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Comparative Study of Routing Protocols in Wireless Sensor Networks by Abid Afsar Khan Malang Palsapi

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Comparative Study of Routing Protocols in Wireless Sensor Networks by Abid Afsar Khan Malang Palsapi

Comparative Study of Routing Protocols in Wireless Sensor Networks by Abid Afsar Khan Malang Palsapi

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  • 1. Comparative Performance Study of Routing Protocols in Wireless Sensor Network By Abid Afsar Khan Malang Palsapi (‫فی‬ ‫س‬ ‫ل‬ ‫ف‬ ‫نگ‬ ‫ل‬ ‫م‬ ‫خان‬ ‫سر‬ ‫اف‬ ‫د‬ ‫)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
  • 2. 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
  • 3. 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
  • 4. 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
  • 5. 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
  • 6. 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 4.8 Parameters Table -Exp 2- Case 3: Mobile nodes using rumor routing 81 4.9 Parameters Table -Exp 2- Case 4: Mobile nodes using rumor routing 82 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 4.15 Parameters Table -Experiment 4- case 2-Simulation Time 84 4.16 Parameters Table -Experiment 4- case 3-Simulation Time 84 4.17 Parameters Table -Experiment 4- case 4-Simulation Time 84 4.18 Parameters Table -Experiment 5- case 1-Deployement Area 85
  • 7. vi Sr. No. List of Tables Page No. 4.19 Parameters Table -Experiment 5- case 2-Deployment Area 85 4.20 Parameters Table -Experiment 5- case 3-Deployment Area 85 4.21 Parameters Table -Experiment 5- case 4-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 4.24 Parameters Table -Experiment 6- Case 3- Interference handler model 86 4.25 Parameters Table -Experiment 6- Case 4- Interference handler model 86 5.1 Parameters of IEEE802.11g WLAN standards 91 5.2 Parameters of IEEE802.11g WLAN standards 95 5.3 Parameters of IEEE802.11g WLAN standards 98 5.4 Parameters of IEEE802.11g WLAN standards 103 5.5 Parameters of IEEE802.11g WLAN standards 107 5.6 Parameters of IEEE802.11g WLAN standards 111
  • 8. 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
  • 9. viii Sr. No. List Of Figures Page 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 4.5 Network topology of 25 nodes 80 4.6 Network topology of 49 nodes 82 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
  • 10. ix Sr. No. List Of Figures Page 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 ≤ R2 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: ___________________
  • 11. 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.
  • 12. 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
  • 13. 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
  • 14. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 15. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 16. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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?
  • 17. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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)
  • 18. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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)
  • 19. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 20. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 21. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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]
  • 22. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 23. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 24. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 25. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 26. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 27. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 28. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 29. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 30. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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:
  • 31. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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].
  • 32. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 19 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.
  • 33. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 20 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
  • 34. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 21 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)
  • 35. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 22 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
  • 36. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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].
  • 37. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 24 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.
  • 38. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 25 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.
  • 39. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 40. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 27 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
  • 41. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 42. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 29 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
  • 43. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 30 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
  • 44. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 45. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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]
  • 46. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 33 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.
  • 47. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 34 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
  • 48. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 49. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 50. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 51. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 38 • 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
  • 52. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 53. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 54. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 55. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 42 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.
  • 56. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 43 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.]
  • 57. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 44 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
  • 58. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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,
  • 59. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 60. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 47 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.
  • 61. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 48  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 ≤ R2 --------------------------Equation 3.1 Where r ≤ R∕√5 [8]
  • 62. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 49  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
  • 63. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 50 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.
  • 64. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 51 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‟.
  • 65. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 52 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,
  • 66. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 53 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
  • 67. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 68. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 55 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
  • 69. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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]
  • 70. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 57 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.
  • 71. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 72. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 73. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 74. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 75. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 76. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 77. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 78. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 79. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 80. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 81. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 82. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 83. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 70 Figure 4.5: Network topology of 25 nodes 4.7.2.1. Case 1: Stationary nodes using rumour routing 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 No. Of Nodes Initial Nodes Distribution Mobility Model Application 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
  • 84. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 71 4.7.2.3. Case 3: Stationary nodes using OSR routing 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 4.8: Exp 2- Case 3: Mobile nodes using rumour routing 4.7.2.4. Case 4: Mobile nodes using OSR routing No. Of Nodes Initial Nodes Distribution Mobility Model Application Traffic Signal Propagation model 25 Random Start Position Random Walk One time random traffic generator Simple Physics Parameters Table 4.9: Exp 2- Case 4: Mobile nodes using rumour routing 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.
