffic is adjusted as

low, medium and high.

5.3.1.1.3. Node Architecture

The six layer model is implemented. The first application layer was implemented using the

model:

net.sf.shox.simulator.node.user.datamanagement.rumor.RumorEvaluationApplicationLayer.

The rumour application layer is the model used for rumour routing algorithm configuration,

and it also used a parameter such as distinctValues and hold value 5.

Second, the operating system layer which was implemented using

net.sf.shox.simulator.node.user.os.BasicOperatingSystemLayer model. Operating system

layer is an abstract super-class and is used for the implementation of an operating system, and

it used the parameter such as serviceManger and can be accessed from

net.sf.shox.simulator.node.user.os.serviceManager class.


79

Thirdly, the network layer was implemented using the ShoX model, such as

net.sf.shox.simulator.node.user.OptimalSourceRouting. Optimal source routing is a class

used for routing in ShoX.

Fourth, the logical link control (LLC) layer is implemented for logical link control

management and it is the upper sub-layer of the data link layer in the OSI model. LLC is

responsible for the multiplexing mechanism, and it also deals with flow control, the

automatic repeat request error mechanism and so on. LLC is implemented using super class

i.e. net.sf.shox.simulator.node.user.LogLinkDebug for both rumour and OSR routing

protocols.

Fifth, the medium access layer (MAC) is the lower sub-layer of the data link layer in the OSI

model. The MAC layer for rumour and OSR is implemented using a super class i.e.

net.sf.shox.simulator.node.user.MAC_IEEE802_11bg_DCF. The maximum number of retries

MAC do is 10. The Rate Adaption method used is AARF. The number of consecutive

successful bit rate is before raising the bit rate is 10 (Unclear). The consecutive number of

transmission fails allowed before lowering the bit rate is 2. After a premature increase in bit

rate the erroneous bit rate for AARF is 2. The time out raised up bit rate is 0.1.

Sixth, the physical layer is the lower layer which deals with the transmission and transmitting

raw bits. IEEE802.11 implements wireless local area network (WLAN) standards b and g

using distributed coordination function (DCF) technique, and their implemented parameters

are shown in the table below,

802.11

Protocol

Power Bandwidth(M

HZ)

Frequen

cy

(GHZ)

Modulati

on

Allowa

ble

MIMO

stream

s

outdoor range

g 100m

W

2.4 20 OFDM

and

DSSS

1 250 meter

[DX5,6]

Table 5.1: Parameters of IEEE802.11g WLAN standards

5.3.1.1.4. Signal Propagation

First, the signal propagation model is the sub-layer of the physical layer and it calculates the

reach of sender and receiver, and a given signal strength. The simple physics model is

implemented and can be accessed from net.sf.shox.simulator.physical.SimplePhysics.


80

Second, the interference handler model is also a part of the physical layer and is responsible

for handling the interference in the transmission channel. The interference model

implemented is thresh-old packet mangler and can be accessed from:

net.sf.shox.simulator.physical.ThresholdPacketMangler. The threshold packet mangler

specifies a signal to noise ratio (SNR) threshold value as a parameter. The SNR shows the

ratio of signal power and noise power. The SNR value should be greater or equal to 1 i.e.

SNR ≥ 1.

5.3.1.1.5. Simulation Time

Simulation time is measured in seconds and the time set for cases 1 and 3 was set at 20

seconds.

Secondly, simulation granularity is the time in which we fixed the number of steps to be

performed by the simulation in per second time. The simulation granularity set for cases 1

and 3 was implemented as 10,000 steps per second.

5.3.1.1.6. Results

The results obtained from cases 1, 2, 3 and 4 are shown in charts as below,

Figure 5.1: Relative performance comparison in small stationary topology of 10 mobile

nodes


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5.4. Experiment 2- Medium Scenario

In this experiment we implemented OSR and rumour routing protocols at medium size nodes

topology. Furthermore, network topology is tested for both stationary and mobile node

movement condition. The experiment consisted of four cases, two cases for stationary nodes,

and two cases for mobile nodes.

5.4.1. Cases 1, 2, 3 & 4: Measurement of packet drop at hop in 25

stationary nodes using OSR and rumour routing algorithm


5.4.1.1.1. Network Topology

The medium node scenarios i.e. cases 1, 2, 3 & 4 consisted of 25 nodes which were

randomly deployed in a two dimensional simulation field i.e. X and Y in 100 meter square.

