Global Journal of Advanced Engineering Technologies, Vol3, Issue ISSN:

Transcription

1 PERFORMANCE EVALUATION OF ROUTING PROTOCOL IN MANETS USING GENETIC ALGORITHM/PARTICLE SWARM OPTIMIZATION 1 Ramandeep Kaur Gill, 2 Rupinder Kaur Gurm 1 Research Scholar, Department of CSE, RIMT-IET, Mandi Gobindgarh, Fatehgarh Sahib, Punjab, India, 2 Assistant Professor, Department of CSE, RIMT-IET, Mandi Gobindgarh, Fatehgarh Sahib, Punjab, India, Abstract: - As we know in MANET route optimization is one of the basic requirements to improve the quality of service (QoS) of the network. To improve the Qos of the network we use different changes in MANET protocols, its parameters routing algorithms, routing techniques and many more. In the proposed work we are improving the QoS by enhancing an existing optimization technique that is inspired from Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Then an enhanced hybrid GA-PSO is implemented over Proactive routing Protocol that is. The aim of this research paper is to modify using existing and enhanced optimization techniques to improve the QoS of the network. Simulation has carried out in terms of performance matrices using NS2 simulator. This paper focus on the comparative performance evaluation of using GA, using PSO and over enhanced hybrid GA- PSO. Results are shown in the form of x graphs using Network Simulator This will optimize the routing algorithm to improve the QoS of the network. Keywords: - MANETs, QoS,, GA, PSO. I. INTRODUCTION MANETs stand for Mobile Ad hoc Networks. Mobile implies mobility. [16][17][19] Ad hoc is a Latin word and it means for this only. MANET is Mobile Ad Hoc Network referred to infrastructure less network of mobile devices connected by wireless links, which consist of mobile devices where exchange of messages between nodes not relies on any pre-deployed infrastructure. Mobile nodes can enter and leave network freely at any time, the nodes break constantly due to movement of the users. Each node in the network may be capable of acting as a router, and have its own routing table to every another node in the network. The degree of randomness of nodes is unpredictable and is dynamic routing protocols. Thus path routing and protocol selection are the key strategies behind the design of any wireless network. Each and every routing protocol of MANET has its own features or characteristics and performance level. So, it is very important to find out the key routing protocol to be used for selected scenario for the better performance. As the nodes are mobile, the structure of the network changes dynamically and unpredictably over time. The MANET routing protocols have a number of properties, but one of the most important of all is the possibility of QoS (Quality of Services) support. [12][14] The first goal of this paper is to briefly described QoS in the network (Section II). The section III provides overview of the routing protocol. Section IV, summaries routing protocol in brief. Section V explores the optimization techniques that are Genetic Algorithm and Particle Swarm Optimization. Section VI states the problem statement and main objectives of the research. Section VII defines the methodology and flowchart of the research work. Section VIII shows the simulation environment along with their results of the proposed work. Section IX shows the comparison in research work. Then in the section X conclusion and in XI future scope is given. II. QOS IN THE NETWORK QoS routing is a major challenge in ad hoc network. One of the main responsibilities of QoS routing is to find out the feasible path between source node and destination node, which satisfies the QoS requirements for each connection and optimizes the use of network resources in network scenario. QoS metrics could be defined in terms of performance parameters like End to End delays, packet loss, packet received, packet delivery ratio, routing overhead, throughput and many more. In MANET, QoS plays a vital role in order to improve the network. III. OVERVIEW OF ROUTING PROTOCOL Routing protocol in MANET can be classified into several ways depending upon their network scenario, communication model, routing protocol, and state information and many more, but most of these are done depending on routing strategy and network structure. Based on the Routing strategy the routing protocols can be classified into two parts: 1. Table driven and 2. Source driven or initiated (on demand) while depending on the network structure these are classified into three basic categories such as flat routing protocols, hierarchical routing protocols and geographic position assisted routing protocols. Flat routing comprises of both routing protocols based on routing strategy. These are as follow [12][17][18]: A) Reactive (On-demand) Protocols: In this protocol, routes are created only when needed. Protocols having higher latency since the routes have to be discovered when source node initiates a route request and lower overhead 326

