Improved AODV protocol for path establishment using nature inspired techniques in manets

摘要

The mobile adhoc networks is the decentralized type of network which has routing, security and quality of service as the major issues. This research work is based on the path establishment from source to destination. The most popular routing protocols like AODV, DSR and DSDV are compared in terms of certain parameters. The best performing AODV routing protocol is improved for path establishment. The hybrid protocol is derived using bee colony and ant colony algorithms. The proposed protocol is implemented in NS2 and simulation results shows improved in the results. [1] Saiful Azadm, Arafatur Rahman and Farhat Anwar, “A Performance comparison of Proactive and Reactive Routing protocols of Mobile Ad hoc Networks(MANET))”, JOURNAL OF ENGINEERING AND APPLIED SCIENCES , 2007. [2] Nadia Qasim, Fatin Said and Hamid Aghvami, “Mobile Ad hoc Networks simulations using Routing protocols for Performance comparisons”, Proceedings of the world congress on Engineering, WCE, VOL I, 2008 [3] Wang Lin-zhu, FANG Ya-qin and SHAN Min, “Performance comparison of Two Routing Protocols for Ad Hoc Networks”, WASE International conference on Information Engineering, 2009 [4] C.M barushimana and A.Shahrabi, “Comparative study of Reactive and Proactive Routing protocols performance in Mobile Ad Hoc Networks”, AINAW-IEEE, 2007. [5] Kun-Ming Yu, Chang-Wu Yu and Shi-Feng Yan, “An ad-hoc routing protocol with multiple backup routes”, Journal Wireless Personal Communications, Vol. 57, Issue. 4, pp. 533-551, April 2011. https://doi.org/10.1007/s11277-009-9860-7 . [6] S.Karunakaran and P.Thangaraj , “A cluster based congestion control protocol for mobile ad-hoc networks” , International Journal of Information Technology and Knowledge Management , Vol. 2, No. 2, pp. 471-474, July-December 2010. [7] D.H. Nguyen, J. C. Juang, "A refined ant colony algorithm for optimal path planning", en international Conference on System Science and Engineering, Macau, 2011, pp 125-130. [8] S.H. Chia, K.L. Su, Jr.H. Guo y C.Y. Chung, "Ant Colony System Based Mobile Robot Path Planning", en Fourth international Conference on Genetic. [9] X. Shi, Y. Li, H. Li, R. Guan, L. Wang, and Y. Liang, “An Integrated Algorithm Based on Artificial Bee Colony And Particle Swarm Optimization,” In Proceedings of the 6th International Conference on Natural Computation (ICNC’10) , pp.2586–2590, August 2010. [10] J.Luo, Q.Wang, and X.Xiao, “A Modified Artificial Bee Colony Algorithm Based on Converge-On Lookers Approach for Global Optimization,” Applied Mathematics and Computation, vol. 219, no.20, pp.10253–10262,2013. https://doi.org/10.1016/j.amc.2013.04.001 . [11] Manuela Graf, Marc Poy, Simon Bischof, Rolf Dornberger, and Thomas Hanne, “Rescue Path Optimization Using Ant Colony Systems”, IEEE, 2017. [12] Ronald Uriol, Antonio Moran, “Mobile Robot Path Planning in Complex Environments Using Ant Colony Optimization Algorithm”, 2017 third International Conference on Control, Automation and Robotics. [13] Deepshikha Sethi, Abhishek Singhal, “Comparative Analysis of A Recommender System Based on Ant Colony Optimization and Artificial Bee Colony Optimization Algorithms ”, EIGHTH ICCCNT 2017. [14] Mandeep Kaur Bedi, Sheena Singh, “Comparative Study of Two Natural Phenomena Based Optimization Techniques”, International Journal of Scientific & Engineering Research Volume 4, Issue3, March 2013. [15] Razif Rashid1, N. Perumal, I. Elamvazuthi, Momen Kamal Tageldeen, “Mobile Robot Path Planning Using Ant Colony Optimization”, IEEE, 2016. [16] Jerry Kponyo Yujun Kuang Enzhan Zhang, Jerry Kponyo, “Dynamic Travel Path Optimization System Using Ant Colony Optimization”, 2014 UK Sim-AMSS 16th International Conference on Computer Modelling and Simulation. [17] Brendan Englot and Franz Hover, “Multi-Goal Feasible Path Planning Using Ant Colony Optimization”, IEEE, 2011 .