  • 85. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 72 Figure 4.6: Network topology of 49 nodes 4.7.3.1. Case 1: Stationary nodes using rumour routing No. Of Nodes Initial Nodes Distribution Mobility Model Application Traffic Signal Propagation model 49 Random Start Position No Movement One time 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 No. Of Nodes Initial Nodes Distribution Mobility Model Application Traffic Signal Propagation model 49 Random Start Position Random Walk One time random traffic generator Simple Physics Parameters Table 4.11: Exp 3- Case 2: Mobile Nodes using rumour routing
  • 86. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 73 4.7.3.3. Case 3: Stationary nodes using OSR routing No. Of Nodes Initial Nodes Distribution Mobility Model Application Traffic Signal Propagation model 49 Random Start Position No Movement One time random traffic generator Simple Physics Parameters Table 4.12: Exp 3- Case 3: Stationary Nodes using OSR routing 4.7.3.4. Case 4: Mobile nodes using OSR routing No. Of Nodes Initial Nodes Distribution Mobility Model Application Traffic Signal Propagation model 49 Random Start Position No Movement One time 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 Parameters Table 4.15: Experiment 4- case 2-Simulation Time 4.7.4.3. Case 3: OSR routing having stationary nodes Number of nodes Simulation Time Mobility Model 49 40,60,80,100 seconds No Movement Parameters Table 4.16: Experiment 4- case 3-Simulation Time
  • 87. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 74 4.7.4.4. Case 4: OSR routing having mobile nodes Number of nodes Simulation Time Mobility Model 49 40,60, 80,100 seconds Random Walk Parameters Table 4.17: Experiment 4- case 4-Simulation Time 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. 4.7.5.1. Case 1: Rumour routing having stationary nodes 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 Number of nodes Deployment Area Mobility Model 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 Number of nodes Deployment Area Mobility Model 49 300 x 400 m2 No Movement Parameters Table 4.20: Experiment 5- case 3-Deployment Area 4.7.5.4. Case 4: OSR outing having mobile nodes Number of nodes Deployment Area Mobility Model 49 300 x 400 m2 Random Walk Parameters Table 4.21: Experiment 5- case 4-Deployment Area
  • 88. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 75 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. 4.7.6.1. Case 1: Rumour routing having stationary nodes 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) Parameters Table 4.23: Experiment 6- Case 2- Interference handler model 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) Parameters Table 4.24: Experiment 6- Case 3- Interference handler model 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) Parameters Table 4.25: Experiment 6- Case 4- Interference handler model 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,
  • 89. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 76 and design of interference handler model variation. In the next chapter we will perform implementation of our proposed experiments.
  • 90. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 91. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 92. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 93. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 94. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 81 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. Network Model 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 net.sf.shox.simulator.movement.RandomStartPositions model of ShoX. Figure 5.2: Relative performance comparison in small mobile topology of 10 nodes
  • 95. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 83 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 net.sf.shox.simulator.movement.RandomWalk. The simulation parameter was adjusted as 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. 5.4.1.1.3. Node Architecture 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 net.sf.shox.simulator.node.user.os.serviceManager class. Third, 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 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 responsible for the multiplexing mechanism, and it also deals with flow control, the 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.
  • 96. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 84 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 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. 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, 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 5.2: Parameters of IEEE802.11g WLAN standards 5.4.1.1.4. Signal Propagation 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.
  • 97. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 85 5.4.1.1.5. Simulation Time 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
  • 98. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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 5.5.1.1. Network Model 5.5.1.1.1. Network Topology 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 net.sf.shox.simulator.movement.RandomStartPositions model of ShoX. 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. The traffic model implemented for cases 1, 2, 3 and 4 was 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.