Initially nodes were randomly distributed using


Figure 5.2: Relative performance comparison in small mobile topology of 10 nodes


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5.4.1.1.2. Network Behaviours

The node behaviour for cases 1, 2, 3 and 4 was stationary using the ShoX stationary model

net.sf.shox.simulator.movement.NoMovemen. The simulation parameter was adjusted as

static.

The node behaviour for cases 2 and 4 were stationary using the ShoX mobile model


static.

The traffic model implemented for cases 1, 2, 3 and 4 was

net.sf.shox.simulator.traffic.OneTimeRandomTrafficGenerator. The traffic model consisted

of parameters like generator, traceFileName, traceFileMode. The speed of traffic was

adjusted as low, medium and high.


The six layer model is implemented, first application layer was implemented using

net.sf.shox.simulator.node.user.datamanagement.rumor.RumorEvaluationApplicationLayer

model. Application Layer was the model used for rumour routing algorithm configuration,

and it also used parameter such as distinctValues.

Second, the operating system layer was implemented using

net.sf.shox.simulator.node.user.os.BasicOperatingSystemLayer model. The operating system

layer is an abstract super-class and is used for the implementation of the operating system,

and it used the parameter such as serviceManger and can be accessed from


Third, the network layer was implemented using the ShoX model, such as



Fourth, the logical link control (LLC) layer was implemented for logical link control

management and it is the upper sub-layer of the data link layer in OSI model. The LLC is


automatic repeat request error mechanism and so on. The LLC is implemented using super

class i.e. net.sf.shox.simulator.node.user.LogLinkDebug for both rumour and OSR routing

protocols.


84




MAC do is 10. The Rate Adaption method used was AARF. The number of consecutive

successful bit rate before raising the bit rate was 10. The consecutive number of transmission

fails before lowering the bit rate was 2. After a premature increase in the bit rate the

erroneous bit rate for AARF was 2. The time out raised up bit rate was 0.1.




are shown in the table,

802.11

Protocol

Power Bandwidth(M

HZ)

Frequen

cy

(GHZ)

Modulati

on

Allowa

ble

MIMO

stream

s

outdoor range

g 100m

W

2.4 20 OFDM

and

DSSS

1 250 meter

[DX5,6]



First, the signal propagation model is the sub-layer of the physical layer and calculates the

reach-ability of sender and receiver, and a given signal strength. The simple physics model

was implemented and can be accessed from net.sf.shox.simulator.physical.SimplePhysics.

Second, the interference handler model is also a part of the physical layer, and is responsible

for handling the transmission channel. The interference model implemented was the

threshold packet mangler and can be accessed from

net.sf.shox.simulator.physical.ThresholdPacketMangler. hreshold packet mangler specify

signal to noise ratio (SNR) threshold value as a parameter. The SNR shows the ratio of signal

power and noise power. The SNR value should be greater or equal to 1 i.e. SNR ≥ 1.


85


The simulation time was measured in seconds, and the time set for cases 1, 2, 3 and 4 was set

at 20 seconds.

Secondly, the simulation granularity is the time in which we fixed the number of steps to be

performed by the simulation in per second time. The simulation granularity set for cases 1, 2,

3 and 4 was implemented at 10,000 steps per second.

5.4.1.1.6. Results

The results obtained from cases 1, 2, 3 and 4 are shown in the charts below,

Figure 5.3: Relative performance comparison in small stationary topology of 25 nodes

Figure 5.4: Relative performance comparison in small mobile topology of

25 nodes


86

5.5. Experiment 3- Large Nodes Scenario

The experiment implemented OSR and rumour routing protocols at a large sized nodes

topology. Furthermore, the network topology was tested for both stationary and mobile node

movement condition. The experiment consisted of four cases, so two cases for stationary

nodes and two for mobile nodes.

5.5.1. Cases 1, 2, 3 and 4: Measurement of packet drop at hop in 49

stationary nodes using OSR and rumour routing algorithm



The medium node scenario i.e. cases 1, 2, 3 & 4 consisted of 49 nodes which were randomly

deployed in a two dimensional simulation field i.e. X and Y in 100 meter square. Initially

nodes were randomly distributed using


5.5.1.1.2. Network Behaviours

The node behaviour for cases 1, 2, 3 and 4 was stationary which used the ShoX stationary

model net.sf.shox.simulator.movement.NoMovemen. The simulation parameters were adjusted

as static.