2 since routes are maintained only on demand basis. Example: AODV, DSR, TORA, ABR. B) Proactive (Table driven) Protocols: In this protocol, when packet needed to be forwarded, route is already known. This protocol has low latency due to maintenance of routes all times and higher overhead due to frequent route updates. Example: DSDV,, DVRP, WRP. C) Hybrid Protocols: Hybrid protocols are the combination of reactive as well as proactive protocols. Example: ZRP Figure 1: Classification of Ad hoc routing protocols IV. PROTOCOL REVIEW (Optimized Link State Routing) routing protocol is a table-driven or proactive protocol based on the traditional link state algorithm. The point to point routing protocol is a non-uniform proactive protocol. It is important to understand that does not route traffic. It is not responsible for the process of routing traffic. It could be described as a route maintenance protocol and responsible for maintain the routing table used for routing packages. protocol, have four steps for find the route from source to destination: neighbour sensing, MPR selection, MPR Information Declaration, routing table calculation. protocol is the enhanced version of pure link state routing protocol that chooses the optimal path during a flooding process for route setup and route maintenance. Under the routing protocol strategy, nodes in the network exchange periodical topology information with each other and select a set of neighbouring nodes called Multipoint Relays (MPRS) to retransmit their packets. uses MPR technique as an optimization for control traffic flooding and minimizes the size of control messages and the number of rebroadcast node during a route update. All the nodes are informed about the subset of all the available links and the link between MPR and MPR selectors. In, link state information is generated by nodes which are chosen by MPR s. [11][12] uses three kinds of the control messages: Hello, Topology control (TC) and Multiple Interface Declaration (MID). These are as under: A. Hello: Hello messages are transmitted to all neighbours. These messages are used for neighbour sensing and MPR calculation. B. TC: Topology Control messages are the link state signalling done by and is optimized using MPRs. C. MID: Multiple Interface Declaration messages are transmitted by nodes running on more than one interface and list all IP addresses used by node. Hello messages are used to find out the information about the link status and its neighbours. With the use of hello message, MPR selector set is constructed or made, which describes which neighbours has chosen this host to act as MPR and from this information, the host can calculate its own set of the MPRs. [16] The hello messages are sent only one hop away but the TC messages are broadcasted throughout the whole network, and TC messages are used for broadcasting information about its own advertised neighbour which includes at least the MPR selector list. Then the TC messages are broadcasted periodically and one and only the MPR hosts can forward the TC message. MID messages are transmitted by nodes running on more than one interface. These messages list all the IP addresses used by a node. V. OPTIMIZATION TECHNIQUES Optimization is to improve the Quality of Service of the network scenario and to minimize the number of control messages in the network. Optimization means to find out the optimal route in terms of performance metrics. The basic principle behind optimizing MANET route is to control the flows in the network such that the flows must be better than previous. Here we are exploring two optimization techniques in detail, GA (Genetic Algorithm) and PSO (Particle Swarm Optimization), which are as follow. A. Genetic Algorithm: Genetic Algorithm (GA) [15] is a search and optimization technique which works on the principle Select the best and Discard the rest. Genetic Algorithm is an iterative process that maintains a population of solutions that are candidate solutions to specific domain. At the beginning in case of MANET there are number of nodes. Some of the nodes are at better position and can be considered as best nodes to reach the destination. We start with the population of randomly generated solution and determine how fit it is. If solution is good, we stop, if not we optimize for a better. Now, as resources are limited, every node has to compete to another for resources. Ultimately only the strongest or fittest node survives and rest are discarded. Then this best node will be selected. GA has following parameters: Selection, Encoding, Recombination, Crossover and Mutation. In MANET, GA has been involved in solving route problems by selecting 327

3 the shortest path and developing optimized routing protocols. B. Particle Swarm Optimization: Particle Swarm Optimization (PSO) optimizes a problem by iteratively trying to improve a candidate solution with regard to a given values of quality. In this algorithm group of particles are moving in search space to find path and their tendency is to remain in centre in the group of particles and tries to attain secured and efficient position in the search space. So they try to move towards the particle which is close to the solution and after some time all particles stop to particular solution which is the optimal solution.[13][15] PSO is a global optimization technique that combines self-experience of particle with social experience of particle to find the best solution for the problem, presented as a point and a velocity. Based on certain metrics, each particle assigns a value to the position it has and also remembers the best position it has seen during search. The particle then communicates the best position to other particles in search space. Therefore the particles will adjust their own positions as well as velocity based on the information. VII. RESEARCH METHODOLOGY AND FLOWCHART The research methodology is divided into following steps: Step 1: In the first step, we need to determine research purposes, configurations and assumptions the performance parameters and the type of expected results. Step 2: This step will include implementation of design. We will create and configure the network with routing protocol using Genetic algorithm and starts the simulation process. Research Plan Review of Literature Generate MANET Scenario using NS-2 VI. PROBLEM STATEMENT AND OBJECTIVES This research is focused on Quality of Service (QoS) challenge in the Mobile Ad hoc Network. In our research, we will work on MANET routing protocol and design a network with an optimized routes. In the proposed work, we are also improving the QoS by modifying the optimization algorithms. Here challenge is to design a network with maximum throughput, reduced end to end delays and packet delivery ratio should increase with time. We are proposing enhanced hybrid optimization algorithms by combining strengths of GA and PSO to enhance routing protocol. Performance comparison of existing and enhanced hybrid algorithm over routing protocol will be computed. The main objectives of this research are: To implement using GA algorithm. To implement using PSO algorithm. To Design an enhanced hybrid Algorithm from existing GA and PSO Algorithm. To compare the performance of existing and an enhanced hybrid algorithm over routing protocol. Evaluation of the results based on various parameters. Analyze the result using Network Simulator-2 (NS-2) under different network parameters. Implementation of using GA Implementation of using PSO Design an enhanced Hybrid algorithm using GA and PSO Comparison of existing and enhanced hybrid algorithm over routing protocol Evaluate results based performance parameters Analysis of Results Research Output Figure 2: Flowchart of Research 328

