Glowworm Swarm Optimization based Clustered on – Demand Load Balancing Scheme (GSO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks

Authors

  • P Aruna Devi Dr. S.N.S. Rajalakshmi College of Arts and Science, Coimbatore
  • K Karthikeyan Department of Computer Science, Government Arts and Science College, Palladam

DOI:

https://doi.org/10.26438/ijcse/v7i8.130136

Keywords:

routing, load balancing, energy, QoS, MANET

Abstract

Obtaining quality of service (QoS) through several routing schemes attracts researchers in the field of MANETs. Optimized routing through energy aware load balanced schemes is plays a significant role in ensuring QoS as well as many real – time applications. In this phase of research work, Glowworm Swarm Optimization is used for performing clustering operation. An adaptive on – demand routing mechanism is also employed. Simulation settings are used for analyzing the performance of the GSO-COD-LBS with other routing protocols / solutions / schemes using the metrics packet delivery ratio, throughput, packets drop, overhead and delay. From the results that are obtained through simulations it is inferred that GSO-COD-LBS outperforms other existing routing protocols and our earlier proposed works.

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 NetworksJournalofComputational 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.

[18]. Aruna Devi P., Karthikeyan K., 2019, Grey Wolf Optimization based Clustered On – Demand Load Balancing Scheme (GWO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks. International Journal of Computer Sciences and Engineering, 7(6), 641-649.

Downloads

Published

2019-08-31
CITATION
DOI: 10.26438/ijcse/v7i8.130136
Published: 2019-08-31

How to Cite

[1]
A. D. P and K. K, “Glowworm Swarm Optimization based Clustered on – Demand Load Balancing Scheme (GSO-COD-LBS) for Heterogeneous Mobile Ad hoc Networks”, Int. J. Comp. Sci. Eng., vol. 7, no. 8, pp. 130–136, Aug. 2019.

Issue

Vol. 7 No. 8 (2019): IJCSE August Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.