GENERAL SELF-ORGANIZING TREE-BASED ENERGY BALANCE ROUTING PROTOCOL WITH CLUSTERING FOR WIRELESS SENSOR NETWORK

Transcription

1 GENERAL SELF-ORGANIZING TREE-BASED ENERGY BALANCE ROUTING PROTOCOL WITH CLUSTERING FOR WIRELESS SENSOR NETWORK A PROJECT REPORT Submitted by DIVYA P Register No: 14MCO010 in partial fulfillment for the requirement of award of the degree of MASTER OF ENGINEERING in COMMUNICATION SYSTEMS Department of Electronics and Communication Engineering KUMARAGURU COLLEGE OF TECHNOLOGY (An autonomous institution affiliated to Anna University, Chennai) COIMBATORE ANNA UNIVERSITY: CHENNAI APRIL 2016 i

2 GENERAL SELF-ORGANIZING TREE-BASED ENERGY BALANCE ROUTING PROTOCOL WITH CLUSTERING FOR WIRELESS SENSOR NETWORK A PROJECT REPORT Submitted by DIVYA P Register No: 14MCO010 in partial fulfillment for the requirement of award of the degree of MASTER OF ENGINEERING in COMMUNICATION SYSTEMS Department of Electronics and Communication Engineering KUMARAGURU COLLEGE OF TECHNOLOGY (An autonomous institution affiliated to Anna University, Chennai) COIMBATORE ANNA UNIVERSITY: CHENNAI APRIL 2016

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4 BONAFIDE CERTIFICATE Certified that this project report titled, GENERAL SELF-ORGANIZING TREE-BASED ENERGY BALANCE ROUTING PROTOCOL WITH CLUSTERING FOR WIRELESS SENSOR NETWORK, is the bonafide work of DIVYA P [Reg. No. 14MCO010] who carried out research under my supervision. Certified further, that to the best of my knowledge the work reported herein does not form part of any other project or dissertation on the basis of which a degree or award was conferred on an earlier occasion on this or any other candidate. HHH SIGNATURE Dr.S.SHIVKUMAR ASSOCIATE PROFESSOR PROJECT SUPERVISOR Department of ECE Kumaraguru College of Technology SIGNATURE Dr. A.VASUKI PROFESSOR AND HEAD Department of ECE Kumaraguru College of Technology Coimbatore Coimbatore The Candidate with Register No.14MCO010 was examined by us in the project viva-voice examination held on.. INTERNAL EXAMINER EXTERNAL EXAMINER ii

5 ABSTRACT A Wireless Sensor Network (WSN) consists of a large number of wireless sensor nodes which are deployed randomly and densely in a targeted region. The networks work properly till it is having sufficient battery power. So to optimize battery power, energy efficient routing protocols should be employed. The current work involves implementing routing protocols like General Self-Organized Tree- Based Energy-Balance routing protocol (GSTEB), Hybrid Energy-Efficient Distributed clustering approach (HEED) and Power Efficient Gathering in Sensor Information Systems (PEGASIS). Only one node communicates with Base Station (BS) at any point of time in PEGASIS. HEED is a clustering based technique which transmits data packet by cluster heads. But the energy level decreases due to Cluster Head when CH is far away from the BS. So GSTEB can be employed to reduce energy consumption. It builds a routing tree for each round. BS assigns a root node and broadcasts information about location awareness to all sensor nodes and it automatically organizes nodes when any node in tree has low battery power. This protocol prolongs network lifetime. Among these protocols GSTEB provides high throughput, packet delivery ratio and having longer battery time compared to HEED and PEGASIS. The disadvantage of GSTEB is that the average packet drop is slightly high with respect to time. So GSTEB is modified with clustering mechanism to reduce packet dropping. This provides scalability, reliability, less average drop and less energy consumption. The simulation was carried out using network simulator tool NS2. iii

6 ACKNOWLEDGEMENT First, I would like to express my praise and gratitude to the Lord, who has showered his grace and blessings enabling me to complete this project in an excellent manner. I express my sincere thanks to the management of Kumaraguru College of Technology and Joint Correspondent Shri Shankar Vanavarayar for his kind support and for providing necessary facilities to carry out the work. I would like to express my sincere thanks to our beloved Principal Dr.R.S.Kumar Ph.D., Kumaraguru College of Technology, who encouraged me with his valuable thoughts. I would like to thank Dr.A.Vasuki Ph.D., Head of the Department, Electronics and Communication Engineering, for her kind support and for providing necessary facilities to carry out the project work. In particular, I wish to thank with everlasting gratitude to the project coordinator Dr.M.Alagumeenaakshi Ph.D., Assistant Professor III, Department of Electronics and Communication Engineering, throughout the course of this project work. I am greatly privileged to express my heartfelt thanks to my project guide Dr.S.Shivkumar Ph.D., Associate professor, Department of Electronics and Communication Engineering, for his expert counselling and guidance to make this project to a great deal of success. I wish to convey my deep sense of gratitude to all teaching and non-teaching staff of ECE Department for their help and cooperation. Finally, I thank my parents and my family members for giving me the moral support and abundant blessings in all of my activities and my dear friends who helped me to endure my difficult times with their unfailing support and warm wishes. iv

7 TABLE OF CONTENTS CHAPTER NO ABSTRACT TITLE PAGE NO iii LIST OF FIGURES vii LIST OF TABLE viii LIST OF ABBREVIATIONS x 1 INTRODUCTION WIRELESS SENSOR NETWORK WSN CHARACTERISTICS WSN DESIGN ISSUE TYPES OF ROUTING PROTOCOLS APPLICATIONS OF WSN 7 2 LITERATURE SURVEY 8 3 EXISTING METHOD GSTEB PROTOCOL HEED PROTOCOL PEGASIS PROTOCOL 16 4 PROPOSED METHOD CLUSTERED GSTEB PROTOCOL 18 5 NS2 SIMULATION TOOL INTRODUCTION PLATFORMS REQUIRED RUNNING NETWORK SIMULATOR 5.3 BACKEND ENVIRONMENT OF NETWORK SIMULATOR v