The node behaviour for cases 2 and 4 was stationary and the ShoX mobile model was

employed - net.sf.shox.simulator.movement.RandomWalk. The simulation parameters were

adjusted as static.


net.sf.shox.simulator.traffic.OneTimeRandomTrafficGenerator. The traffic models consist of

parameters like generator, traceFileName, traceFileMode. The speed of traffic was adjusted

to low, medium and high.


87


The six layer model was employed and the first application layer was implemented using


model. The rumour application layer is the model used for rumour routing algorithm

configuration, and it also used parameters such as distinctValues and holds value 5.

Second, the operating system layer was implemented using the


layer is an abstract super-class and is used for the implementation of the operating system,

and it used the parameters from serviceManager and can be accessed from


Third, the network layer was implemented using the ShoX model,



Fourth, the logical link control (LLC) layer is implemented for the logical link control



automatic repeat request error mechanism and so on. LLC is implemented using super class


protocols.

Fifth, the mmedium access layer (MAC) is the lower sub-layer of the data link layer in OSI





fail before lowering the bit rate is 2. After a premature increase in bit rate the erroneous bit

rate for AARF was 2. The time out raised up bit rate was 0.1.






88

802.11

Protocol

Power Bandwidth(MH

Z)

Frequen

cy

(GHZ)

Modulatio

n

Allowa

ble

MIMO

streams

outdoor range

g 100m

W

2.4 20 OFDM

and DSSS

1 250 meter

[DX5,6]



First, the signal propagation model is the sub-layer of the physical layer, and calculates the



Second, the interference handler model is also a part of the physical layer, and is responsible

for handling inference in the transmission channel. The interference model implemented

was the threshold packet mangler and can be accessed from

net.sf.shox.simulator.physical.ThresholdPacketMangler. This model specifies a signal to

noise ratio (SNR) threshold value as a parameter. The SNR shows the ratio of signal power

and noise power. The SNR value should be greater or equal to 1 i.e. SNR ≥ 1.


The simulation time was measured in seconds and the time set for cases 1, 2, 3, and 4 was set

to 20 seconds.

Secondly, the simulation granularity is the time in which we fixed the number of steps to be

performed by the simulation in per second time. The simulation granularity set for cases 1, 2,

3 and 4 was implemented at 10,000 steps per second.


89

5.5.1.1.6. Results

The results obtained from case 1, 2, 3 and 4 are shown in the charts below,

Figure 5.6: Relative performance comparison in large mobile topology of 49 nodes

Figure 5.5: Relative performance comparison in large stationary topology of 49 nodes


90

5.6. Experiment No 4- Simulation Time variation

The experiment implements OSR and rumour routing protocols at large size nodes topology

and at different simulation pause time. Furthermore, network topology was tested for both

stationary and mobile node movement. The experiment comprised four cases, namely two

cases for stationary nodes and two for mobile nodes.

5.6.1. Cases 1, 2, 3 and 4: Measurement of packet drop at stationary

topology using OSR and rumour routing algorithm



The medium node scenario was used for cases 1, 2, 3 & 4 and consisted of 49 nodes which

were randomly deployed in a two dimensional simulation field i.e. X and Y in 100 x 100

meter square. Initially the nodes were randomly distributed using

net.sf.shox.simulator.movement.RandomStartPositions model of ShoX


The node behaviour used for cases 1, 2, 3 and was stationary which used the ShoX stationary

model net.sf.shox.simulator.movement.NoMovemen. The simulation parameters were adjusted

to static.

The node behaviour for cases 2 and 4 was mobile using ShoX mobile model


static. Is this not contradictory? Sorry maybe I‟m missing something but it seems to

contradict the first sentence.


net.sf.shox.simulator.traffic.OneTimeRandomTrafficGenerator. The traffic model consisted

of parameters like generator, traceFileName, traceFileMode. The speed of traffic was set at

low, medium and high.


The six layer model was implemented and the first application layer was ted


model. The Rumour Application Layer is the model used for rumour routing algorithm

configuration, and it also used parameter such as distinctValues.


91

Second, the operating system layer was implemented using


layer is an abstract super-class and is used for the implementation of operating system, and it

used the parameter such as serviceManger and can be accessed from


Thirdly, the network layer was implemented using the ShoX model



Fourthly, the logical link control (LLC) layer was implemented for logical link control


responsible for the multiplexing mechanism, and it also deals with the flow control, the

automatic repeat request error mechanism and so on. LLC was implemented using super class


protocols.




