IJCNIS Vol. 7, No. 3, 8 Feb. 2015
Cover page and Table of Contents: PDF (size: 605KB)
Full Text (PDF, 605KB), PP.26-34
Views: 0 Downloads: 0
Ad hoc Network, Ant colony, Swarm Behavior, MTSP, ACO for MTSP, Transmission Queue Length
This paper represents The Ant Colony Optimization for MTSP and Swarm Inspired Multipath Data Transmission with Congestion Control in MANET using Total Queue Length based on the behavioral nature in the biological ants. We consider the problem of congestion control for multicast traffic in wireless networks. MANET is multi hop wireless network in which the network components such as PC, mobile phones are mobile in nature. The components can communicate with each other without going through its server. One kind of agent (salesman) is engaged in routing. One is Routing agent (salesman), who collects the information about network congestion as well as link failure and same is message agent (salesman) that uses this information to get his destination nodes. Though a number of routing protocols exists, which aim to provide effecting routing but few provide a plausible solution to overall network congestion. We attempt to explore the property of the pheromone deposition by the real ant for MTSP. The proposed algorithm using path pheromone scents constantly updates the goodness of choosing a particular path and measuring the congestion in the network using total queue length and Hop-distance.
Dhriti Sundar Maity, Subhrananda Goswami, "Multipath Data Transmission with minimization of Congestion Using Ant Colony Optimization for MTSP and Total Queue Length", International Journal of Computer Network and Information Security(IJCNIS), vol.7, no.3, pp.26-34, 2015. DOI:10.5815/ijcnis.2015.03.04
[1]Z. Chi , S. Hlaing, M. Aye, “An Ant Colony Optimization Algorithm for Solving Traveling Salesman Problem”, Yangon Khine University of Computer Studies, IACSIT Press, Singapore, vol.16 ,2011.
[2]S. Modi, J. Prithviraj. “Multiple Feasible Paths in Ant Colony Algorithm for mobile Adhoc Networks with Minimum Overhead”, Global Journals Inc. (USA) Online ISSN: 0975-4172&Print ISSN: 0975-4350, Volume 11 Issue 4 Version 1.0, March 2011.
[3]C. Lochert, B. Scheuermann, M. Mauve, “A Survey on Congestion Control for Mobile Ad-HocNetworks”,Wiley Wireless Communications and Mobile Computing 7 (5), pp. 655–676,June 2007, [http://www.interscience.wiley.com]
[4]S. Yang and J. Wu , “New Technologies of Multicasting in MANET”, Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431,2007,[Email:fsyang,jieg@cse.fau.edu(2007)].
[5]S. Goswami, C.B. Das and S. Joardar,” Comparative Performance Analysis of DSDV and AODV Routing Protocols in MANET using NS2”, CiiT International journal of Networking and communication Engineering, Vol 5, No 12 2013, pp-536-544.
[6]M. Dorigo and G. Di Corne, F. Glover, “The Ant Colony Optimization Meta- Heuristic”, McGraw-Hill, 11-32, 1999.
[7]M. Dorigo and L. M. Gambardella, “Ant colonies for the traveling salesman problem In BioSystems”, IDSIA, Corso Elvezia 36, 6900 Lugano, Switzerland, 1997.
[8]M. Dorigo and L. M. Gambardella, “Ant Colony System: A cooperative learning approach to the traveling salesman problem”. In: IEEE Transactions on Evolutionary Computation., 1997.
[9]M. Dorigo, V. Maniezzo, and A. Colorni., “The Ant System: An autocatalytic optimizing process” et al., academia.edu/760931,1991,
[10]V. Maniezzo and A. Colorni, “The Ant System applied to the quadratic assignment problem”, In: IEEE Transactions on Data and Knowledge Engineering, 11(5):769.778, 1999.
[11]S. Goswami, S. Joardar, C.B. Das, and B. Das. ” A Simulation Based Performance Comparison of AODV and DSDV Mobile Ad Hoc Networks”,’ I.J. Information Technology and Computer Science’, vol-6, Number-10, pp-11-18, 2014.
[12]A. C. SolaiJawahar, “Ant Colony Optimization for Mobile Ad-hoc Networks”, IJREAT, Volume 1, Issue 1, 2013.
[13]V. Maniezzo, L. M. Gambardella, F. de Luigi, “Ant Colony Optimization new Optimization Techniques, in Engineering Studies in Fuzziness and Soft Computing Volume 141, pp 101-121, 2004.
[14]M. Dorigo,V. Maniezzo and A. Colorni, “ Ant System: Optimization by a Colony of Cooperating Agents”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS-PART B, 1996.
[15]M. Dorigo,G. Di Caro, “.Ant Colony Optimization : A New Mata-Heuristic”, , IEEE-0-7803-5536-1999.