8 5.4 BASICS OF TCL PROGRAMMING Initialization Creating the Output Files Defining nodes, links, queues and topology Agents and Applications Network Animator Tracing 31 6 RESULTS AND DISCUSSIONS SIMULATION PARAMETERS ILLUSTRATION OF PROTOCOLS GSTEB Protocol HEED Protocol PEGASIS Protocol Clustered GSTEB Protocol PERFORMANCE ANALYSIS Energy Level Graph Throughput Packet Delivery Ratio Packet Drop COMPARATIVE ANALYSIS 46 7 CONCLUSION 49 REFERENCES 50 LIST OF PUBLICATIONS 53 vi

9 LIST OF TABLE TABLE NO TABLE NAME PAGE NO 6.1 Simulation parameters 33 vii

10 FIGURE NO LIST OF FIGURES FIGURE NAME PAGE NO 1.1 Architecture of Wireless Sensor Network Block diagram of Wireless Sensor Nodes Node Deployment Flowchart of GSTEB Protocol Data Gathering in PEGASIS Protocol Flowchart of Clustered GSTEB Protocol Structure of Trace Files Execution of GSTEB in NAM Window Execution of HEED in NAM Window Execution of PEGASIS in NAM Window Execution of Clustered GSTEB in NAM Window Energy Level Graph for GSTEB Energy Level Graph for HEED Energy Level Graph for PEGASIS Energy Level Graph for Clustered GSTEB Throughput Graph for GSTEB Protocol Throughput Graph for HEED Protocol Throughput Graph for PEGASIS Protocol Throughput Graph for Clustered GSTEB Protocol Packet Delivery Ratio for GSTEB Protocol Packet Delivery Ratio for HEED Protocol 42 viii

11 6.15 Packet Delivery Ratio for PEGASIS Protocol Packet Delivery Ratio for Clustered GSTEB Protocol Packet Dropping Graph for GSTEB Protocol Packet Dropping Graph for HEED Protocol Packet Dropping Graph for PEGASIS Protocol Packet Dropping Graph for Clustered GSTEB Protocol Comparison of Energy level Comparison of Throughput Comparison of Packet Delivery Ratio Comparison of Packet Dropping Ratio 48 ix

12 LIST OF ABBREVIATIONS BS CH DSDV EL GSTEB HEED LEACH MEMS NAM Base Station Cluster Head Destination sequence distance vector Energy Level General Self-Organized Tree-Based Energy Balance Routing Protocol Hybrid Energy-Efficient Distributed Clustering Low-Energy Adaptive Clustering Hierarchy Micro-Electro-Mechanical Systems Network Ani Mator NS2 Network Simulator version 2 PEDAP PEGASIS TBC TBRP WSN Power Efficient Data Gathering And Aggregation Protocol Power-Efficient Gathering In Sensor Information Systems Tree Based Clustering Tree Based Routing Protocol Wireless Sensor Network x

13 1.1 WIRELESS SENSOR NETWORK CHAPTER 1 INTRODUCTION Wireless Sensor Network is begun to be deployed at an accelerated pace. It is reasonable to expect that whole world will be covered through the WSN in years and access to them through internet. It provides thousands of such nodes to communicate through wireless channels for information sharing as well as cooperative processing. The new technologies are exciting with unlimited potential for numerous application areas including environmental, medical, military, transportation, entertainment and smart spaces. A wireless sensor network is a collection of nodes organized into a concerted network. The sensor nodes are deployed randomly and densely in a targeted region. After the initial deployment of the network, sensor nodes are responsible for self-organizing an appropriate network infrastructure with multi-hop connections between sensor nodes. A wireless sensor node composes of sensing, computing, communication, actuation, and power components. These components are integrated on a single or multiple boards and packaged in a small number of cubic inches. The sensing unit is consisted of sensors and Analog to Digital Converters (ADC) which are used to collect and spread environmental data. The processing unit comprises of microprocessor and small memory storage used to store temporary data during processing. For commercial purpose, each node has a range between 2 and 512 kilobytes of RAM. The transceiver unit is used for sending and receiving data through a wireless channel. All these units are powered by batteries within the power unit. WSNs are very useful and can be deployed for support variety of applications. These are based on growing technologies like wireless communication technology, information technology, semiconductors, MEMS, micro systems technology and microsensors. 1

14 Fig 1.1 Architecture of wireless sensor network Fig 1.1 shows the architecture of wireless sensor network. It explains that the WSN is connected to an internet via Sink node from targeted node. 1.2 WSN CHARACTERSTICS Power consumption Ability to cope with node failures Mobility of nodes Scalability to large scale of deployment Ability to withstand harsh environmental conditions Fig 1.2 Block diagram of wireless sensor nodes 2

15 Fig 1.2 shows the Block diagram of wireless sensor nodes which consists of battery, sensor, ADC, Microprocessor and transceiver. 1.3 WSN DESIGN ISSUES At first WSNs was mostly motivated by military applications. Afterward civilian application domains of wireless sensor networks have been considered i.e., environmental and species monitoring, production and healthcare, smart home etc. These WSNs may consist of heterogeneous and mobile sensor nodes. The network topology may be as simple as a star topology. The scale and density of a network varies depending on the application. To meet up this common trend towards diversification, the following important design issues of the sensor network have to be considered. Fault Tolerance A few sensor nodes may be unsuccessful or be blocked owing to lack of power and it will have physical damage or environmental interference. The failure of sensor nodes should not affect the overall task of the sensor network. This is the reliability or fault tolerance issue. Fault tolerance is the ability to maintain sensor network utilities without any disruption because of sensor node failures. Scalability The number of sensor nodes organized in the sensing area may be in the order of hundreds, thousands or more and routing schemes should be scalable sufficient to react to events. Production Costs As the sensor networks consist of a huge number of sensor nodes, the price of a single node is very important to justify the on the whole cost of the networks and hence the cost of each sensor node has to be kept low. 3

16 Operating Environment We can able to set up the WSN in the interior of large machinery, on the underneath of an ocean, within a biologically or chemically contaminated field, in a battle field away from the enemy lines, in a home or a large building, in a large warehouse, attached to animals, attached to fast moving vehicles, in forest area for habitat monitoring etc. Power Consumption As the transmission power of a wireless radio is directly proportional to square of a distance or even higher order in the occurrence of obstacles, multi-hop routing will consume less energy than direct communication. However, multi-hop routing introduces significant overhead for topology management and medium access control. Sensor nodes are operated with limited power source (<0.5 Ah 1.2V). Node lifetime is robustly dependent on its battery lifetime. Data Delivery Models Data delivery models determine when the data composed by the nodes have to be delivered. Depending on the application, the data delivery model to the sink can be Continuous, Event driven, Query-driven and Hybrid. In the continuous delivery model, each sensor sends data occasionally. In event-driven models, the transmission of data is activated when an event occurs. In query driven models, the transmission of data is prompted when query made by the sink. A few networks apply a hybrid model using a combination of continuous, event-driven and query driven data delivery. Data Aggregation or Fusion While sensor nodes might produce significant redundant data, similar packets from multiple nodes can be combined. So the number of transmissions would be minimized. Data aggregation is the collection of data from various sources by using functions for example suppression, min, max and average. As computation would be reduced amount of energy consuming than communication, substantial energy savings can be obtained through data aggregation. This technique has been used to achieve energy efficiency and traffic optimization in a number of routing protocols 4