MAC do is 10. The Rate Adaption method was AARF. The number of consecutive successful

bit rate permissible before raising the bit rate was 10. The consecutive number of

transmission fails allowed before lowering the bit rate was 2. After a premature increase in

the bit rate the erroneous bit rate for AARF was 2. The time out raised up bit rate was 0.1.

Sixth, physical layer is the lower layer which deals with the transmission and transmitting

raw bits. IEEE802.11 implemented wireless local area network (WLAN) standards b and g

using distributed coordination function (DCF) technique, and their implemented parameter


802.11

Protocol

Power Bandwidth(M

HZ)

Frequen

cy

(GHZ)

Modulati

on

Allowa

ble

MIMO

stream

s

outdoor range

g 100m

W

2.4 20 OFDM

and

DSSS

1 250 meter


92



signal propagation model is the sub-layer of the physical layer and calculates the reach-

ability of sender and receiver, and a given signal strength. The simple physics model was


interference handler model is also a part of physical layer, and is responsible for handling

inference in the transmission channel. The interference model implemented was threshold

packet mangler and can be accessed from

net.sf.shox.simulator.physical.ThresholdPacketMangler. threshold packet mangler specifies

the signal to noise ratio (SNR) threshold value as a parameter. The SNR shows the ratio of

signal power and noise power. The SNR value should be greater or equal to 1 i.e. SNR ≥ 1.


imulation time is calculated in seconds, and the time set here for cases 1, 2, 3, and 4 was set

at 20, 40, 60, 80 and 100 seconds.

Simulation granularity is the time in which we fixed the number of steps to be performed by

the simulation in per second time. The simulation granularity set for cases 1,2, 3 and 4 was

implemented at 10,000 steps per second.

5.6.1.1.6. Results

The results obtained for cases 1, 2, 3 and 4 are shown in the charts below,

Figure 5.7: Relative performance comparison at different simulation time


93

5.7. Experiment No 5- Network Deployment Area Variation

The experiment implements OSR and rumour routing protocols at a large sized nodes

topology and at a different network deployment area. Furthermore, the network topology was

tested for both stationary and mobile node movement. The experiment consisted of four

cases, two cases for stationary nodes and two for mobile nodes.





The network scenarios consisted of 49 nodes which were randomly deployed in a two

dimensional simulation field i.e. 300 x 400 meter square. Initially nodes were randomly

distributed using net.sf.shox.simulator.movement.RandomStartPositions model of ShoX.


The node behaviour for the four cases was stationary and the ShoX stationary model was

employed, net.sf.shox.simulator.movement.NoMovemen. The simulation parameters were

static.

The node behaviour for cases 2 and 4 was mobile using the ShoX mobile model

net.sf.shox.simulator.movement.RandomWalk. The parameters were static.

The traffic model implemented was

Figure 5.8: Relative performance comparison at different simulation time


94

net.sf.shox.simulator.traffic.OneTimeRandomTrafficGenerator. Their parameters are listed

as, generator, traceFileName, traceFileMode. The speed of traffic was measured at low,

medium and high.


The six layer model was implemented and the first application layer was used with:


model. The rumour application layer was the model used for rumour routing algorithm

configuration and it also used parameters such as distinctValues.

Operating system layer was employed using:

net.sf.shox.simulator.node.user.os.BasicOperatingSystemLayer model. perating system layer

is an abstract super-class used for the implementation of operating system. It used the

parameter serviceManger which can be accessed from:

net.sf.shox.simulator.node.user.os.serviceManager class.Thirdly the network layer was

implemented using ShoX model, such as



logical link control (LLC) layer was implemented for logical link control management and it

is the upper sub-layer of the data link layer in the OSI model. LLC is responsible for

multiplexing mechanism, and it also deals with flow control, and automatic repeat request

error mechanism and so on. LLC is implemented using super class i.e.

net.sf.shox.simulator.node.user.LogLinkDebug for both rumour and OSR routing protocols.

Fifth, the mss layer (MAC) is the lower sub-of data link layer in OSI model.MAC layer for

rumour and OSR was implemented using a super class i.e.


MAC do is 10. The rate adaption method was AARF. The number of consecutive successful

bit rates allowed before raising the bit rate was 10. The consecutive number of transmission

fails before lowering the bit rate was 2. After a premature increase in the bit rate the

erroneous bit rate for AARF was 2. The time out raised up bit rate was 0.1.

