JNACSISSN:2582-3817

Energy Efficient Genetic Algorithm Based Clustering Technique for Prolonging the Life Time of Wireless Sensor Network

Abstract

Wireless Sensor network plays a vital role in most of the real world applications and has gained a lot of interest in terms of research. In a WSN, the nodes are found to be positioned in remote area to observe, detect or gather data from that location and send the collected data to the receiver at another location. The main concern in this process is that a lot of energy is wasted during the process of data transmission from the source node to the destination. Since each node is provided with a limited power supply this cause the node to discharge its battery completely and thereby making the node to dead state. By adopting the suitable cluster head selection technique for data transmission this issue of energy dissipation can be reduced. In this paper we propose a concept of Primary Cluster Head (PCH), along with a genetic algorithm based selection process. The overall process includes a process of cluster formation, Cluster Head (CH) selection and Primary Cluster Head (PCH) selection among the CH using genetic algorithm. The complete process is simulated using NS-2 simulator and the results are compared with similar existing techniques for evaluating our better performance.

References

  • W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” inProc. IEEE Comput. Soc. 33rd Annu. Hawaii Int. Conf. Syst. Sci. (HICSS), Jan. 2000, pp. 1–10.

  • W. B. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,”IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660–670, Oct. 2002.

  • B. Baranidharan, S. Srividhya, and B. Santhi, “Energy efficient hierarchical unequal clustering in wireless sensor networks,” Indian Journal of Science and Technology,vol.7,no.3,pp.301– 305, 2014.

  • K. Akkaya and M. Younis, “A survey on routing protocols for wireless sensor networks,”Ad Hoc Netw., vol. 3, no. 3, pp. 325–349, 2005.

  • Md.AbdulAlim,Y.Wu,andW.Wang,“A fuzzy based clustering protocol for energy-efficient wireless sensor networks,” in Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE ‟13),pp.2874– 2878, Hangzhou, China, March 2013.

  • J.-S. Lee and W.-L. Cheng, “Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication,”IEEE Sensors Journal,vol.12,no.9,pp.2891–2897,2012.

  • S. A. Sert, H. Bagci, and A. Yazici, “MOFCA: multi-objective fuzzy clustering algorithm for wireless sensor networks,” Applied Soft Computing Journal,vol.30,pp.151–165,2015.

  • Y. Yoon and Y.-H. Kim, “An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks,” IEEE Transactions on Cybernetics,vol.43,no.5,pp.1473–1483, 2013.

  • F. Valdez, P. Melin, and O. Castillo, “An improved evolutionary method with fuzzy logic for combining particle swarm optimization and genetic algorithms,”Applied Soft Computing Journal, vol. 11, no. 2, pp. 2625–2632, 2011.

  • S. Misra and P. D. Thomasinous, “A simple, least-time, and energy-efficient routing protocol with one-level data aggregation for wireless sensor networks,”Journal of Systems and Software,vol.83,no.5,pp.852–860,2010.

  • H.Shu,Q.Liang,andJ.Gao,“Wireless sensor network lifetime analysis using interval type-2 fuzzy logic systems,”IEEE Transactions on Fuzzy Systems, vol. 16, no. 2, pp. 416–427, 2008. [12] S. M. Nekooei and M. T. Manzuri-Shalmani, “Location finding in wireless sensor network based on soft computing methods,” in Proceedings of the International Conference on Control, Automation and Systems Engineering (CASE ‟11), pp. 1–5, Singapore, July 2011.

  • W. Cheng, H. Shi, X. Yin, and D. Li, “An elitism strategy based genetic algorithm for streaming pattern discovery in wireless sensor networks,” IEEE Communications Letters,vol.15,no.4, pp. 419–421, 2011.

  • O. Younis and S. Fahmy, “Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach,” in Proceedings of the 23rd Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM ‟04),vol. 1, pp. 366–379, IEEE, Hong Kong, March 2004.

  • A. Ghosh, N. S. Mishra, and S. Ghosh, “Fuzzy clustering algorithms for unsupervised change detection in remote sensing images,”Information Sciences, vol. 181, no. 4, pp. 699–715, 2011.

  • G. E. Tsekouras and J. Tsimikas, “On training RBF neural networks using input-output fuzzy clustering and particle swarm optimization,”Fuzzy Sets and Systems,vol.221,pp.65–89,2013.

  • N.A.Alrajeh,S.Khan,J.Lloret,andJ.Loo,“Artificial neural network based detection of energy exhaustion attacks in wireless sensor networks capable of energy harvesting,”Ad-Hoc and Sensor Wireless Networks,vol.22,no.1- 2,pp.109–133,2014.

  • W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” inProc. IEEE Comput. Soc. 33rd Annu. Hawaii Int. Conf. Syst. Sci. (HICSS), Jan. 2000, pp. 1–10.

  • W. B. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “An application-specific protocol architecture for wireless microsensor networks,”IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660–670, Oct. 2002.

  • P. Nayak and A. Devulapalli, "A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime", IEEE Sensors J., vol. 16, no. 1, pp. 137-144, 2016.