17 Quality Of Service (QoS ) The QoS means that the quality service is an essential depending on the application. It could be the length of life time, the data reliable, energy efficiency, and location-awareness, collaborative-processing. These factors will affect the selection of routing protocols for a particular application. In some of the applications, the data should be delivered within a certain period of time from the moment it is sensed. Data Latency And Overhead These are considered as the important things that manipulate routing protocol design. Data aggregation and multi-hop relay causes data latency. In addition, some routing protocols create extreme overheads to implement their algorithms, which are not suitable for serious energy constrained networks. Lifetime maximization: Energy or Power Consumption of the sensing device should be minimized and sensor nodes should provide energy efficiency since their limited energy resource determines their lifetime. To save power, wireless sensor nodes generally power off both the radio transmitter and the radio receiver while not in use. Node Deployment Node deployment is an application dependent and affects the performance of the routing protocol. The deployment is either deterministic or self-organizing. In deterministic situations, the sensors are manually placed and data is routed through predetermined paths. Though in self organizing systems, the sensor nodes are scattered randomly creating an infrastructure in an Ad-hoc manner. In that infrastructure, the position of the sink or the Cluster Head (CH) is also crucial in terms of energy efficiency and performance. When the distribution of nodes is not uniform, optimal positioning of cluster head becomes a pressing issue to enable energy efficient network operation. 5

18 Fig. 1.3 Node Deployment Fig 1.3 shows the node deployment. At the network layer, the intention is to find ways for energy efficient route setup and reliable relaying of data from the sensor nodes to the sink, in order to maximize the lifetime of the network. Moreover, the performance of the sensor network applications highly depends on the lifetime of the network. 1.4 TYPES OF ROUTING PROTOCOLS: In order to maximize the lifetime of WSN, Energy Efficient Routing Protocols should be employed. Types of routing protocols are, NODE-CENTRIC ROUTING: In WSNs, node centric communication is not a commonly expected communication type. Therefore, routing protocols designed for WSNs are more datacentric or geocentric. DATA-CENTRIC, OR LOCATION-AWARE ROUTING: In data-centric routing, the sink sends queries to certain regions and waits for data from the sensors located in the selected regions. Since data is being requested through queries, attribute based naming is necessary to specify the properties of data. Here data is usually transmitted from every sensor node within the deployment region with significant redundancy. In location aware routing nodes know where they are in a geographical 6

19 region. Location information can be used to improve the performance of routing and to provide new types of services. QOS BASED ROUTING: In QoS based routing protocols data delivery ratio, latency and energy consumption are mainly considered. To get a good QoS (Quality of Service), the routing protocols must possess more data delivery ratio, less latency and less energy consumption. To prolong the lifetime of the sensor nodes, routing protocols should be designed very efficiently. 1.5 APPLICATIONS OF WSN Wireless sensor networks have the potential to revolutionize telecommunications in a way similar to what we call the internet of things by offering a wide range of different applications some of which remain to be discovered. Sensor networks have a huge potential for applications in various fields, including: Environment and health: ocean temperature, collecting information on patients' conditions Management of critical industrial areas: monitoring of oil containers, checking the concentration of chemicals and gases Warehouse management and supply chain monitoring and historical states of the goods with the conditions of critical conservation Military applications: surveillance and recognition. 7

20 CHAPTER 2 LITERATURE SURVEY Akkaya.K et.al surveyed about recent routing protocols for sensor networks and presented a classification for the various approaches pursued. The three main categories explored in this paper [2] were data-centric, hierarchical and location-based. In this paper, following design issues considered that network dynamics, energy considerations, data delivery models, node capabilities and data aggregation/fusion. The author highlighted about the design trade of between energy and communication overhead savings in some of the routing paradigm, as well as the advantages and disadvantages of each routing technique. Although many of these routing techniques look promising, there are still many challenges that need to be solved in the sensor networks. Akyildiz.I.F et.al described the concept of sensor networks which had been made viable by the convergence of Micro Electro- Mechanical Systems Technology, wireless communications and digital electronics. The sensing tasks and the potential sensor networks applications were explored, and a review of factors influencing the design of sensor networks provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks were discussed [1]. Ameer Ahmed Abbasi et.al described about a survey on clustering algorithms for wireless sensor networks. Sensors in these applications were expected to be remotely deployed in large numbers and to operate autonomously in unattended environments. This mechanism used to support scalability. Nodes were often grouped into disjoint and mostly non-overlapping clusters. In this paper [3], they presented a taxonomy and general classification of published clustering schemes. They surveyed different clustering algorithms for WSNs, highlighting their objectives, features, complexity, etc. They 8

21 compared of these clustering algorithms based on metrics such as convergence rate, cluster stability, cluster overlapping, location awareness and support for node mobility. Chang.J.H et.al proposed the algorithm to select the routes and the corresponding power levels such that the time until the batteries of the nodes drain-out was maximized [4]. When there was a single power level, the problem was reduced to a maximum flow problem with node capacities and the algorithms converge to the optimal solution. When there were multiple power levels then the achievable lifetime was close to the optimal most of the time. It turned out that in order to maximize the life time, the traffic should be routed such that the energy consumption was balanced among the nodes in proportion to their energy reserves instead of routing to minimize the absolute consumed power. Heinzelman.W.B et.al developed and analyzed about architecture of Low-Energy Adaptive Clustering Hierarchy (LEACH) and it combined with energy-efficient clusterbased routing application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality [5]. LEACH included a new distributed cluster formation technique that enabled self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Their results showed that LEACH can improve system lifetime by an order of magnitude compared with generalpurpose multi hop approaches. Kevin L. Mills surveyed about Self-Organization in Wireless Sensor Networks. They first introduced scientific understanding of self-organizing systems and then identified the main models investigated by computer scientists seeking to apply self-organization to design large, distributed systems [8]. Subsequently, they surveyed research that uses models of self-organization in wireless sensor networks to provide a variety of functions: sharing processing and communication capacity; forming and maintaining structures; 9