Sixth, physical layer is the lower layer which deals with the transmission and transmitting

raw bits. IEEE802.11 implemented wireless local area network (WLAN) standards b and g


95



802.11

Protocol

Power Bandwidth(M

HZ)

Frequen

cy

(GHZ)

Modulati

on

Allowa

ble

MIMO

stream

s

outdoor range

g 100m

W

2.4 20 OFDM

and

DSSS

1 250 meter

[DX5,6]



First, the signal propagation model is the sub-layer of the physical layer, and calculates the



interference handler model is also a part of the physical layer, and is responsible for handling

inference in the transmission channel. The interference model implemented threshold packet

mangler and can be accessed from:

net.sf.shox.simulator.physical.ThresholdPacketMangler. The threshold packet mangler

specifies a signal to noise ratio (SNR) threshold value as a parameter. The SNR shows the

ratio of signal power and noise power. The SNR value should be greater or equal to 1 i.e.

SNR ≥ 1.


This is measured in seconds and the time set for the four cases was 20 seconds.

Next simulation granularity is the time which we fixed for the number of steps to be

performed by the simulation in per second time. The simulation granularity set for 10,000

steps per second.


96

5.7.1.1.6. Results

The results obtained from cases 1, 2, 3 and 4 are shown below,

Figure 5.9: Relative performance comparison at deployment area of 300 x 400 m2

Figure 5.10: Relative performance comparison at deployment area of 300 x 400 m2


97

5.8. Experiment No 6 - Interference Handler Model Variation

This experiment implements OSR and rumour routing protocols at large size nodes topology

and with a different interference handler model. Furthermore, the network topology was

tested for both stationary and mobile node movement. The experiment looked at four cases,

so two cases for stationary nodes and two cases for mobile nodes.





The network scenarios of cases 1, 2, 3 & 4 consisted of 49 nodes which were randomly

deployed in a two dimensional simulation field i.e. 100 x 100 meter square. Initially the

nodes were randomly distributed using



The node behaviour for cases 1, 2, 3 and 4 was stationary which used ShoX stationary model

net.sf.shox.simulator.movement.NoMovemen. The simulation parameters were adjusted as

static. (Again it‟s contradictory – I thought it was two cases for each.)

The node behaviour for cases 2 and 4 were chose as mobile which used ShoX mobile model


static.


net.sf.shox.simulator.traffic.OneTimeRandomTrafficGenerator. The traffic models consisted

of the parameter like generator, traceFileName, traceFileMode. The speed of traffic was low,

medium and high.


98


The six layer model was implemented with the first application layer using


model. The Rumour Application Layer model was used and it also used parameters such as

distinctValues and hold value 5.

operating system layer was implemented using


layer is an abstract super-class and it used parameters such as serviceManger and can be

accessed from net.sf.shox.simulator.node.user.os.serviceManager class.

Third, network layer was implemented using the ShoX model, such as



logical link control (LLC) layer was used for logical link control management. It is

responsible for the multiplexing mechanism and it also deals with flow control, and automatic

repeat request error mechanism and so on. LLC was implemented using super class i.e.

net.sf.shox.simulator.node.user.LogLinkDebug for both rumour and OSR routing protocols.

Fifth, m The MAC layer for rumour and OSR was implemented using a super class i.e.




fail before lowering the bit rate was 2. After premature increase in bit rate the erroneous bit

rate for AARF was 2. The time out raised up bit rate was 0.1.

Sixth, pIEEE802.11 implemented wireless local area network (WLAN) standards b and g



802.11

Protocol

Power Bandwidth(MH

Z)

Frequen

cy

(GHZ)

Modulatio

n

Allowa

ble

MIMO

streams

outdoor range

g 100m

W

2.4 20 OFDM

and DSSS

1 250 meter

[DX5,6]



99


signal propagation model is the sub-layer of the physical layer and calculates the reach-

ability of sender and receiver and a given signal strength. The simple physics model was


interference handler model is also a part of the physical layer and is responsible for handling

interference in the transmission channel. The interference model implemented was the

threshold packet mangler and can be accessed from

net.sf.shox.simulator.physical.ThresholdPacketMangler. The signal to noise ratio (SNR)

threshold value was a parameter. The SNR shows the ratio of signal power and noise power.

The SNR value should be greater or equal to 1 i.e. SNR ≥ 1.


is measured in seconds and the time set for every case was 20 seconds.

