IJRIT International Journal of Research in Information Technology, Volume 2, Issue 6, June 2014, Pg: 512-519

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

Route Optimization To Make The Ad-Hoc Network Efficient Using Pso Algorithm Aashi Jain1, Prashant Dixit2 1

2

M.tech Student, Computer Science and Engineering , Manav Rachna International University Faridabad, Haryana, India [email protected]

Assistant Professor, Computer Science and Engineering, Manav Rachna International University Faridabd, Haryana, India [email protected]

ABSTRACT An Ad hoc network is a assortment of wireless mobile hosts forming a temporary network without the abet of any centralized administration or standard bear services. MANET can be defined using rickety network infrastructure, self-organizing network topology and autonomous node mobility. This becomes accessible due to their routing techniques; in other terms, routing is a spine for MANET. However, due to network consignment routing performance of MANET is sullied, thus some optimization on network routing stratagem is required. In this paper, we pioneer a new technique by using the perception of Particle Swarm Optimization (PSO) Algorithm to compose competent routing decision in network. In an ad-hoc network, the routing algorithms are commonly defined relevant to the distance study over the network. To classify the path selection algorithm over the network and other to optimize it using PSO as some network crash or the awful node occur. The parameters measured in this effort are distance, energy, Load over the network. If there is no collapse or foe node in the network same parameters will be worn to take the routing decisions. But as the impostor or the block node is recognized, the PSO will be used to spot the right path.

Keywords: Routing Protocol, Manet , Ad-hoc network, PSO, Optimization. I. INTRODUCTION Mobile ad-hoc networks (MANET’S) are decentralized, self-organizing networks competent of forming a network without relying on any preset infrastructure, these networks tolerate impulsive structure and bend of mobile networks . Defining on ad hoc network as an independent system of mobile hosts associated by wireless links. A routing algorithm should endeavor to locate a shortest path for transmission packet.ad-hoc network features are mobility and litheness, peer-to-peer multi-hop networks. Routing in mobile ad-hoc networks depends on many factors for topology; locate optimal path assortment of routers.ad hoc network uses two different types of protocols. They protocols are Proactive and Reactive protocols. Proactive protocol in a MANET should trail of routes in all nodes destination. This protocol maintains routing table information and also called table-driven protocol. (Example of vital proactive protocols for destination sequenced distance vector (DSDV), wireless routing protocol (WRP). In reactive protocols a node major to route detection method and also called on demand distance vector routing. The protocols are Aashi Jain,IJRIT

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 6, June 2014, Pg: 512-519

two types of mechanisms of “route discovery” and “route maintenance”. (Example of reactive protocol for Dynamic Source Routing Protocol (DSR) and Ad-hoc On-Demand Distance Vector (AODV) [1].Shortest path trouble in MANETS asks for the computation of path from source to destination node that minimizes the sum of total cost related with the path. Several traditional algorithms like Bellman ford Algorithm, Dijkstra‟s Algorithm are developed to discover the shortest path. These algorithms are not suitable for MANETS, but are suitable only for wired networks. For wireless networks, physically enthused algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Evolution Algorithm (EA) can be implemented competently