22 conserving power; synchronizing time; configuring software components; adapting behavior associated with routing, with disseminating and querying for information, and with allocating tasks; and providing resilience by repairing faults and resisting attacks. They concluded with a summary of open issues that must be addressed before self organization can be applied routinely during design and deployment of senor networks and other distributed and computer systems. Liang.W et.al proposed that to maximize the network lifetime without any knowledge of future query arrivals and generation rates. In this paper [10], authors presented a generic cost model of energy consumption for data gathering queries if a routing tree was used for the query evaluation. They finally conducted experiments by simulation to evaluate the performance of the proposed algorithms in terms of network lifetime. The experimental results showed that one algorithm that took into account of both the residual energy and the volume of data at each sensor node significantly outperformed compared to others. Lindsey.S et.al introduced PEGASIS as the improvement of LEACH protocol. It makes the chain formation of nodes instead of making clusters. The farther node will transmit the data via its neighbor node and in this way chain was created. The last node in the chain called as leader node which will transmit all the data to sink node [11]. Compared to LEACH protocol PEGASIS had been shown performance of about % for different network topologies in WSN. PEGASIS will dissipate less energy because only leader node in chain will actively take part in data aggregation and data fusion. In PEGASIS each node should be aware of the remaining energy status of its neighbors. Node far away from the leader node will forward the data many times by the chain which causes the long time delay. So there was a problem of time delay in PEGASIS protocol which should be improved. 10

23 Mohammad Zeynali et.al introduced a new Tree Based Routing Protocol (TBRP) for improve network lifetime of the sensor nodes [14]. TBRP accomplished with a better performance in lifetime by balancing the energy load with respect to all the nodes. TBRP presented a new clustering factor for cluster head election, which can efficient to handle the heterogeneous energy capacities. It also introduced a fuzzy spanning tree for sending aggregated data to the base station. Rui Xu discussed on Survey of Clustering Algorithms. They surveyed clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrated their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics. Several tightly related topics, proximity measure, and cluster validation, are also discussed [16]. Cluster analysis examined unlabeled data, by either constructing a hierarchical structure, or forming a set of groups according to a prespecified number. That process included a series of steps, ranging from preprocessing and algorithm development, to solution validity and evaluation. Each of them was tightly related to each other and exerted great challenges to the scientific disciplines. They focused on the clustering algorithms and review a wide variety of approaches appearing in the literature. These algorithms evolved from different research communities and aim to solve different problems. Sohrabi et.al discussed about algorithms for Self-Organization of Wireless Sensor Networks. The protocols were provided scalability and highly energy constrained for large number of static nodes [18]. The protocols also supported for slow mobility by using a subset of the nodes, energy-efficient routing and formation of ad hoc sub networks for carrying out cooperative signal processing functions among a set of the nodes. Tan.H.O et.al proposed two new algorithms under name PEDAP (Power Efficient Data gathering and Aggregation Protocol) which were near optimal minimum spanning tree 11

24 based routing schemes where one of them was the power-aware version of the other. The algorithms [21] performed well both in systems where base station was far away from and where it was in the center of the field. PEDAP achieved improvement between 4x to 20x in network lifetime compared with LEACH and about three times improvement compared with PEGASIS. Travis C. Collier and Charles Taylor explained about Self-Organization in Sensor Networks [22]. They discussed a technical definition of the term self-organization which was self-organizing system as one where a collection of units coordinate with each other to form a system that adapted to achieve a goal more efficiently. They discussed some examples of self-organizing systems. Younis.O et.al states that HEED is a multi-hop wireless sensor network clustering algorithm that brought an energy-efficient clustering routing which is different from LEACH in the way of selecting the Cluster Head (CH) and which was selected based on the hybrid combination of the two parameters. The first parameter depends on the residual energy of the node and the second parameter was the cost of communications within the intra-cluster. The communication cost was the minimum power levels required by all nodes within the cluster range to reach the cluster head. In HEED protocol [23] each node can join only to one CH with one hop only. After a cluster formation, each node can be either elected to become a CH due to a probability or join a cluster according to CH messages. It gave more life time of sensor nodes than PEGASIS. But it had a disadvantage of loss transmission and decreases energy level in CH when CH was far away from the BS. Zhao Han et.al found that GSTEB is used to reduce energy consumption. It has number of rounds. It builds a routing tree for each round. BS assigns a root node and broadcasts information about location awareness to all sensor nodes and it automatically organizes nodes when any node in tree has low battery power. It consists of four phases for each 12

25 round. The four phases are Initial Phase, Tree Constructing Phase, Self-Organized Data Collecting and Transmitting Phase, Information Exchanging Phase. This protocol prolongs network lifetime. Among these protocols GSTEB provides high throughput, packet delivery ratio and having high remaining energy compared to HEED and PEGASIS. But one of the demerits in GSTEB is having average drop high when time increases [24]. 13

26 CHAPTER 3 EXISTING METHOD To minimize the highest energy consumption, GSTEB protocol is used. This protocol is compared with HEED and PEGASIS protocol which are used to prolong the network life time. But HEED and PEGASIS consumes more energy when nodes are far away from the BS. So GSTEB is introduced to offer a long life time. This protocol is based on tree structure and organizing nodes themselves automatically when energy decreases in root node. 3.1 GSTEB PROTOCOL GSTEB consists of several rounds. Each round consists of four phases. They are Initial phase, Tree constructing phase, Self-Organized Data Collecting and Transmitting Phase and Information exchanging phase. START INITIAL PHASE ROUND START TREE CONSTRUCTING PHASE SELF ORGANISED DATA COLLECTION AND TRANSMISSION INFORMATION EXCHANGING PHASE PACKET COUNTER=MAX XAXAXAXAX STOP Fig 3.1 Flowchart for GSTEB protocol 14