S The simulation granularity set for cases 1, 2, 3 and 4 was implemented at 10,000 steps per

second.

5.8.1.1.6. Results

The results obtained are shown in the charts below,

Figure 5.11: Relative performance comparison at different SNR levels


100

5.9. Simulation Results and Performance Analysis

Figure 5.1 shows that when the number of nodes is increasing so the ratio of dropped packets

is also on the rise. To achieve an optimal performance the ratio of dropped packets needs to

be low. From figure 5.1 we observed that the OSR routing protocol drops slightly more

packets than the rumour. The reason is that OSR involves high routing overheads during the

path discovery phase which results in a negative effect on the OSR performance. The

Rumour routing protocol drop packets is in the range of 2% and 15% drop packet ratio in

stationary nodes which is lower than OSR.

We analyse from the figure 5.2, that the drop packets ratio is slightly increased due to mobile

nodes movements in comparison with stationary nodes. OSR shows an increase in drop

packets ratio due to nodes movements, routing path discovery overhead because it gathers

global topological information for full topology, while rumour routing also shows a slight

increase in its dropped packets ratio due to nodes mobility but is still lower than OSR. The

nodes moments effect the performance of both routing protocols. The optimal performance is

closely associated with the lower drop packet ratio.

Furthermore, igure 5.3 demonstrates that in stationary nodes the OSR routing performance

further deteriorates by 25% in the drop packet ratio, while on the other hand in rumour

routing performance is still found to be uniform and is unaffected by an increase in the

number of nodes or network resources. We analyse that as the network resources increase the

performance of OSR is further worsening.

Figure 5.4 explains that in mobile topology the drop packet ratio in both OSR and rumour

shows a slight rise. We observed that due to mobility mode OSR routing performance is

Figure 5.12: Relative performance comparison at minimum SNR levels


101

further tainted due to an increase in network resources. OSR requires more storage to store

global topological information. Conversely, rumour routing creates a path only when an event

occurs, so consequently it minimises the routing overheads at the queue interface and boosts

its performance in situations where there is heavy network traffic and so on.

Moreover, igure 5.5 depicts that in a large stationary topology the total number of nodes

increased to 49. Rumour routing protocol starts drop packets earlier than OSR, while overall

rumour packets dropping ratio is less than OSR routing. OSR routing protocol facing more

packet drop which is round about 20%, it‟s due to routing overheads during the path

discovery phase in the spine. Rumour routing is observed to be scalable in terms of node

modification and network resource changes.

Figure 5.6 shows that in large stationary topology first rumour protocol begins to drop

packets because rumour uses a query to discover a path to an event which took place

somewhere in topology. When rumour failed to find a path to an event then it resubmits the

query with TTL (time to live), and floods it to the whole network which increases routing

overheads with the result of dropped packets. In dropping packets OSR protocol begins after

rumour, but the percentage of OSR drop packet ratio is greater than the rumour as the

network resources grow and to adjust and update global routing information table involves

large routing traffic and overhead at interface queue and consequently dropped packets. As

we analyse that in a condensed stationary large network OSR suffers more than rumour,

while rumour performance is not an optimal but better than OSR in terms of routing.

Figure 5.6 shows the placement of 49 nodes in a mobile condense network. The performance

of OSR routing protocol is further worsened and drop packets at percentage of greater than

25% because nodes are moving around in different positions which affects the transmission

path to the target node (sink), and there is a strong possibility that some nodes move out of

the wireless transmission range and the current path will no longer be able to reach the

destination node. Therefore, the discovery path operation is re-performed. Rumour routing

protocols drop packets ratio is recorded as 20% at maximum number of nodes. As an

average drop packet ratio of rumour is between 10 to 15 % at 10 to 45 nodes. We observed

that the rumour routing protocol performs better in terms of drop packet ratio, path discovery,

and route maintenance at mobile nodes.


102

Figure 5.7 illustrates that in stationary nodes with different simulation time. When simulation

time is increased on the other hand the drop packets ratio is noted as uniform. We observed

that OSR was not affected by a different simulation time. Similarly, the rumour routing

protocol was also not affected by a different simulation time because the simulation is even

discrete simulator and each event happening at particular time.

Figure 5.8 shows mobile nodes with a different simulation time slot in a dense sensor

network. With the OSR routing algorithm it was noted that their drop packet ratio is uniform

at different simulation times. The OSR routing operation is not affected by different

simulation time and the overall drop packet ratio is between 25 to 30 %.