4 Step 3: In this step, we are going to implement using PSO and starts simulation for optimized routing. Step 4: Genetic algorithm and PSO algorithm will be combined to design an enhanced hybrid algorithm and that will be applied over. Step 5: In this step we verify and evaluate the performance of all the simulated networks over the considered performance parameters. Step 6: Final results will be analyzed in the form of x- graphs and NAM file that will show the performance comparison between existing and an enhanced hybrid algorithm. This is how the research work will be carried out; the information gained at each step will play vital inputs for the research and further is the flowchart of the work that will be undergone to find out the objectives of the research proposed. The above figure represents the flowchart of this research work. VIII. SIMULATION ENVIRONMENT AND RESULTS NS-2 network simulator used to simulate behavior of network on a simulator. Protocol is implemented over NS2 Simulator. NS2 provides a way to design and evaluate new network protocols, comparing different protocols and simulating their corresponding behaviours. using existing optimization techniques will be compared with using enhanced hybrid technique. To analyze the effective performance as well as results of the protocol for Mobile Ad hoc Network, it is significant to evaluate it under practical conditions using simulator. We are going to evaluate, a proactive protocol in terms of different parameters that are Throughput, Packet Delivery Ratio and End to End Delay. Table 1: Simulation Parameters Parameter NS-2 Version 2.33 OS Ubuntu Simulation Area No. of Nodes 50 Channel Routing Protocol Simulation time Traffic type Packet Size Optimization Techniques Mobility Model Output File Value 1000m X 1000m Wireless Channel 200s CBR, TCP 512 bytes Genetic Algorithm, Particle Swarm Optimization Random Way Mobility X graphs (.xg) C) Network Animator (NAM): NAM stands for Network Animator. NAM trace records simulation detail in a text file, and uses the text file as a play back the simulation using animation. It has data for network topology and shows graphical representation. NAM trace is activated by the command in tcl file. A) Simulation Environment: The Simulation Parameters are presented below in table 1: Performance is evaluated based on the basis of four performance metrics namely: Throughput, Packet Delivery Ratio and End-to-End Delay. B) Environmental Process: The simulation scenario and parameters used for performing the detailed analysis and study of optimization techniques on protocol is described below. This fact represents that how the effective performance parameters have been analyzed to simulate the protocols. Following steps have been used for simulation. 1. Inputs to Simulator: Simulation TCL file. 2. Outputs File from Simulator: Trace file and Network Animator (NAM) file. 3. Output from trace file: X graph file. Figure 3: Network Animator After performing simulation as per network scenario, trace files are produced. Trace file records the detail of packets 329

5 passing through network nodes and queues. Trace file have following information: Time Source Node/ Destination Node Packet Name and packet size. Type identifier Flags flow ID Source address/ Destination address Sequence number Packet unique ID ii) Packet delivery ratio: It is the ratio of data packets delivered to the destination to those generated by means of the sources. It is calculated by dividing the number of packet received by destination through the number packet originated from source. D) Analysis done using Trace Analyzer: Now we are having trace file in hand that would not be useful unless meaningful analysis is performed on data. So files trace analyser is used to evaluate and extracts a subset of interest and further analysis it to trace outputs from simulation. When files are evaluated using this trace analyzer then an output.x gr file is produced which results in the form of graphs i.e. x graphs E) Simulation Results: Using the above mentioned parameters and performance metrics we compared the performance protocol against existing and enhanced hybrid optimization techniques and we came across the following results i) Throughput (bits/s): Throughput is the measure of the number of packets successfully transmitted to the destination per unit time. Figure 5: Packet Delivery Ratio Analysis The Packet Delivery Ratio (PDR) of with enhanced hybrid Optimization algorithm is higher as compare to Genetic Algorithm, Particle Swarm Optimization and simple routing protocols. iii) End-To-End Delay: Average End to End Delay signifies the average time taken by packets to reach from one end to other. Figure 4: Throughput Analysis The throughput of with enhanced hybrid Optimization algorithm is higher as compare to Genetic Algorithm, Particle Swarm Optimization and simple routing protocols. Figure 6: End to End Delay Analysis 330