27 Fig 3.1 shows the flowchart for GSTEB Protocol where in initial phase, BS assigns a root node and it broadcasts information about location awareness to all sensor nodes. In tree constructing phase, every node chooses the parent from its neighbors according to Energy Level (EL) and every node records its neighbors neighbors information. In next phase, each sensor node collects information from child nodes to generate a data packet which needs to be transmitted to BS. In information exchanging phase, each node needs to generate and transmit a DATA_PKT in each round. So it may exhaust its energy. While all other nodes are monitoring the channel, they will receive this packet and perform an ID check. Then they modify their tables. If no such packet is received in the time slot, the network will start the next round. 3.2 HEED PROTOCOL This protocol is designed to select different cluster heads in a field according to the amount of energy distributed among the node. There are three phases in this protocol, Initialization phase Repetition phase Finalization phase INITIALIZATION PHASE: In this phase the Cluster Head is elected based on the following parameters, Residual energy. Communication cost. 15

28 REPETITION PHASE: this phase, In this phase its stops execution when CHprob reaches 1. There are two status in Tentative Status: The sensor becomes a tentative CH if its CHprob is less than 1. It can change its status to a regular node at a later iteration if it finds a lower cost CH. Final Status: The sensor permanently becomes a CH if its CHprob has reached 1. FINALIZATION PHASE: During this phase, each sensor makes a final decision depend on its status. It either picks the least cost CH or pronounces itself as CH. 3.3 PEGASIS PROTOCOL It is a family of routing and information gathering protocol. PEGASIS uses a chain structure. This protocol forms a chain among the sensor nodes, so that each node will receive and transmit data to the nearest neighboring node. Only one node is chosen as head node to transmit the aggregated data to the base station. PEGASIS protocol has two phases, Chain construction Data gathering. 16

29 CHAIN CONSTRUCTION: Greedy algorithm is used to construct the chain which is formed by connecting the neighboring nodes based on the signal strength of the node. DATA GATHERING: A node within the chain is selected as the leader node. Fig 3.2 Data gathering in PEGASIS Protocol Fig 3.2 shows the process of PEGASIS Protocol. Steps involved in this process is given by, Leader node issue token to the other nodes present in the network. Upon receiving the token, all other node transmit their data to their neighboring node towards the leader. The neighboring nodes aggregate the data and transmit to the leader node. The leader node, then transmit the aggregated data to the base station. 17

30 CHAPTER 4 PROPOSED WORK To prolong the network lifetime and minimize the highest energy consumption, Clustered GSTEB is used. The proposed work is that the GSTEB is modified by using clustering and then it will be employed with GSTEB protocol. This GSTEB protocol is based on tree structure. It is a self organizing network that the sensor nodes are automatically organized according to the energy. This Clustered GSTEB provides less energy consumption, less packet drops and high throughput CLUSTERED GSTEB PROTOCOL The Clustered GSTEB has following six steps. Initialization Phase Splitting number of clusters Choose the cluster head Tree constructing phase Self organized data collection and transmission Information exchange phase INITIALIZATION PHASE: The parameters of network are introduced in this phase. To consider parameters i.e., required area, number of nodes, initial energy, radio model, packet size, maximum packet size, routing protocol and so on. After these assumptions, Base Station (BS) sends a packet to all sensor nodes in specific area to inform them of starting time. Each sensor nodes send its location awareness to all sensor nodes i.e., in the specific radius circle. This packet consists of node ID, Energy Level (EL) and distance of nearest nodes. 18

31 START INITIAL PHASE ROUND START SPLIT IN TO NO OF CLUSTERS CHOOSE CLUSTER HEAD WHICH HAS HIGHEST ENERGY TREE CONSTRUCTING PHASE SELF ORGANISED DATA COLLECTION AND TRANSMISSION INFORMATION EXCHANGING PHASE PACKET COUNTER=MAX STOP Fig 4.1 Flowchart of Clustered GSTEB protocol 19

32 Fig 4.1 shows the flowchart of Clustered GSTEB protocol. It demonstrates that various steps involves in the proposed work. SPLITTING INTO NUMBER OF CLUSTERS: All sensor nodes are splitting in to number of clusters. Merit of this step is that the data packets send within cluster. So energy consuming will be reduced. CHOOSE CH: Cluster head is chosen according to the EL in this step. All nodes in the cluster send data packet to the cluster head. That CH node sends packet to sink node or BS. TREE CONSTRUCTING PHASE: In tree constructing phase, each node elect parent node from its neighbors according to Energy Level (EL) and every node records its neighbors neighbors information. The parent node is selected within the cluster by using EL. Lowest energy level nodes are acting as child nodes. These child nodes send packets to their parent node. That parent nodes send packet to their respective CH. CH sends the packets to BS. So distance will be reduced. EL should not be suddenly decreased. SELF ORGANIZED DATA COLLECTION AND TRANSMISSION: After construction of tree, each node gathers information and it will produce data packet. The data packet of child node sends to CH through parent node. CH sends packet to Base Station. Nodes are self organized with respect to the EL. 20

33 INFORMATION EXCHANGING PHASE: In this phase, parent node is exchanged when node exhausts its energy. After recognition of EL reduced, the parent node is exchanged in the next round of this phase. All nodes are monitoring the neighbouring nodes. Automatically nodes are updating changes. All packets are sent then it will move to next round. 21

34 CHAPTER 5 NS2 SIMULATION TOOL 5.1. INTRODUCTION The network simulator is a discrete event packet level simulator. The network simulator covers a very large number of applications of different kind of protocols of different network types consisting of different network elements and traffic models. Network simulator is a package of tools that simulates behavior of networks such as creating network topologies, log events that happen under any load, analyze the events and understand the network. 5.2 PLATFORMS REQUIRED RUNNING NETWORK SIMULATOR Unix and Unix like systems Linux (Use Fedora or Ubuntu versions) Free BSD SunOS/Solaris Windows 95/98/NT/2000/XP 5.3 BACKEND ENVIRONMENT OF NETWORK SIMULATOR Network Simulator is mainly based on two languages. They are C++ and OTcl. OTcl is the object oriented version of Tool Command language. The network simulator is a bank of different network and protocol objects. C++ helps in the following way: It helps to increase the efficiency of simulation. It is used to provide details of the protocols and their operation. It is used to reduce packet and event processing time. OTcl helps in the following way: 22