Figure 5.9 shows different field area with respect to stationary topology. The sensor nodes are

stationary in all these different sizes of a simulation field area. It is analysed as the simulation

expands the drop packet ratio was noted uniform both in rumour and OSR. The nodes are

randomly deployed so the probability of network partition cannot be ignored, and the path

discovery mechanism also fails due to network partition. As the area size of the network

increases the distance between the nodes and sink is also increased.

Figure 5.10 exhibits the results of WSNs mobile topology different field area. The drop

packet ratio in rumour was not uniform at stationary topology. While OSR‟s performance is

further worsened with nodes mobility and rumour is approximately uniform in mobile and

stationary topology, even if there is a slight increase in the drop packet ratio. The OSR

routing phase involves the construction of a virtual spinal back bone for the purpose of

providing a back-up and maintenance path, and gathering full topological information as

result the packet at interface queue increases which ends up with dropping packets. On the

other side at rumour with mobile nodes different positions the network performance in terms

of dropping packets is better than OSR because rumour routing agents discovered path and

there is a possibility that multiple optimal path is available to an event which minimise drop

packets and shows flexibility with nodes random movements.

Figure 5.11 shows sound-to-noise ratio (SNR) levels at stationary nodes. The network is

densely populated and consists of 49 nodes at 100 x 100 m2

outdoor environment. We

observed that at SNR lower level 1 the drop packets ratio at OSR was noted as 25% because

the nodes are densely populated at close proximity and the signal overhead and interference is

higher, while on at rumour protocol performance in terms of drop packet ratio was found 5%


103

lower. As the SNR level is increases the performance of both OSR and rumour was varies

positively.

Figure 5.12 illustrates sound-to-noise ratio (SNR) different levels at mobile topology. Mobile

topology consisted of 49 nodes and was densely populated at 100 x 100 m2

outdoor

environment. andom walk mobility model is in place for nodes movements. As we analysed

that at SNR level 1 drop packet ratio was noted greater that 25% at OSR because the nodes

moved at a random speed between 0 to 360 degree and this had an obvious impact on the

routing operation. As we increased the SNR level the drop packet ratio decreased in both the

OSR and rumour routing protocol. In terms of performance rumour performed better in a

dense network while OSR had slightly more interference and signal overhead.

Summary

In this chapter we described the implementation of the experiments which are, Experiment 1-

Small Nodes Scenario, Experiment 2- Medium Nodes Scenario, Experiment 3- Large Nodes

Scenario, Experiment No. 4- Simulation Time Variation, Experiment No. 5- Network

Deployment Area Variation, Experiment No. 7- Interference Handler Model Variation and

finally done the performance analysis of simulation results.


104

Chapter 6

Conclusion and Future Work

6.1. Introduction

Routing in the wireless sensor network (WSNs) is an emergent research area. The reason for

this high interest is the abrupt changes in the network topology and the network does not have

particular infrastructure. A number of routing protocols were proposed for WSNs but the

majority of them were tested for a limited number of scenarios and network performance

metric. To find out the best routing protocols in terms of performance under different

circumstances, is necessary to test it for a number of network performance metrics. In this

chapter we present the conclusions of our thesis, pointing out our contribution and stating

future work.

6.2. Conclusion

Routing Protocols play an important role in the wireless sensor networks and have a vivid

impact on the performance of the overall network. hesis presents novel routing protocols for

wireless sensor networks.

rop packet ratio is our core metric for measuring the rumour and OSR routing protocols

performance. We performed different simulated experiments under different conditions like

an increase in the number of nodes, density of network, simulation pause time variation,

interference SNR levels.

Furthermore, we investigated by means of ShoX simulation based experimentation that when

we have small number of stationary dense topology so we observed that both OSR and

rumour have a very slight variation in terms of performance against the packet drop ratio

metric. Conversely, while in mobile topology OSR and rumour drop packet ratio is noted

between 16-18%, so OSR performance deteriorates slightly in mobile topology due to abrupt

topological link updates and routing table maintenance.

In medium stationary dense topology, we observed that 25% of packets are dropping in case

of OSR routing protocol. On the other side, in rumour at mobile topology drop packet ratio

measurement is lower and overall rumour performs better at mobile nodes.


105

6.2.1. Reflection of our work on main research question

The sub-research questions which we set out to answer are the following:

Q1. Which protocol performs better in small, medium, and large network topologies?