  • 99. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 87 5.5.1.1.3. Node Architecture The six layer model was employed and the first application layer was implemented using net.sf.shox.simulator.node.user.datamanagement.rumor.RumorEvaluationApplicationLayer 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 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 parameters from serviceManager and can be accessed from net.sf.shox.simulator.node.user.os.serviceManager class. Third, the network layer was implemented using the ShoX model, 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 the 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 mmedium access layer (MAC) is the lower sub-layer of the data link layer in 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 was AARF. The number of consecutive successful bit rate before raising the bit rate was 10. The consecutive number of transmission 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. 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,
  • 100. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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] Table 5.3: Parameters of IEEE802.11g WLAN standards 5.5.1.1.4. Signal Propagation 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 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. 5.5.1.1.5. Simulation Time 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.
  • 101. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 102. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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 5.6.1.1. Network Model 5.6.1.1.1. Network Topology 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 5.6.1.1.2. Network Behaviour 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 net.sf.shox.simulator.movement.RandomWalk. The simulation parameter was adjusted as static. Is this not contradictory? Sorry maybe I‟m missing something but it seems to contradict the first sentence. 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 set at low, medium and high. 5.6.1.1.3. Node Architecture The six layer model was implemented and the first application layer was ted net.sf.shox.simulator.node.user.datamanagement.rumor.RumorEvaluationApplicationLayer model. The Rumour Application Layer is the model used for rumour routing algorithm configuration, and it also used parameter such as distinctValues.
  • 103. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 91 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 operating system, and it used the parameter such as serviceManger and can be accessed from net.sf.shox.simulator.node.user.os.serviceManager class. Thirdly, the network layer was implemented using the ShoX model net.sf.shox.simulator.node.user.OptimalSourceRouting. Optimal source routing is a class used for routing in ShoX. Fourthly, 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 the OSI model. LLC is 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 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 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 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
  • 104. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 92 Table 5.4: Parameters of IEEE802.11g WLAN standards 5.6.1.1.4. Signal Propagation 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. 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. 5.6.1.1.5. Simulation Time 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
  • 105. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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. 5.7.1. Cases 1, 2, 3 and 4: Measurement of packet drop at stationary topology using OSR and rumour routing algorithm 5.7.1.1. Network Model 5.7.1.1.1. Network Topology 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. 5.7.1.1.2. Network Behaviour 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
  • 106. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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. 5.7.1.1.3. Node Architecture The six layer model was implemented and the first application layer was used with: net.sf.shox.simulator.node.user.datamanagement.rumor.RumorEvaluationApplicationLayer 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 net.sf.shox.simulator.node.user.OptimalSourceRouting. Optimal source routing is a class used for routing in ShoX. 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. net.sf.shox.simulator.node.user.MAC_IEEE802_11bg_DCF. The maximum number of retries 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
  • 107. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 95 using distributed coordination function (DCF) technique, and their implemented parameters 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] Table 5.5: Parameters of IEEE802.11g WLAN standards 5.7.1.1.4. Signal Propagation 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. 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. 5.7.1.1.5. Simulation Time 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.
  • 108. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 109. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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. 5.8.1. Cases 1, 2, 3 and 4: Measurement of packet drop at stationary topology using OSR and rumour routing algorithm 5.8.1.1. Network Model 5.8.1.1.1. Network Topology 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 net.sf.shox.simulator.movement.RandomStartPositions model of ShoX. 5.8.1.1.2. Network Behaviour 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 net.sf.shox.simulator.movement.RandomWalk. The simulation parameter was adjusted as static. The traffic model implemented for cases 1, 2, 3 and 4 was 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.
  • 110. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 98 5.8.1.1.3. Node Architecture The six layer model was implemented with the first application layer using net.sf.shox.simulator.node.user.datamanagement.rumor.RumorEvaluationApplicationLayer 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 net.sf.shox.simulator.node.user.os.BasicOperatingSystemLayer model. The operating system 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 net.sf.shox.simulator.node.user.OptimalSourceRouting. Optimal source routing is a class used for routing in ShoX. 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. net.sf.shox.simulator.node.user.MAC_IEEE802_11bg_DCF. The maximum number of retries 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 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 using distributed coordination function (DCF) technique, and their implemented parameters are shown in the table, 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] Table 5.6: Parameters of IEEE802.11g WLAN standards
  • 111. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 99 5.8.1.1.4. Signal Propagation 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. 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. 5.8.1.1.5. Simulation Time 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
  • 112. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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
  • 113. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 114. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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%
  • 115. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 116. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 117. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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 m2. 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
  • 118. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
  • 119. Comparative Performance Study of Routing Protocols in Wireless Sensor Network 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.
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