35 OTcl is to describe different network topologies It helps to specify the protocols and their applications It allows fast development Tcl is compatible with many platforms and it is flexible for integration Tcl is very easy to use and it is available in free 5.4 BASICS OF TCL PROGRAMMING To consider the following things: 1. Initialization and termination aspects of network simulator. 2. Defining the network nodes, links, queues and topology as well. 3. Defining the agents and their applications 4. Network Animator(NAM) 5. Tracing Initialization To start a new simulator 1. set ns [new Simulator] Above command is used to get that a variable ns is being initialized by using the set command. Here the code [new Simulator] is an instantiation of the class Simulator which uses the reserved word 'new'. So we can call all the methods present inside the class simulator by using the variable ns Creating the output files #To create the trace files set tracefile1 [open out.tr w] $ns trace-all $tracefile1 #To create the nam files set namfile1 [open out.nam w] $ns namtrace-all $namfile 23

36 In the above, to create an output trace file as out.tr and for nam visualization file out.nam. But in the Tcl script they are not called by their names declared, while they are called by the pointers initialized for them such as tracefile1 and namfile1 respectively. The line which starts with '#' commented. The next line opens the file 'out.tr' which is used for writing is declared 'w'. The next line uses a simulator method trace-all by which it will trace all the events in a particular format. The termination program is done by using a 'finish' procedure 01 # Defining the 'finish' procedure' 02 proc finish {} { 03 global ns tracefile1 namfile1 04 $ns flush-trace 05 close $tracefile 06 close $namfile 07 exec nam out.nam & 08 exit 0 09 } In the above the word 'proc' is used to declare a procedure called 'finish'. The word 'global' is used to tell what variables are being used outside the procedure. 'flush-trace' is a simulator method that dumps the traces on the respective files. The command 'close' is used to close the trace files and the command 'exec' is used to execute the nam visualization. The command 'exit' closes the application and returns 0 as zero(0) is default for clean exit. To end the program in ns by calling the 'finish' procedure 1 #end the program 2 $ns at "finish" Thus the entire operation ends at 125 seconds. To begin the simulation by using the command 24

37 1 #start the the simulation process 2 $ns run Defining nodes, links, queues and topology Way to create a node: view source print? 1 set n0 [ns node] In the above we created a node that is pointed by a variable n0. While referring the node in the script we use $n0. Similarly we create another node n2. Now we will set a link between the two nodes. 1 $ns duplex-link $n0 $n2 10Mb 10ms DropTail So we are creating a bi-directional link between n0 and n2 with a capacity of 10Mb/sec and a propagation delay of 10ms. In NS an output queue of a node is implemented as a part of a link whose input is that node to handle the overflow at the queue. But if the buffer capacity of the output queue is exceeded then the last packet arrived is dropped and here we will use a 'DropTail' option. Many other options such as RED (Random Early Discard) mechanism, FQ (Fair Queuing), DRR (Deficit Round Robin), SFQ (Stochastic Fair Queuing) are available. So now we will define the buffer capacity of the queue related to the above link 1 #Set queue size of the link 2 $ns queue-limit $n0 $n2 20 so, if summarize the above three things we get 01 #create nodes 02 set n0 [$ns node] 03 set n1 [$ns node] 04 set n2 [$ns node] 25

38 05 set n3 [$ns node] 06 set n4 [$ns node] 07 set n5 [$ns node] 08 #create links between the nodes 09 $ns duplex-link $n0 $n2 10Mb 10ms DropTail 10 $ns duplex-link $n1 $n2 10Mb 10ms DropTail 11 $ns simplex-link $n2 $n3 0.3Mb 100ms DropTail 12 $ns simplex-link $n3 $n2 0.3Mb 100ms DropTail 13 $ns duplex-link $n0 $n2 0.5Mb 40ms DropTail 14 $ns duplex-link $n0 $n2 0.5Mb 40ms DropTail 15 #set queue-size of the link (n2-n3) to $ns queue-limit $n2 $n Agents and applications TCP TCP is a dynamic reliable congestion protocol which is used to provide reliable transport of packets from one host to another host by sending acknowledgements on proper transfer or loss of packets. Thus TCP requires bi-directional links in order for acknowledgements to return to the source. Now we will show how to set up tcp connection between two nodes 1 #setting a tcp connection 2 set tcp [new Agent/TCP] 3 $ns attach-agent $n0 $tcp 4 set sink [new Agent/TCPSink] 5 $ns attach-agent $n4 $sink 6 $ns connect $tcp $sink 7 $tcp set fid_1 8 $tcp set packetsize_552 26

39 The command 'set tcp [new Agent/TCP]' gives a pointer called 'tcp' which indicates the tcp agent which is an object of ns. Then the command '$ns attach-agent $n0 $tcp' defines the source node of tcp connection. Next the command 'set sink [new Agent/TCPSink]' defines the destination of tcp by a pointer called sink. The next command '$ns attach-agent $n4 $sink' defines the destination node as n4. Next, the command '$ns connect $tcp $sink' makes the TCP connection between the source and the destination i.e., n0 and n4 when we have several flows such as TCP, UDP etc in a network. So, to identify these flows we need to mark these flows by using the command '$tcp set fid_1'. In the last line we set the packet size of tcp as 552 while the default packet size of tcp is FTP over TCP File Transfer Protocol (FTP) is a standard mechanism provided by the Internet for transferring files from one host to another. FTP differs from other client server applications in that it establishes between the client and the server. One connection is used for data transfer and other one is used for providing control information. FTP uses the services of the TCP. It requires two connections. The well Known port 21 is used for control connections and the other port 20 is used for data transfer. Well here we will learn in how to run a FTP connection over a TCP 1 #Initiating FTP over TCP 2 set ftp [new Application/FTP] 3 $ftp attach-agent $tcp In above, the command 'set ftp [new Application/FTP]' gives a pointer called 'ftp' which indicates the FTP application. Next, we attach the ftp application with tcp agent as FTP uses the services of TCP. 27

40 UDP The User datagram Protocol is one of the main protocols of the Internet protocol suite.udp helps the host to send messages in the form of datagrams to another host which is present in an Internet protocol network without any kind of requirement for channel transmission setup. UDP provides a unreliable service and the datagrams may arrive out of order, appear duplicated, or go missing without notice. UDP assumes that error checking and correction is either not necessary or performed in the application, avoiding the overhead of such processing at the network interface level. Time-sensitive applications often use UDP because dropping packets is preferable to waiting for delayed packets, which may not be an option in a real-time system. Now we will learn how to create a UDP connection in network simulator. 1 # setup a UDP connection 2 set udp [new Agent/UDP] 3 $ns attach-agent $n1 $udp 4 $set null [new Agent/Null] 5 $ns attach-agent $n5 $null 6 $ns connect $udp $null 7 $udp set fid_2 Similarly, the command 'set udp [new Agent/UDP]' gives a pointer called 'udp' which indicates the udp agent which is a object of ns. Then the command '$ns attachagent $n1 $udp' defines the source node of udp connection. Next the command 'set null [new Agent/Null]' defines the destination of udp by a pointer called null. The next command '$ns attach-agent $n5 $null' defines the destination node as n5.next, the command '$ns connect $udp $null' makes the UDP connection between the source and the destination.i.e n1 and n5.when we have several flows such as TCP, UDP etc in a network. So, to identify these flows we mark these flows by using the command '$udp set fid_2 28

