Abstract
Wireless sensor networks (WSNs) consist of spatially distributed low power sensor nodes and gateways along with sink to monitor physical or environmental conditions. In cluster-based WSNs, the Cluster Head is treated as the gateway and gateways perform the multiple activities, such as data gathering, aggregation, and transmission etc. Due to improper clustering some sensor nodes and gateways are heavily loaded and dies early. This decreases lifetime of the network. Moreover, sensor nodes and gateways are constrained by energy, processing power and memory. Hence, to design an efficient clustering is a key challenge in WSNs. To solve this problem, in this paper we proposed (1) a clustering algorithm based on the shuffled complex evolution of particle swarm optimization (SCE-PSO) (2) a novel fitness function by considering mean cluster distance, gateways load and number of heavily loaded gateways in the network. The experimental results are compared with other state-of-the-art load balancing approaches, like score based load balancing, node local density load balancing, simple genetic algorithm, novel genetic algorithm. The experimental results shows that the proposed SCE-PSO based clustering algorithm enhanced WSNs lifetime when compared to other load balancing approaches. Also, the proposed SCE-PSO outperformed in terms of load balancing, execution time, energy consumption metrics when compared to other existing methods.











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References
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.
Amirthalingam, K., et al. (2016). Improved leach: A modified leach for wireless sensor network. In IEEE international conference on advances in computer applications (ICACA) (pp. 255–258). IEEE.
Dietrich, I., & Dressler, F. (2009). On the lifetime of wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 5(1), 5.
Eberhart, R., Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the sixth international symposium on micro machine and human science, 1995. MHS’95 (pp. 39–43). IEEE.
Edla, D. R., Lipare, A., Cheruku, R., & Kuppili, V. (2017). An efficient load balancing of gateways using improved shuffled frog leaping algorithm and novel fitness function for WSNs. IEEE Sensors Journal, 17(20), 6724–6733.
Gattani, V. S., Jafri, S. H. (2016). Data collection using score based load balancing algorithm in wireless sensor networks. In International conference on computing technologies and intelligent data engineering (ICCTIDE) (pp. 1–3). IEEE.
Gupta, G., Younis, M. (2003). Load-balanced clustering of wireless sensor networks. In IEEE international conference on communications, 2003. ICC’03 (Vol. 3, pp. 1848–1852). IEEE.
Heinzelman, W. B. (2000) Application-specific protocol architectures for wireless networks. Ph.D. thesis, Massachusetts Institute of Technology.
Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on wireless communications, 1(4), 660–670.
Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for hierarchical wireless sensor networks. JNW, 2(5), 87–97.
Jakubcová, M., Máca, P., & Pech, P. (2014). A comparison of selected modifications of the particle swarm optimization algorithm. Journal of Applied Mathematics, 2014, 10.
Jakubcová, M., Máca, P., & Pech, P. (2015). Parameter estimation in rainfall-runoff modelling using distributed versions of particle swarm optimization algorithm. Mathematical Problems in Engineering, 2015, 1–13.
Kennedy, J. (2011). Particle swarm optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 760–766). Berlin: Springer.
Kuila, P., Gupta, S. K., & Jana, P. K. (2013). A novel evolutionary approach for load balanced clustering problem for wireless sensor networks. Swarm and Evolutionary Computation, 12, 48–56.
Kuila, P., & Jana, P. K. (2012). Energy efficient load-balanced clustering algorithm for wireless sensor networks. Procedia Technology, 6, 771–777.
Kuila, P., & Jana, P. K. (2014). Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach. Engineering Applications of Artificial Intelligence, 33, 127–140.
Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing, 25, 414–425.
Kumar, N., Kaur, J. (2011). Improved leach protocol for wireless sensor networks. In 2011 7th international conference on wireless communications, networking and mobile computing (WiCOM) (pp. 1–5). IEEE.
Lai, C. C., Ting, C. K., Ko, R. S. (2007). An effective genetic algorithm to improve wireless sensor network lifetime for large-scale surveillance applications. In IEEE congress on evolutionary computation, 2007, CEC 2007 (pp. 3531–3538). IEEE.
Low, C. P., Fang, C., Ng, J. M., & Ang, Y. H. (2008). Efficient load-balanced clustering algorithms for wireless sensor networks. Computer Communications, 31(4), 750–759.
Wang, N., Zhang, N., & Wang, M. (2006). Wireless sensors in agriculture and food industry-recent development and future perspective. Computers and electronics in agriculture, 50(1), 1–14.
Xiang, W., Wang, N., & Zhou, Y. (2016). An energy-efficient routing algorithm for software-defined wireless sensor networks. IEEE Sensors Journal, 16(20), 7393–7400.
Yan, J., Tiesong, H., Chongchao, H., Xianing, W., Faling, G. (2007). A shuffled complex evolution of particle swarm optimization algorithm. In International conference on adaptive and natural computing algorithms (pp. 341–349). Berlin: Springer.
Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. (2015). Cross-layer network lifetime maximization in interference-limited WSNs. IEEE Transactions on Vehicular Technology, 64(8), 3795–3803.
Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. (2015). Network-lifetime maximization of wireless sensor networks. IEEE Access, 3, 2191–2226.
Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys and Tutorials, 19(2), 828–854.
Yu, S., Wang, R., Xu, H., Wan, W., Gao, Y., & Jin, Y. (2011). WSN nodes deployment based on artificial fish school algorithm for Traffic Monitoring System. In 2011 IET international conference on smart and sustainable city (ICSSC 2011) (pp. 1–5). IEEE.
Zhang, J., Yang, T. (2013). Clustering model based on node local density load balancing of wireless sensor network. In 2013 Fourth international conference on emerging intelligent data and web technologies (EIDWT) (pp. 273–276). IEEE.
Zhao, H., Zhang, Q., Zhang, L., Wang, Y. (2015). A novel sensor deployment approach using fruit fly optimization algorithm in wireless sensor networks. In Trustcom/BigDataSE/ISPA, 2015 IEEE (Vol. 1, pp. 1292–1297). IEEE.
Zhou, Y., Wang, N., & Xiang, W. (2017). Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm. IEEE Access, 5, 2241–2253.
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Edla, D.R., Kongara, M.C. & Cheruku, R. SCE-PSO based clustering approach for load balancing of gateways in wireless sensor networks. Wireless Netw 25, 1067–1081 (2019). https://doi.org/10.1007/s11276-018-1679-2
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DOI: https://doi.org/10.1007/s11276-018-1679-2