II. RELATED WORK Mobile Ad hoc networks (MANETS) consists of mobile platforms, which are open to move randomly. These are self-organizing and adaptive networks. These networks permit impulsive configuration and deformation of mobile networks. A MANET is an independent anthology of mobile users that communicate over moderately bandwidth inhibited wireless links. Since the nodes are mobile, the network topology may alter swiftly and impulsively over time. The network is decentralized, where all network action counting discovering the topology and delivering messages must be executed by the nodes themselves i.e., routing functionality will be integrated into mobile nodes. In year 2008, Alfredo Garcia performed a work," Rational Swarm Routing Protocol for Mobile Ad-hoc Wireless Networks". WirelessMobile Ad-hoc networks (MANET) require dynamic routing schemes for adequate performance. In this paper, Author introduce a new dynamic routing scheme based upon stigmergy. In a similar manner to ant-colony based dynamic routing protocols, Presented scheme is able to respond to link quality changes after a path is established[5]. In [3]. C.Mala, et al anticipated a PSO Based Multicast Routing Algorithm with Bandwidth and Delay as the Quality of Service Constraints for optimization. In [3] Liu Jing et al have addressed the Quality of Service Multicast routing predicament using Particle Swarm Optimization with the Quality of Service Parameters under contemplation were Cost ,Delay ,Delay Jitter and Packet Loss. In [3].C.Mala, et al converse solving the Multicast Routing crisis with Quality of Service parameters Buffer Space and Queuing Delay in addition to the other vital Quality of Service Parameters using Particle Swarm Optimization. The next section discusses the projected work and the algorithm for solving the problem under study. The simulation and results are discussed in the Simulation and performance study section, followed by conclusion. Each node selectively adds only the paramount (most fit) nodes in its neighborhood to its proactive area. In [4] Jihar Doshi et al, the correction of the zone is based on an approximation cost model. [4] Adjusts the proactive area in order to create a node more reachable. We modify the Proactive area in order to cut route acquisition latencies. Unlike [4], we use the idea of FITNESS (a Genetic Algorithm-based technique) to resolve the node’s participation in proactive routing. This yields a more sensible proactive region as it takes into account the changing atmosphere of a node. III. ROUTING PROTOCOLS FOR MOBILE AD-HOC NETWORK (MANET) MANET routing protocols are classically subdivided into two main categories: proactive routing protocols and reactive on-demand routing protocols [5]. Proactive routing protocols are derivative from legacy Internet distance-vector and link-state protocols. They shot to sustain consistent and updated routing information for every pair of network nodes by propagating, proactively, route updates at preset time intervals. As the routing information is generally maintained in tables, these protocols are sometimes referred to as Table-Driven protocols. Reactive on demand routing protocols, on the other hand, set up the route to a destination only when there is a stipulate for it. The source node through the route discovery procedure usually initiates the route requested. Once a route has been recognized, it is maintained until either the destination becomes unreachable (along every path from the source), or until the route is no longer used, or expired [5,6 ]. REACTIVE ROUTING PROTOCOLS:- These protocols leave from the legacy Internet approach. To lessen the overhead, the route between two nodes is exposed only when it is needed. Representative reactive routing protocols comprise ABR protocol is also a loop free protocol, but it uses a new routing metric termed degree of involvement stability in selecting routes, so that route exposed can be longer-lived route, thus more steady and requiring less updates consequently. The restriction of ABR comes generally from a periodic beaconing used to Aashi Jain,IJRIT

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 6, June 2014, Pg: 512-519

create the association stability metrics, which may consequence in supplementary energy consumption. Signal Stability Algorithm (SSA) [9] is mainly an ABR protocol with the supplementary property of routes_ selection by means of the signal strength of the link. In general, on-demand reactive protocols are more competent than proactive ones. On-demand protocols lessen control overhead and power consumption since routes are only recognized when essential. By contrast, proactive protocols need periodic route updates to keep information current and reliable; in addition, preserve multiple routes that might never be desired, adding superfluous routing overheads. Proactive routing protocols offer improved quality of service than on-demand protocols. As routing information is continuously updated in the proactive protocols, routes to every destination are always accessible and up-to-date, and hence end to-end delay can be minimized. For on-demand protocols, the source node has to stay for the route to be exposed before communication can occur. This latency in route detection might be unbearable for real-time communications.

IV. PARTICLE SWARM OPTIMIZATION Particle swarm optimization (PSO) is a computational technique that optimizes a hitch by iteratively demanding to improve a candidate solution with consider to a given measure of quality. PSO optimizes a difficulty by having a population of candidate solutions, here dubbed particles, and moving these particles approximately in the search-space according to effortless mathematical formulae over the particle's position and velocity. Each particle's movement is inclined by its local finest known position but, is also guided toward the best recognized positions in the search-space, which are updated as superior positions are found by other particles. This is predictable to move the swarm toward the best solutions. PSO is a metaheuristic as it builds few or no assumptions about the problem being optimized and can hunt very large spaces of candidate solutions. However, met heuristics such as PSO do not pledge an optimal solution is ever establish. Particularly, PSO does not use the gradient of the problem being optimized, which means PSO does not need that the optimization dilemma be differentiable as is mandatory by standard optimization methods such as gradient descent andquasi-newton methods. PSO can therefore also be used on optimization problems that are moderately uneven, noisy, vary over time, etc. HOW PSO WORKS :

1. 2. 3. 4.

4

5.

Define N Number of mobile Nodes in the network with specific parameters in terms of energy, transmission rate etc. Define the Source and the Destination node over the network Set CurNode as the current node Find M Neighbor Nodes of Nodes CurNode and Maintains the respective Information For (j=1 to M) { Energy(Neighbor(i)) Distance(Neighbor(i)) Load(Neighbor(i)) Throughput(Neighbor(i)) Delay(Neighbor(i)) } if Throughput(Neighbor(i))
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6. 7. 8. 9.