41 Constant Bit Rate (CBR) Constant Bit Rate (CBR) is a term used in telecommunications, relating to the quality of service. When referring to codecs, constant bit rate encoding means that the rate at which a codec's output data should be consumed is constant. CBR is useful for streaming multimedia content on limited capacity channels since it is the maximum bit rate that matters, not the average, so CBR would be used to take advantage of all of the capacity. CBR would not be the optimal choice for storage as it would not allocate enough data for complex sections (resulting in degraded quality) while wasting data on simple sections. CBR over UDP Connection 1 #setup cbr over udp 2 set cbr [new Application/Traffic/CBR] 3 $cbr attach-agent $udp 4 $cbr set packetsize_ $cbr set rate_0.01mb 6 $cbr set random _false In the above we define a CBR connection over a UDP one. Well we have already defined the UDP source and UDP agent as same as TCP. Instead of defining the rate we define the time interval between the transmission of packets in the command '$cbr set rate_0.01mb'. Next, with the help of the command '$cbr set random _false' we can set random noise in cbr traffic. We can keep the noise by setting it to 'false' or we can set the noise on by the command '$cbr set random _1'. We can set by packet size by using the command '$cbr set packetsize_(packetsize).we can set the packet size up to sum value in bytes. 29

42 Scheduling Events In ns the tcl script defines how to schedule the events or in other words at what time which event will occur and stop. This can be done using the command $ns at. So here in our program we will schedule the ftp and cbr. 1 # scheduling the events 2 $ns at 0.1 "cbr start" 3 $ns at 1.0 "ftp start" 4 $ns at "ftp stop" 5 $ns at "cbr stop" Network Animator (NAM) When we will run the above program in ns then we can visualize the network in the NAM. But instead of giving random positions to the nodes, we can give suitable initial positions to the nodes and can form a suitable topology. So, we can give positions to the nodes in NAM in the following way 1 #Give position to the nodes in NAM 2 $ns duplex-link-op $n0 $n2 orient-right-down 3 $ns duplex-link-op $n1 $n2 orient-right-up 4 $ns simplex-link-op $n2 $n3 orient-right 5 $ns simplex-link-op $n3 $n2 orient-left 6 $ns duplex-link-op $n3 $n4 orient-right-up 7 $ns duplex-link-op $n3 $n5 orient-right-down We can also define the color of cbr and tcp packets for identification in NAM. For this we use the following command 30

43 1 #Marking the flows 2 $ns color1 Blue 3 $ns color2 Red To view the network animator we need to type the command: nam Tracing Tracing Objects NS simulation can produce visualization trace as well as ASCII file corresponding to the events that are registered at the network. While tracing ns inserts four objects: EnqT, DeqT, RecvT & DrpT. EnqT registers information regarding the arrival of packet and is queued at the input queue of the link. When overflow of a packet occurs, then the information of the dropped packet is registered in DrpT. DeqT holds the information about the packet that is dequeued instantly. RecvT hold the information about the packet that has been received instantly. Structure of Trace files Fig 5.1 Structure of Trace files 1. The first field is event. It gives you four possible symbols '+' '-' 'r' 'd'. These four symbols correspond respectively to enqueued, dequeued, received and dropped. 2. The second field gives the time at which the event occurs 3. The third field gives the input node of the link at which the event occurs 4. The fourth field gives the output node at which the event occurs 31

44 5. The fifth field shows the information about the packet type i.e., whether the packet is UDP or TCP 6. The sixth field gives the packet size 7. The seventh field give information about some flags 8. The eighth field is the flow id (fid) for IPv6 that a user can set for each flow in a tcl script. It is also used for specifying the color of flow in NAM display 9. The ninth field is the source address 10. The tenth field is the destination address 11. The eleventh field is the network layer protocol's packet sequence number 12. The last field shows the unique id of packet Following are trace of two events: r cbr r ack The trace file can be viewed with the cat command: cat out.tr 32

45 6.1 SIMULATION PARAMETERS CHAPTER 6 RESULTS AND DISCUSSION The simulation was carried out using NS2. Table 6.1: Simulation Parameters Number of nodes 40 Area size 1000*1000m Simulation time 30ms Packet size 100 bytes Maximum number of packets 300 Routing protocol DSDV Initial energy level 100J Model Two ray model Mac IEEE ILLUSTRATIONS OF PROTOCOLS GSTEB PROTOCOL Fig. 6.1 Execution of GSTEB in NAM window 33

46 Fig 6.1 shows the execution of GSTEB Protocol. In this figure, Red color node represents the BS. Violet color nodes and orange color node represent the root nodes, parent nodes and child node. In initial Phase, BS station sends packet to all sensor nodes. Automatically, all nodes calculate their energy level which is sent to neighbor nodes. Those nodes update their neighbors information into their own table. Then, Tree is to be constructed i.e., parent and child nodes are assigned according to energy. Data packets are transmitted from child nodes to root nodes via Parent nodes. Then root nodes only send packets to BS HEED PROTOCOL Fig.6.2 Execution of HEED in NAM window Fig 6.2 shows the execution of HEED protocol. In which combination of red color and maroon color node is BS. 40 nodes are splitted in to 8 numbers of clusters. Then 34

47 Cluster Head will be chosen according to energy. Data packets are sent to CH from nodes in clusters. Then, it will be sent to Sink Node from all CH PEGASIS PROTOCOL Fig.6.3 Execution of PEGASIS in NAM window Fig.6.3 shows the execution of PEGASIS protocol. Blue color node represents BS. Black color node act as a leader node. Green color and violet color nodes are used to form a chain. Data packets are collected by starting from both endpoints of the chain, transmitted along the chain and fused each time it transmits from one node to the next node until it reaches the leader. That leader node only communicates to Base Station. 35