6 The End to End Delay of with enhanced hybrid Optimization algorithm is lower as compare to Genetic Algorithm, Particle Swarm Optimization and simple routing protocols. IX. COMPARISON The table hence compares the performance of existing and enhanced optimization techniques over routing protocol. Table2: Comparison Throughput (kbps) PDR (%) End to End Delay (ms) GA END TO END DELAY PSO GAPSO Figure 9: End to End Comparison END TO END DELAY GA PSO GAPSO The following graphs show the comparison in performance parameters: X. CONCLUSION In this paper, we have evaluated the performance of proactive routing protocol using existing and enhanced hybrid optimization technique in NS2.33 Network Simulator. After the analysis of our simulation results, we conclude the performance of enhanced hybrid optimization technique over MANET is better than existing optimization techniques in terms of QoS parameters: Throughput, End to End Delay and Packet Delivery Ratio metrics. XI. FUTURE WORK Figure 7: Throughput Comparison Our implementation of enhanced protocol can be further extended under different QoS parameters in order to improve further using different network scenario in different tools like Opnet, Omnet, Matlab tools. In future, we intended to study the different routing protocol using more optimization techniques to determine the optimum protocol selection to meet the specific network scenario. XII. ACKNOWLEDGEMENT I express my sincere gratitude to my guide Mrs Rupinder Kaur Gurm, for her valuable guidance and advice. Also I would like to thanks Head of Department, all the faculty members, colleagues and my parents for their continuous support and encouragement in order to complete my research work. Figure 8: Packet Delivery Ratio Comparison 331

7 REFERENCES [1] Al-Maashri, A. and Ould-Khaoua, M. (2006), Performance analysis of MANET routing protocols in the presence of self-similar traffic, Proceedings of the 31st IEEE Conference on Local Computer Networks, 2006, November 2006, pages pp , Tampa, Florida, USA. [2] V. Deepalakshmi, T. Muthuramalingam and Ms. R.Vijayalakshmi, Current Survey of Routing Protocols in MANETS, International Journal of Engineering Research & Technology (IJERT), ISSN: , Vol. 3 Issue 1, January [3] Ipsita Panda, A Survey on Routing Protocols of MANETs by Using QoS Metrics, IJARCSSE, ISSN: X, Volume 2, Issue 10, pp , October [4] Rupinder Kaur, Performance evaluation of QoS routing for Mobile Ad Hoc Network using NS-2 International Journal of Research in Engineering and Technology., vol.-1, Issue 2, July 2013, , impact journals. [5] Sukhwinder Singh, Renu Dhir, Performance Evaluation and Analysis of QoS in MANET routing protocols with Mobility and Node Density issue for HTTP and FTP Traffics, ISSN: , Vol. 2 Issue 2, pp 92-99, IJEA, 2013 [6] Anuj K. Gupta, Harsh Sadawarti, and Anil K. Verma, Review of Various Routing Protocols for MANETs, International Journal of Information and Electronics Engineering, Vol. 1, No. 3, pp November [7] Jiwa Abdullah, Performance of QOSRGA Routing Protocol for MANET with Random Waypoint Mobility Model, International Journal of Advanced Science and Technology Vol. 40, pp March, [8] Abhishek Toofani, Solving Routing problem using PSO International Journal of Computer applications ( ), Volume 52-No. 18, August [9] Vishal Gupta, Route Optimization in MANET using FIGA, International Journal of Electrical and Electronics Research (IJEER) Vol. 1, Issue 1, pp: (38-45), Month: October-December [10] E. Baburaj, and V. Vasudevan, An Intelligent Mesh Based Multicast Routing Algorithm for MANETs using Particle Swarm Optimization, IJCSNS International Journal of Computer 214 Science and Network Security, VOL.8 No.5, pp May [11] Ashu Tyagi, Pankaj Sharma, "Implementation of Ant Colony Optimization with in Mobile Ad hoc Network, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 4 (6), 2013, [12] Pragya Gupta and Sudha Gupta, Performance Evaluation of MANET Routing Protocols in Random Waypoint Mobility Model, International Journal of Computer Applications ( ), International Conference and Workshop on Emerging Trends in Technology [13] H.Babaee and A.Khosravi, An Improve PSO Based Hybrid Algorithms, IEEE [14] S.K.Nivetha, R.Asokan and N.Senthilkumaran, A Swarm-based Hybrid Routing Protocol to Support Multiple Quality of Service (QoS) Metrics in Mobile Ad Hoc Networks, 4th ICCCNT 2013, July 4-6, IEEE 2013, Tiruchengode, India. [15] Preeti.c.sambrani1 & dr. Arun biradar, Efficient Routing in Wireless Ad Hoc Networks using Bio Inspired Algorithms, International Conference on Computer Science and Engineering (CSE) 7th April 2013, Bangalore, ISBN: [16] Latif Ullah Khan FaheemKhan NaeemKhan and M Naeem Khan Bilal Pirzada, Effect of Network Density on the Performance of MANET Routing Protocols, International Conference on Circuits, Power and Computing Technologies [ICCPCT], IEEE [17] Jiri Hosek, Dominik Kovac, Pavel Vajsar and Attila Potfay, QoS Support in Routing Protocols for MANET, IEEE [18] Jatinder Pal Singh1 and Anuj Kr. Gupta, A Review on Dynamic MANET On Demand Routing Protocol in MANETs, International Journal of Advanced Trends in Computer Science and Engineering, Volume 2, No. 2, March April 2013, ISSN No , pp [19] Jatinder Pal Singh1, Anuj Kr. Gupta, Protocol Stack based Security Vulnerabilities in MANETs, International Journal of Computer Applications ( ) Volume 69 No.21, May [20] NS-2. ns manual (formally known as NS Documentation), Available at [21] Jatinder Pal Singh1, Anuj Kr. Gupta, Performance Analysis of AODVv2 Protocol vs. AODV Protocol in MANET, International Journal of Emerging Technologies in Computational and Applied Sciences, vol 4, issue 6), pp , March-May [22] Fall K, Varadhan K (Eds). NS Manual., documentation.html, [23] Ashima Mehta, Prof. Anuj Gupta, Retrospection and comparison of DSDV and AOMDV routing protocol in Manet using NS2, International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), IEEE [24] Narinder Pal, Renu Dhir, Performance comparison of Routing Protocols of MANET using vector and random way point mobility model, IJAIR, Vol.2 Issue 6, ISSN: ,

