Abstract

The aim of this research is to evaluate the performance of OLSR using swarm intelligence and HPSO with Gravitational search algorithm to lower the jitter time, data drop and end to end delay and improve the network throughput. Simulation was carried out for multimedia traffic and video streamed network traffic using OPNET Simulator. Routing is exchanging of information from one host to another in a network. Routing forwards packets to destination using an efficient path. Path efficiency is measured through metrics like hop number, traffic and security. Each host node acts as a specialized router in Ad-hoc networks. A table driven proactive routing protocol Optimized Link State Protocol (OLSR) has available topology information and routes. OLSR’s efficiency depends on Multipoint relay selection. Various studies were conducted to decrease control traffic overheads through modification of existing OLSR routing protocol and traffic shaping based on packet priority. This study proposes a modification of OLSR using swarm intelligence, Hybrid Particle Swarm Optimization (HPSO) using Gravitational Search Algorithm (GSA) and evaluation of performance of jitter, end to end delay, data drop and throughput. Simulation was carried out to investigate the proposed method for the network’s multimedia traffic.

Keywords:

Ad hoc network, gravitational search algorithm, Hybrid Particle Swarm Optimization (HPSO), multimedia traffic, Optimized Link State Routing (OLSR),

References

1. Agarwal, S., J. Padhye, V. Padmanabhan, L. Qiu, A. Rao and B. Zill, 2005. Estimation of link interference in static multihop wireless networks. Proceeding of ACM Internet Measurement Conference (IMC), pp: 28-28.
   PMCid:PMC1215514    
2. Aleksandr, H., 2004. Comparing AODV and OLSR routing protocols. Proceeding of Seminar on Internetworking, Telecommunication Software and Multimedia Laboratory, Helsinki University of Technology, Sjokulla, pp: 1-9.
   
3. Broch, J., D.A. Maltz, D.B. Johnson, Y.C. Hu and J. Jetcheva, 1998. A performance comparison of multi-hop wireless ad hoc network routing protocols. Proceedings of the 4th Annual ACM/IEEE International Conference on Mobile Computing and Networking, pp: 85-97.
   CrossRef    
4. Edward, G., C. Yeison and E. Paola, 2011. Hybrid approaches in network optical routing with QoS based on genetic algorithms and particle swarm optimization. Int. J. Network Secur., 2(4).
   
5. Gimmler, J., T. St�tzle and T.E. Exner, 2006. Hybrid particle swarm optimization: An examination of the influence of iterative improvement algorithms on its behaviour. Proceeding of the 5th International Conference on Ant Colony Optimization and Swarm Intelligence (ANTS'06), pp: 436-443.
   CrossRef    
6. Guo, Z., S. Malakooti, S. Sheikh, C. Al-Najjar and B. Malakooti, 2011. Multi-objective OLSR for proactive routing in MANET with delay, energy and link lifetime predictions. Appl. Math. Modell., 35(3): 1413-1426.
   CrossRef    
7. Karaboga, D. and B. Akay, 2009. A survey: Algorithms simulating bee swarm intelligence. Artif. Intell. Rev., 31(1-4): 61-85.
   CrossRef    
8. Kumar, R., 2003. A protocol with transcoding to support QoS over internet for multimedia traffic. Proceeding of International Conference on Multimedia and Expo (ICME '03), pp: 1-465.
   CrossRef    
9. Kumar, A. and R. Singh, 2011. Mobile ad hoc networks routing optimization techniques using swarm intelligence. Int. J. Res. IT Manage., 1(4).
   
10. Liang, Y. and G. Pond, 2011. Using linear optimization and swarm intelligent heuristic to find a route for MANET. Proceeding of 7th International Conference Natural Computation (ICNC), 4: 2368-2372.
    CrossRef    
11. Mueller, S., R.P. Tsang and D. Ghosal, 2004. Multipath Routing in Mobile Ad Hoc Networks: Issues and Challenges. In: Calzarossa, M.C. and E. Gelenbe (Eds.), MASCOTS 2003. LNCS 2965, Springer, Berlin, Heidelberg, pp: 209-234.
    CrossRef    
12. Priyadharshini, C. and K.T. Rubini, 2012. Integration of route lifetime prediction algorithm and particle swarm optimization algorithm for selecting reliable route in MANET. Proceeding of International Conference on Computing, Communication and Applications (ICCCA), pp: 1-6.
    CrossRef    
13. Rashedi, E., H. Nezamabadi-Pour and S. Saryazdi, 2009. GSA: A gravitational search algorithm. Inform. Sci., 179(13): 2232-2248.
    CrossRef    
14. Saleem, M., G.A. Di Caro and M. Farooq, 2011. Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions. Inform. Sci., 181(20): 4597-4624.
    CrossRef    
15. Shakkeera, 2010. Optimal path selection technique for flooding in link state routing protocol using forwarding mechanisms in MANET. Proceeding of International Conference on Communication and Computational Intelligence (INCOCCI), pp: 318-323.
    
16. Shi, Y. and R.C. Eberhart, 1998. Parameter selection in particle swarm optimization. Lect. Notes Comput. Sci., 1447: 591-600.
    CrossRef    PMid:9721156    
17. Shirkande, S.D. and R.A. Vatti, 2013. ACO based routing algorithms for ad-hoc network (WSN, MANETs): A Survey. Proceeding of International Conference on Communication Systems and Network Technologies (CSNT), pp: 230-235.
    CrossRef    
18. Shrestha, A. and F. Tekiner, 2009. On MANET routing protocols for mobility and scalability in parallel and distributed computing. Proceeding of IEEE International Conference on Applications and Technologies, pp: 451-456.
    
19. Shrivastava, A., A.R. Shanmogavel, A. Mistry, N. Chander, P. Patlolla and V. Yadlapalli, 2005. Seminar, Overview of Routing Protocols in MANET's and Enhancements in Reactive Protocols.
    Direct Link
20. Wu, X., G. Ding and W. Zhu, 2009. Load-based route discovery through searching range adaptation for MANET throughput improvement. IEEE T. Veh. Technol., 58(4): 2055-2066.
    CrossRef    
21. Zhang, Z., 2011. A novel secure routing protocol for MANETs, mobile ad-hoc networks: Protocol design. University of Southern Queensland, Australia, pp: 455-466.
    

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