Set the Pheramon on Each Hop and Identify the Possible Path Implement Backward SWARM to inform Neighbour Nodes about Backup Path Trace the Pharamons and Commmunicate of New Path Perform the Normal Communication } V. SIMULATION RESULT

Simulation of the loom in finished on MATLAB. In this the various nodes n=50 are deployed as shown in figure 1. On this both Algorithm is applied and shown the comparison between them as

Figure 1 Node Deployed

In figure 1 we can see that the network is defined with 50 number of nodes. As we can see the nodes are numbered from 1 to 50. Blue nodes are showing source node and the destination node.All other nodes are the intermediate nodes. On this network we have first implemented the shortest path algorithm used by DSDV protocol. The Sender node = 1 and the destination node =30.The path obtained from the network is 1

=> 4 => 41 =>

30

Figure 2 Path with DSDV Protocol The distance covered is Aashi Jain,IJRIT

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 6, June 2014, Pg: 512-519

Parameters

Values

Distance

71.2484

Energy Consumed

1.0697e+004

Network Delay

2.6855e+005 ms

But as we know such kind of path is always the first choice of intruder. The proposed PSO Improved algorithm has defined an intruder safe compromising path that will not cover any node of shortest path and will return a safer path to the user. The results driven from the Compromizing path Algorithm gives the path 1 => 12 => 29 => 28 => 22 => 30

Figure 3 PSO Applied Path The distance coverd is Parameters

Values

Distance

50.1397

Energy Consumed

7.0162e+003

Network Delay

1.8551e+005 ms

The comparative analysis is shown in the form of graphs given as under (1) Distance Analysis

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Figure 3 : Distance Analysis (Existing Vs. Proposed) As we can see, the proposed work has reduce the total distance covered while generating the intruder safe and congestion free path over the network.

(2) Energy based Analysis

Figure 4 : Energy Analysis (Existing Vs. Proposed) As we can see, in figure 4, as the data is transferred from a congestion free path, the overall energy consumed while performing the transmission is reduced as compared to the existing approach.

(3) Network Delay Analysis

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IJRIT International Journal of Research in Information Technology, Volume 2, Issue 6, June 2014, Pg: 512-519

Figure 5: Delay Analysis (Existing Vs. Proposed)

VI. Conclusion In this thesis, we have measured the routing approaches in mobile ad hoc networks from the security and congestion standpoint. We have analyzed the coercion against ad hoc routing and offered the necessities that require to be addressed for secure routing. Existing secure routing algorithm for mobile ad hoc networks are not much safe And significance of Mobile networks cannot be shorn of as the world of computing is receiving portable and compact. Contrasting wired networks, mobile networks cause a number of challenges to security solutions due to their unpredictable topology, wireless shared medium, heterogeneous resources and stringent resource constraints etc. The Security investigate area is still open as many of the provided solutions are premeditated keeping a limited size scenario and limited kind of attacks and vulnerabilities. In this present work, we have defined an PSO superior safe routing loom to transfer data from congestion free and attack safe path. Generally, the shortest path is the most preferred area for the attackers to carry out the intrusion, but the existing approach will not cover any node that is having the higher prospect of the attack or the congestion. As the communication will be performed over a congestion free path, the energy and the delay over the network will be reduced. The presented loom is efficient in terms of energy and the time as well as provides a consistent route over the network. The obtained outcome shows that the presented approach has develop the network reliability and the energy. The proposed algorithm intends to provide security. The Secure Compromising path Algorithm provides a foundation for governing a secure communication system for mobile ad hoc networks.