8 [25] Suman Banik, Bibhash Roy and Biswajit Saha Nabendu Chaki, Design of QoS Routing Framework based on Protocol, International Conference on Advances in Recent Technologies in Communication and Computing, IEEE Computer Society, [26] [27] E.Baburaj and J. Arul Valan, Impact of HGAPSO in Optimizing Tree based Multicast Routing Protocol for MANETs, International Conference on Computational Intelligence and Communication Networks, IEEE Computer Society, 2010 [28] R. Mohan, Merin Francis and Shri Alarmelu, An Analysis of Stable Mobility Model for TORA & Routing Protocols in MANET International Journal of Advanced Research in Computer Science, Vol. 4, No. 2, Jan-Feb [29] Anuj K. Gupta, Harsh Sadawarti, Anil K. Verma, A Review of Routing Protocols for Mobile Ad Hoc Networks, WSEAS TRANSACTIONS on COMMUNICATIONS, ISSN: , Issue 11, Volume 10, November AUTHORS Ramandeep Kaur Gill is a Research Scholar of department of Computer Science and Engineering at RIMT-IET, Mandi Gobindgarh, Punjab. She is currently pursuing her M.Tech in Computer Science and Engineering from RIMT-IET, Mandi Gobindgarh, Punjab and has done her B.Tech in Computer Science and Engineering in 2012 from GNDEC, Ludhiana, Punjab. She has 01 international journal publication. She has published and presented 01 research paper in national conference and selected as best paper of the session. Her main research interests are in Mobile Ad hoc Networks. Rupinder Kaur Gurm, a highly qualified assistant professor with 8 years teaching experience, holds a master's degree in computer science and pursuing Ph.D. in computer science. She is currently serving as an Assistant Professor in computer science department of RIMT-IET, Mandi Gobindgarh, Punjab. She has written many technical papers and is a regular contributor to industry periodicals, and author of ten books. She is a member of the ISTE (Indian Society for Technical Education), IAENG (International Association of Engineers) and IEEE (Institute of Electrical and Electronics Engineers). She is Associate editor in international journal of engineering and technology. Her current research focuses primarily on the application of mobile computing and wireless networks. 333