Grey Wolf Optimization based Clustered On – Demand Load Balancing Scheme (GWO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks

Authors

  • Devi PA Research Scholar, Dr. S.N.S. Rajalakshmi College of Arts and Science, Coimbatore
  • Karthikeyan K Assistant Professor and Head, Department of Computer Science, Palladam

DOI:

https://doi.org/10.26438/ijcse/v7i6.641649

Keywords:

MANET, load balancing, clustering, grey wolf optimization, packet delivery ratio, throughput, delay

Abstract

Load balancing is the research dimension in the field of mobile ad hoc networks. From the several previously conducted research works it is inferred that clustering based load balancing approach offers better solution. Many protocols are proposed formerly, and multipath routing seems to be better one. This research work aims to make use of grey wolf optimization technique for clustering the nodes. Conventional multipath routing strategy is employed along with adaptive load balancing approach. Simulation settings are made and the performance metrics namely packet delivery ratio, throughput, packets drop, overhead and delay are taken into account for evaluating the efficiency of the approach.

References

[1]. Hui, Chenga, Shengxiang, Yangb, Xingwei, Wangc, 2012. Immigrants-enhanced multi-population genetic algorithms for dynamic shortest path routing problems in mobile ad hoc networks. Int. J. Appl. Artif. Intell. 26 (7), 673–695.

[2]. Hui, Chenga, Shengxiang, Yangb, Jiannong, Cao, 2013. Dynamic genetic algorithm for the dynamic load balanced clustering problem in mobile ad hoc networks. J. Expert Syst. Appl. 40 (4), 1381–1392.

[3]. Shengxiang, Yang, Hui, Cheng, Fang, Wang, 2010. Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks. J. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 40 (1), 52–63.

[4]. Bhaskar, Nandi, Subhabrata, Barman, Soumen, Paul, 2010. Genetic algorithm based optimization of clustering in ad hoc networks. Int. J. Comput. Sci. Inf. Secur. 7 (1), 165–169.

[5]. Bo, Peng, Lei, Li, 2012. A method for QoS multicast based on genetic simulated annealing algorithm. Int. J. Future Gener. Commun. Netw. 5 (1), 43–60.

[6]. Abin, Paul, Preetha, K.G., 2013. A cluster based leader election algorithm for MANETs. In: International Conference on Control Communication and Computing, pp. 496–499.

[7]. Ting, Lu, Jie, Zhu, 2013. Genetic algorithm for energy efficient QoS multicast routing. IEEE Commun. Lett. 17 (1), 31–34.

[8]. John, M., Shea, Joseph, P., Macker, 2013. Automatic selection of number of cluster in networks using relative Eigen value quality. In: Proceedings of IEEE Military Communication, pp. 131–136.

[9]. Samaneh, A.D., Jamshid, A., 2016. Toward cluster-based weighted compressive data aggregation in wireless sensor networks. Ad Hoc Netw. 36 (1), 368–385.

[10]. Syed Zohaib, H.Z., Aloul, F., Sagahyroon, A., Wassim, El-Hajj, 2013. Optimizing complex cluster formation in MANETs using SAT/ILP techniques. J. IEEE Sens. 13 (6), 2400–2412.

[11]. Peng, Zhao, Xinyu, Yang, Wei, Yu, Xinwen, FuXinyu, 2013. A loose-virtual-clustering-based routing for power heterogeneous MANETs. J. IEEE Trans. Veh. Technol. 62 (5), 2290–2302.

[12]. Ibukunola, A., Modupea, Oludayo, O., Olugbarab, Abiodun, Modupea, 2013. Minimizing energy consumption in wireless adhoc networks with meta heuristics. In: Proceeding of 4th International Conference on Ambient Systems, Networks and Technologies, vol. 19, pp. 106–115.

[13]. El Khawaga, Sally E., Saleh, Ahmed I., Ali, Hesham A., 2016. An administrative cluster-based cooperative caching (ACCC) strategy for mobile ad hoc networks. J. Netw. Comput. Appl. 69, 54–76.

[14]. Jabbar W.A., Ismail M., Nordin R., 2017.Energy and mobility conscious multipath routing scheme for route stability and load balancing in MANETs. Simulation Modelling Practice and Theory. 77, 245-271.

[15]. Ali H.A., Areed M.F., Elewely D.I., 2018. An on-demand power and load-aware multi-path node-disjoint source routing scheme implementation using NS-2 for mobile ad-hoc networks. Simulation Modelling Practice and Theory. 80, 50 – 65.

[16]. Aruna Devi P., Karthikeyan K., 2018, Bio Inspired Ant Colony Optimization Based NeighborNode Selection and Enhanced Ad Hoc on DemandDistance Vector to Defending Against Black HoleAttack by Malicious Nodes in Mobile AD HOC NetworksJournal ofComputational and Theoretical Nanoscience. 15, 3011 – 3018.

[17]. Aruna Devi P., Karthikeyan K., 2019, Fuzzy Clustering and Constructive Relay-based Cooperative and Load Balanced Routing In MANET. Journal of Advanced Research in Dynamical and Control Systems. 11 (04), 1743 – 1753.

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Published

2019-06-30
CITATION
DOI: 10.26438/ijcse/v7i6.641649
Published: 2019-06-30

How to Cite

[1]
P. A. Devi and K. Karthikeyan, “Grey Wolf Optimization based Clustered On – Demand Load Balancing Scheme (GWO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks”, Int. J. Comp. Sci. Eng., vol. 7, no. 6, pp. 641–649, Jun. 2019.

Issue

Vol. 7 No. 6 (2019): IJCSE June Edition

Section

Research Article

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