VII. References [1]. E.M. Belding-Royer, C.-K. Toh, A review of current routing protocols for ad-hoc mobile wireless networks, IEEE Personal Communications Magazine (April 1999) 46–55. [2]. Anju Sharma, Madhavi Sinha, “A Differential Evaluation Algorithm For Routing Optimization In Mobile Ad Hoc Networks”, The Birla Institute Of Technology, Jaipur Mesra, Ranchi Campus Jaipur, Rajasthan, International Journal Of Computer Science And Network (IJCSN),Volume 1, Issue 4, Www.Ijcsn.Org ISSN 2277-5420,Pages 109-115, August 2012. [3]. C.Mala, A.Anurag Mahesh ,R.Aravind ,R.Rajgopal ,Narendran Rajagopalan , B.Nithya , “ Simulated Study Of Qos Multicast Routing Using Genetic Algorithm”, National Institute Of Technology,Tiruchirappalli. World Applied Programming, Vol (2), Issue (5),pages 342-348,Issn: 22222510©2011 Wap Journal. Www.Waprogramming.Com, May 2012. [4]. Jihar Doshi, Prahlad Kilambi, “Safar: An Adaptive Bandwidth-Efficient Routing Protocol For Mobile Ad Hoc Networks”, Sri Venkateswara College Of Engineering, University Of Madras, Pennalur, Sriperumbudur, [email protected], [email protected]. S. Pierre, M. Barbeau, and E. Kranakis (Eds.): Adhoc-Now 2003, Lncs 2865, Pp. 12–24, 2003._C Springer-Verlag Berlin Heidelberg 2003.

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[5]. Perkins C.E., E.M. Royer, Ad-hoc on-demand distance vector routing, in: Proceedings of 2nd IEEE Workshop on Mobile Computing Systems and Applications, February 1999. [6]. Elizabeth Belding-Royer, Routing approaches in mobile ad hoc networks, in: S. Basagni, M. Conti, S. Giordano, I. Stojmenovic (Eds.), Ad Hoc Networking, IEEE Press Wiley, New York, 2003. [7]. Y. Sankarasubramaniam, O. Akan and L. Akyildiz, \ESRT: Event-to-Sink Reliable Transport in Wireless Sensor Networks," in MobiHoc'03, Annapolis, Maryland, USA, June 2003, pp. 113-112. [8]. V.D. Park, M.S. Corson, A highly adaptive distributed routing algorithm for mobile wireless networks, in: Proceedings of INFOCOM _97, April 1997. [9] Jang, W.S., H.I. Kang, B.H. Lee, K.I. Kim, D.I. Shin and S.C. Kim, 2007. Optimized fuzzy clustering by predator prey particle swarm optimization, IEEE Congress on Evolutionary Computation(CEC), 32323238. [10] João Pedro,” Distributed Routing Path Optimization for OBS Networks based on Ant Colony Optimization”, IEEE "GLOBECOM" 2009 978-1-4244-4148-8/09©2009 [11] Janson, S. and M. Middendorf, 2004. A hierarchical particle swarm optimizer for dynamic optimization problems”, Congress on Evolutionary Computation (CEC), 3005: 513-524. [12] Karl, O.J., 2005. Comparison of Genetic Algorithm and Particle Swarm Optimization, International Conference on Computer Systems and Technologies, IIIA.1-1-IIIA.1-6. [13] Kennedy, J. and R.C. Eberhart, 1997. A discrete binary version of the particle swarm algorithm, IEEE conference on systems, man, and cyber, 5: 4104-4108. [14] Li, W., L. Yushu, Z. Xinxin and X. Yuanqing, 2006. Particle swarm optimization for fuzzy c-means clustering, IEEE World Congress on Intelligent Control and Automation (WCICA), 2: 6055-6058. [15] Lu, H. and W. Chen, 2008. Self-adaptive velocity particle swarm optimization for solving constrained optimization problems, Journal of Global Optimization, 41: 427-445. [16] Martinez, R., A. Rodriguez, O. Castillo and L.T. Aguilar, 2010b. Optimization of type-2 fuzzy logic controllers using PSO applied to linear plants, Computational Intelligence, 318: 181-193. [17] Meissner, M., M. Schmuker and G. Schneider, 2006. Optimized particle swarm optimization (OPSO) and its application to artificial neural network training, BMC Bioinform., 7: 1-11 [18] Michael Rinehart,” The Value of Side Information in Shortest Path Optimization”, IEEE TRANSACTIONS ON AUTOMATIC CONTROL 0018-9286© 2011 IEEE [19] Marcelo Portela Sousa," Ant Colony Optimization with Fuzzy Heuristic Information Designed for Cooperative Wireless Sensor Networks", MSWiM’11, October 31–November 4, 2011, Miami, Florida, USA. ACM 978-1-4503-0898-4/11/10 [20] Marina Yusoff,” A Discrete Particle Swarm Optimization with Random Selection Solution for the Shortest Path Problem”, 978-1-4244-7896-5/10@ 2012 IEEE [21] Maumita Bandyopadhyay,” Zone Based Ant Colony Routing In Mobile Ad-hoc Network”, 978-14244-5489-1/10© 2010 IEEE.

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