After running hundreds of simulations for OSR and rumour routing protocols, we concluded

that rumour protocol performs better at both stationary and mobile topologies. Rumour

routing average drop packet ratio in small, medium and large topologies was lower than its

counterpart OSR routing protocol. In addition, the rumour routing protocol is more scalable

and less suffered from routing overhead in case of topological updates.

Q2. Which protocol performs better with different size of deployment area?

We conclude from our experiment, that when the area size of topology was increased from

100 m2 to 400 m

2. e observed that the deployment area size effect the routing operation. As

we analysed the enlargement of the field size, we found that the ratio of drop packets was

raised in both OSR and rumour routing protocols. In terms of performance rumour shows

better because the overall ratio at different deployment size is lower than OSR. It shows that

rumour is more adaptable and compatible with a different area size but condensed network.

Rumour packet drop ratio shows that rumour does not suit well for the environment where it

involves long routing path and scalability is important. On the other side, OSR can be used

for small networks with an undersized deployment area. As the size of the area grows OSR

drops more packets because OSR is a proactive protocol, and requires complete global

information of topology in case of updates such as link failure, nodes movements and so on.

Q3. Which protocol performs better at different simulation time?

We conclude that different simulation time at rumour and OSR performed at uniform level.

The input used during simulation time was the same so we observed that simulation times has

not shown an impact on protocols performance.

Q4. Which protocol suffers more from interference?

We observed that signal-to-noise ratio (SNR) has an impact on the operation of the routing

protocol. As we analysed that with different SNR level the ratio of drop packets varies. As

the level of SNR increases the rate of interference decreases and signal taking more strength.

As with strong signal the chances of path loss, re-routing was found in decline as result less

number of packets are dropped. OSR and Rumour routing protocol both show a uniform

decrease as the SNR level increased from 1 to 2. In stationary topology rumour dropped


106

fewer packets while in mobile topology rumour and OSR perform similar at SNR level 2 but

differ at SNR level 1 and 1.5 and at SNR level 1 at OSR drop packet ratio is noted as 27%.

The main question which was needed to be answered in this thesis is the following:

Q. Which of the two routing protocols: rumour protocol or OSR protocol performs better

under different circumstances?

We proved that rumour shows better performance under different circumstances such as

different nodes density, deployment area, and signal-to-noise ratio but in terms of rumour

limitation it does not perform well in sparse sensor network both at stationary and mobile

topologies. We also proved that OSR performance was degraded under different

circumstances such as different nodes density, deployment area. Meanwhile, OSR

performance was satisfactory in terms of signal-to-noise ratio threshold different values. In

terms of simulation times both rumour and OSR performance was found uniform under

different simulation time for the same set of inputs.

David Brahinsky, Deborah Estrin states that their simulation results shows that rumour

routing protocol gives an efficient delivery rate in large dense network under different

circumstances, and the algorithm also provides best fault tolerant mechanism at failure rate

of 20 % . [DX8]

Raghupathy et al., 1998, states that OSR routing protocol is suffering from large routing

overhead and therefore it is not a practical solution for ad hoc networks. [CX24]

The core contribution of our work can be broken into four parts;

First, the thesis provides a detailed study of routing protocols which includes wireless routing

protocols and their architecture, design challenges and other constraints.

Second, a comparative study of routing algorithms wireless sensor network can be presented

for the selected list of protocols in the WSN routing domain.

Third, an extensive number of simulation experiments and analysis were performed using

ShoX simulation.

Fourth, the thesis would be a guideline tool for the selection of an efficient routing protocol

and would alleviate the work of the developer and researcher in the routing domain in the

near future.


107

6.3. Future Work

Our results assessments shows two routing protocols rumour and OSR, which further need to

be for other network performance metrics such as end-to-end delay, energy consumption, and

so on. From our simulation observation we analysed that proactive routing protocols such as

OSR suffers from more drop packets at both dense and sparse networks because it involves

large routing overhead during updating topology. While reactive protocols such as rumour

performed better but some time suffer due to large flooding. Next, the presented wireless

sensor routing protocols can be deployed on real hardware with realistic performance

parameters for further investigation of their merits and de-merits routing protocols in a real

environment.

Our simulation observations did not involve the security aspect of rumour and OSR, so it

would be interesting to study security aspects of the stated routing protocols.


108

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Technology

osr routing

rumor routing protocols

rumor routing stationary

rumor routing mobile

rumor routing algorithm

conventional routing

adaptive routing protocols

parameters table experiment