Cover page and Table of Contents: PDF (size: 594KB)
Full Text (PDF, 594KB), PP.19-27
Views: 0 Downloads: 0
Magnetic Optimization Algorithm, Ad-hoc on demand multipath distance vector, Vehicular Ad-hoc Networks, Optimization of Value of Parameters, Quality of service
Vehicular Ad-hoc Networks is one of the emerging research areas of Mobile ad- hoc network. One of the key problems of VANET is changing topology of vehicles which leads to frequent disconnections. Therefore, for communication among the running vehicles, routing of the message becomes a challenging problem. Although, many routing protocols have been proposed in the literatures, but the performance of these protocols, in different scenarios, depends on the value of parameters used in. The objective of our work is to find best fitness function value for Ad-hoc on demand multipath distance vector routing protocol, in real scenario map by obtaining an optimal value of parameters using Magnetic Optimization Algorithm. Therefore, in this paper, we have proposed an algorithm based on Magnetic Optimization Algorithm which finds the optimal value of parameters for Ad-hoc on demand multipath distance vector routing protocol in a given scenario. The fitness function guides Magnetic Optimization Algorithm to achieve the best fitness value. The experimental results, using the optimal value of parameters obtained by Magnetic Optimization Algorithm, show 81.41% drop in average end-to-end delay, 39.24 % drop in Normalized Routing Loads, and slight rise (0.77%) in the packet delivery ratio as compared to using default value of parameters in Ad-hoc on demand multipath distance vector routing protocol.
A K Giri, D K Lobiyal, C P Katti, "Optimization of Value of Parameters in Ad-hoc on Demand Multipath Distance Vector Routing Using Magnetic Optimization Algorithm", International Journal of Computer Network and Information Security(IJCNIS), vol.7, no.12, pp.19-27, 2015. DOI:10.5815/ijcnis.2015.12.03
J. Toutouh, J. Garcia-Nieto and E. Alba, “Intelligent OLSR Routing Protocol Optimization for VANETs,” IEEE Transaction on Vehicular Technology, Vol. 61, pp. 1884 –1894, 2012.
M. K. Marina And S. Das, “Ad-hoc on-demand multipath distance vector routing,” Wireless Communications and Mobile Computing, Vol. 6, pp. 969-988, 2006.
N. M. H. Tayarani and T. M. R. Akbarzadeh, “Magnetic Optimization Algorithms a New Synthesis,” IEEE Congress on Evolutionary Computation, pp. 2659-2664, 2008.
M. M. Ismail, M. Iqbal Zakaria and A. F. Z. Abidin, “Magnetic optimization algorithm approach for travelling salesman problem,” World Academy of Science, Engineering and Technology, Vol. 62, pp. 1393-1397, 2012.
S. McCanne and S. Floyd, “The Network Simulator Project - Ns-2,” [online]: http://www.isi.edu/nsnam/ns/
Java OpenStreetMap Editor, [online]: http://josm.openstreetmap.de, 2011.
F. K. Karnadi, Z. H. Mo and K. Lan, “Rapid Generation of Realistic Mobility Model for VANET,” IEEE WCNC, pp. 2506-2511, 2007.
E. Alba, B. Dorronsoro,, F. Luna, A. Nebro, P. Bouvry and L. Hogie, “A Cellular MOGA for Optimal Broadcasting Strategy in Metropolitan MANETs,” Computer Communications. Vol. 30, pp. 685 – 697, 2007.
B. Dorronsoro, G. Danoy, P. Bouvry and E. Alba, “Evaluation of different optimization techniques in the design of ad hoc injection networks,” Workshop on Optimization Issues in Grid and Parallel Computing Environments, pp. 290–296, 2008.
Garca-Nieto, J. Toutouh and E. Alba, “Automatic Parameter Tunning with Metaheuristics of the AODV Routing Protocol for Vehicular Ad-hoc Networks,” EvoApplications, part II. LNCS 6025, pp. 21-30, 2010.
H. Cheng and S. Yang, “Genetic algorithms with immigrant schemes for dynamic multicast problems in mobile ad hoc networks,” Eng. Appl. Artif. Intell, Vol. 23, pp. 806–819, 2010.
H. Shokrani and S. Jabbehdari, “A novel ant-based QoS routing for mobile ad hoc networks,” ICUFN’09, Proceedings of the first inter- national conference on Ubiquitous and future network,. Piscataway, NJ, USA, IEEE Press, pp. 79-82, 2009.
J. Toutouh, Garca-Nieto and E. Alba, “Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics,” Engineering Applications of Artificial Intelligence, Vol. 23, 5, pp. 795–805, 2010.
C. E. Perkins, E. M. Belding-Royer and S. Das, “Ad hoc on Demand Distance Vector (AODV) Routing,” IETF RFC 3561, 2003.
A. Nasipuri, R. Castaneda and S. R. Das, “Performance of multipath routing for on demand protocols in mobile ad hoc networks,” ACM/Kluwer Mobile Networks and Application (MONET), Vol. 6, pp. 339-349, 2001.
V. Naumov and T. R. Gross, “An evaluation of inter-vehicle ad-hoc networks based on realistic vehicular traces,” Proceedings of the 7thl ACM MobiHoc, ACM, pp. 108-119, 2006.
K. Han and J. Kim, “Quantum-inspired evolutionary algorithm for a class of combinatorial optimization,” IEEE Trans. on Evolutionary Computation, pp. 580-593, 2002.
D. Krajzewicz, M. Bonert and P. Wagner, “The open source traffic simulation package SUMO,” RoboCup, Bremen, Germany, pp. 1-10, 2006.
OpenStreetMap contributors and Copyright, [online]: www.openstreetmap.org/copyright