48 CLUSTERED GSTEB PROTOCOL Fig 6.4 Execution of Clustered GSTEB in NAM window Fig 6.4 shows the NAM output for clustered GSTEB in NAM window. In this, clusters are represented using various colors like blue, orange, black, yellow, pink and etc. Red color node is represented as sink node. In this, child nodes send packets to Parent nodes and those Parent nodes are sent to CH. All CHs send packet to sink node. 6.3 PERFORMANCE ANALYSIS Energy Level, Packet dropping ratio, Packet drop and Throughput are used to evaluate the performance of GSTEB, HEED and PEGASIS and Clustered GSTEB. 36

49 6.3.1 ENERGY LEVEL GRAPH: Fig 6.5 Energy Level for GSTEB Protocol Fig 6.5 shows energy level for GSTEB protocol. This protocol has lowest energy consumption compared to HEED and PEGASIS protocol. Fig 6.6 Energy level Graph for HEED Protocol 37

50 Fig 6.6 shows energy level graph for HEED protocol. This protocol consumes more energy compared to GSTEB and Clustered GSTEB protocol. Fig 6.7 Energy level Graph for PEGASIS Protocol Fig 6.7 shows energy level for PEGASIS protocol. This protocol consumes more energy compared to all other protocols. 38

51 Fig 6.8 Energy level Graph for Clustered GSTEB Protocol Fig 6.8 shows energy level for Clustered GSTEB protocol. This protocol consumes less energy compared to all other protocols. The GSTEB protocol is modified with clustering THROUGHPUT Throughput is defined as a total number of packets delivered over the total simulation time. This parameter is more important to analyze the performance of all sensor networks. 39

52 Fig 6.9 Throughput graph for GSTEB Protocol Fig 6.9 shows the throughput graph for GSTEB Protocol. This protocol has high throughput compared to HEED and GSTEB protocol. Fig 6.10 Throughput graph for HEED Protocol 40

53 Fig 6.10 shows the throughput graph for HEED Protocol. It has low throughput compared to all other protocols. Fig 6.11 Throughput graph for PEGASIS Protocol Fig 6.11 shows the throughput graph for GSTEB Protocol. This protocol has high throughput compared to HEED. 41

54 Fig 6.12 Throughput graph for Clustered GSTEB Protocol Fig 6.12 shows the throughput graph for Clustered GSTEB Protocol. This protocol has high throughput compared to all other protocols Packet Delivery Ratio It is defined as the ratio of the number of delivered data packet to the destination. 42

55 Fig 6.13 Packet delivery ratio for GSTEB Protocol Fig 6.13 shows the Packet delivery ratio graph for GSTEB Protocol. This protocol has highest Packet delivery ratio compared to HEED and PEGASIS protocol. But it has low delivery ratio at the end. Fig 6.14 Packet delivery ratio graph for HEED Protocol Fig 6.14 shows the Packet delivery ratio graph for HEED Protocol. This protocol has lowest Packet delivery ratio. 43

56 Fig 6.15 Packet delivery ratio graph for PEGASIS Protocol Fig 6.15 shows the Packet delivery ratio graph for PEGASIS Protocol. This protocol has Packet delivery ratio as low at end of the execution. Fig 6.16 Packet delivery ratio graph for Clustered GSTEB Protocol Fig 6.16 shows the Packet delivery ratio graph for Clustered GSTEB Protocol. This protocol has highest Packet delivery ratio compared to all other protocols. 44

57 6.3.4 PACKET DROP Packet dropping ratio indicates how much amount of packet drops, after the transmission of all the packets. Fig 6.17 Packet dropping Graph for GSTEB Protocol Fig 6.17 shows the Packet dropping ratio graph for GSTEB Protocol. This protocol has packet dropping ratio as low. But it will be high at end of round. Fig 6.18 Packet Dropping Graph for HEED Protocol 45

58 Fig 6.18 shows the Packet dropping ratio graph for GSTEB Protocol. This protocol has more packets dropping ratio. Fig 6.19 Packet Dropping Graph for PEGASIS Protocol Fig 6.19 shows the Packet dropping ratio graph for PEGASIS Protocol. This protocol has the packet dropping ratio as high. Fig 6.20 Packet Dropping Graph for Clustered GSTEB Protocol 46

59 Fig 6.20 shows the Packet dropping ratio graph for Clustered GSTEB Protocol. This protocol has no packet dropping ratio. 6.4 COMPARATIVE ANALYSIS This provides comparative analysis of HEED, PEGASIS, GSTEB and Modified GSTEB. The parameters of performance analysis are throughput, energy consumption, packet delivery ratio and packet dropping ratio. Fig 6.21 Comparison of Energy level- GSTEB, HEED, PEGASIS and Clustered GSTEB Fig 6.21 shows a comparison of energy consumption- GSTEB, HEED, PEGASIS and Clustered GSTEB. It explains that Clustered GSTEB has lowest energy consumption compared to others. Red color denotes the Clustered GSTEB; green color represents the GSTEB, blue color node represents the HEED and yellow color represents the PEGASIS. 47

60 Fig 6.22 Comparison of Throughput- GSTEB, HEED, PEGASIS and Clustered GSTEB Fig 6.22 shows a comparison of throughput- GSTEB, HEED, PEGASIS and Clustered GSTEB. It explains that Clustered GSTEB has highest throughput compared to others. Red color denotes the Clustered GSTEB; green color represents the GSTEB, blue color node represents the HEED and yellow color represents the PEGASIS. Fig 6.23 Comparison of packet delivery ratio - GSTEB, HEED, PEGASIS and Clustered GSTEB 48

61 Fig 6.23 shows a comparison of packet delivery ratio - GSTEB, HEED, PEGASIS and Modified GSTEB. It explains that Clustered GSTEB has highest packet delivery ratio compared to others. Red color denotes the Clustered GSTEB, green color represents the GSTEB, blue color node represents the HEED and yellow color represents the PEGASIS. Fig 6.24 Comparison of packet dropping ratio - GSTEB, HEED, PEGASIS and Clustered GSTEB Fig 6.24 shows a comparison of packet dropping ratio- GSTEB, HEED, PEGASIS and Clustered GSTEB. It explains that Clustered GSTEB has fewer packets compared to others. Red color denotes the Clustered GSTEB; green color represents the GSTEB, blue color node represents the HEED and yellow color represents the PEGASIS. 49