Abstract
Maximizing the lifetime of the wireless sensor networks (WSNs) is one of the biggest challenges due to the difficulty of changing their batteries when they run out of energy. Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the most famous protocols which have applied to solve this problem. The main drawback of LEACH is that it may choose a cluster head that has less energy. Therefore, it will die in a short time and the network lifetime will finish rapidly. Many researchers have applied swarm intelligence algorithm to solve this problem however most of these algorithms trapped in local minima and suffer from premature convergence. In this paper, we combine the sunflower optimization algorithm (SFO) with the lèvy flight to maximize the WSNs lifetime. Such a combination can help the SFO algorithm to avoid trapping in local minima due to the random walk of the lèvy flight. The proposed algorithm is called a modified sunflower optimization algorithm (MSFO). To verify the superiority of the MSFO we compare it with five algorithms in literature for different numbers of nodes and cluster heads. The results show that the lifetime of the WSNs which is using the proposed MSFO is longer than their lifetime when they applied the other algorithms.
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References
Adnan, M.A., Razzaque, M.A., Abedin, M.A., Reza, S.S., Hussein, M.R.: A novel cuckoo search based clustering algorithm for wireless sensor networks. In: Advanced Computer and Communication Engineering Technology, pp. 621–634. Springer, Cham (2016)
Bari, A., Jaekel, A., Bandyopadhyay, S.: Clustering strategies for improving the lifetime of two-tiered sensor networks. Comput. Commun. 31(14), 3451–3459 (2008)
Gomes, G.F., da Cunha, S.S., Ancelotti, A.C.: A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates. Eng. Comput. 35(2), 619–626 (2019)
Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, p. 10. IEEE (2000)
Heinzelman, W.B., Chandrakasan, A.P., Balakrishnan, H.: An application-specific protocol architecture for wireless microsensor networks. IEEE Trans. Wirel. Commun. 1(4), 660–670 (2002)
Eberhart, R., Kennedy, J.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)
Lindsey, S., Raghavendra, C.S.: PEGASIS: power-efficient gathering in sensor information systems. In: Proceedings, IEEE Aerospace Conference, vol. 3, pp. 3–3. IEEE (2002)
Mirjalili, S.: The ant lion optimizer. Adv. Eng. Softw. 83, 80–98 (2015)
Rao, P.S., Jana, P.K., Banka, H.: A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wirel. Netw. 23(7), 2005–2020 (2017)
Sharawi, M., Emary, E.: Clustering optimization for WSN based on nature-inspired algorithms. In: Nature-Inspired Computation in Engineering, pp. 111–132. Springer, Cham (2016)
Xiangning, F., Yulin, S.: Improvement on LEACH protocol of wireless sensor network. In: 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007), pp. 260–264. IEEE (2007)
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 210–214. IEEE (2009)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010), pp. 65–74. Springer, Heidelberg (2010)
Yang, X.S., Karamanoglu, M., He, X.: Multi-objective flower algorithm for optimization. Procedia Comput. Sci. 18, 861–868 (2013)
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Raslan, A.F., Ali, A.F., Darwish, A. (2020). A Modified Sunflower Optimization Algorithm for Wireless Sensor Networks. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_21
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DOI: https://doi.org/10.1007/978-3-030-44289-7_21
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