Abstract
The issue of energy constraint has always been a challenging task in the research field of wireless sensor networks. Clustering is the most effective approach for enhancing the performance of wireless sensor networks to a great extent in terms of energy consumption, network lifetime and throughput. However, the uneven formation of clusters can lead to faster energy depletion of few nodes, and thus results in premature failure of the wireless sensor network. This paper proposes an energy-efficient scalable clustering protocol (EESCP) which considers inter-cluster and intra-cluster distances to generate balanced clusters. A novel Dragonfly algorithm based particle swarm optimization technique is proposed to optimize the selection of cluster heads. Further, extensive simulations have been carried out by varying node densities and network sizes to demonstrate the full potential of EESCP.
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Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks,38(4), 393–422. https://doi.org/10.1016/S1389-1286(01)00302-4.
Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks,52(12), 2292–2330. https://doi.org/10.1016/j.comnet.2008.04.002.
Fahmy, H. M. A. (2016). Wireless sensor networks. Berlin: Springer. https://doi.org/10.1109/ITW.1998.706478.
Singh, H., & Singh, D. (2016). Taxonomy of routing protocols in wireless sensor networks: A survey. In Proceedings of 2nd international conference on contemporary computing and informatics, ic3i (pp. 822–830). https://doi.org/10.1109/ic3i.2016.7918796.
Wang, F., & Liu, J. (2011). Networked wireless sensor data collection: Issues, challenges, and approaches. IEEE Communications Surveys & Tutorials,13(4), 673–687. https://doi.org/10.1109/SURV.2011.060710.00066.
Arboleda, L., & Nasser, N. (2006). Comparison of clustering algorithms and protocols for wireless sensor networks. In Proceedings of Canadian conference on electrical and computer engineering (pp. 1787–1792). https://doi.org/10.1109/ccece.2006.277358.
Dabirmoghaddam, A., Ghaderi, M., & Williamson, C. (2014). On the optimal randomized clustering in distributed sensor networks. Computer Networks,59, 17–32. https://doi.org/10.1016/j.bjp.2013.12.008.
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. https://doi.org/10.1109/TWC.2002.804190.
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. https://doi.org/10.1016/j.swevo.2013.04.002.
Kuila, P., & Jana, P. K. (2014). A novel differential evolution based clustering algorithm for wireless sensor networks. Applied Soft Computing,25, 414–425. https://doi.org/10.1016/j.asoc.2014.08.064.
Li, D., Liu, Q., Hu, X., & Jia, X. (2007). Energy efficient multicast routing in ad hoc wireless networks. Computer Communications,30(18), 3746–3756. https://doi.org/10.1016/j.comcom.2007.09.003.
Mohajerani, A., & Gharavian, D. (2015). An ant colony optimization based routing algorithm for extending network lifetime in wireless sensor networks. Wireless Networks,22(8), 2637–2647. https://doi.org/10.1007/s11276-015-1061-6.
Khalil, E. A., & Attea, B. A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation,1(4), 195–203. https://doi.org/10.1016/j.swevo.2011.06.004.
Kennedy, J. (2010). Particle swarm optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning (pp. 760–766). Boston, MA: Springer. https://doi.org/10.1007/978-0-387-30164-8_630.
Dorigo, M., Birattari, M., & Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine,1(4), 28–39. https://doi.org/10.1109/MCI.2006.329691.
Holland, J. H. (1973). Genetic algorithms and the optimal allocation of trials. SIAM Journal of Computing,2(2), 88–105. https://doi.org/10.1393/ncr/i2004-10001-9.
Storn, R., & Price, K. (1997). Differential evolution—A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization,11(4), 341–359. https://doi.org/10.1023/A:1008202821328.
Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications,27(4), 1053–1073. https://doi.org/10.1007/s00521-015-1920-1.
Sambandam, R. K., & Jayaraman, S. (2016). Self-adaptive dragonfly based optimal thresholding for multilevel segmentation of digital images. Journal of King Saud University—Computer and Information Sciences. https://doi.org/10.1016/j.jksuci.2016.11.002.
Daely, P. T., & Shin, S. Y. (2016). Range based wireless node localization using Dragonfly algorithm. In Proceedings of eighth international conference on ubiquitous and future networks (ICUFN) (pp. 1012–1015). https://doi.org/10.1109/icufn.2016.7536950.
Lim, C. P., & Jain, L. C. (2009). Advances in swarm intelligence. Studies in Computational Intelligence,248, 1–7. https://doi.org/10.1007/978-3-642-04225-6_1.
Hema, C., Sankar, S., & Sandhya. (2016). Energy efficient cluster based protocol to extend the RFID network lifetime using Dragonfly algorithm. In International conference on communication and signal processing, ICCSP (Vol. 600048, pp. 530–534). https://doi.org/10.1109/iccsp.2016.7754194.
Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing,3(4), 366–379. https://doi.org/10.1109/TMC.2004.41.
Ye, M. Y. M., Li, C. L. C., Chen, G. C. G., & Wu, J. (2005). EECS: An energy efficient clustering scheme in wireless sensor networks. In Proceedings of 24th IEEE international conference on performance, computing, and communications (pp. 535–540). https://doi.org/10.1109/pccc.2005.1460630.
Kumar, D., Aseri, T. C., & Patel, R. B. (2009). EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks. Computer Communications,32(4), 662–667. https://doi.org/10.1016/j.comcom.2008.11.025.
Kumar, D. (2014). Performance analysis of energy efficient clustering protocols for maximising lifetime of wireless sensor networks. IET Wireless Sensor Systems,4(1), 9–16. https://doi.org/10.1049/iet-wss.2012.0150.
Chamam, A., & Pierre, S. (2010). A distributed energy-efficient clustering protocol for wireless sensor networks. Computers & Electrical Engineering,36(2), 303–312. https://doi.org/10.1016/j.compeleceng.2009.03.008.
Liu, Z., Zheng, Q., Xue, L., & Guan, X. (2012). A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks. Future Generation Computer Systems,28(5), 780–790. https://doi.org/10.1016/j.future.2011.04.019.
Latiff, N. M. A., Tsimenidis, C. C., Sharif, B. S., & Kingdom, U. (2007). Energy-aware clustering for wireless sensor networks using particle swarm optimization. In Proceedings of 18th annual IEEE international symposium on personal, indoor and mobile radio communications (PIMRC’07) (pp. 5–9). https://doi.org/10.1109/pimrc.2007.4394521.
Rahmanian, A., Omranpour, H., Akbari, M., & Raahemifar, K. (2011). A novel genetic algorithm in LEACH-C routing protocol for sensor networks. In Proceedings of 24th Canadian conference on electrical and computer engineering, CCECE (pp. 1096–1100). https://doi.org/10.1109/ccece.2011.6030631.
Elhabyan, R. S. Y., & Yagoub, M. C. E. (2015). Two-tier particle swarm optimization protocol for clustering and routing in wireless sensor network. Journal of Network and Computer Applications,52, 116–128. https://doi.org/10.1016/j.jnca.2015.02.004.
Rao, P. C. S., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks,23(7), 2005–2020. https://doi.org/10.1007/s11276-016-1270-7.
Lalwani, P., Banka, H., & Kumar, C. (2016). BERA: A biogeography-based energy saving routing architecture for wireless sensor networks. Soft Computing. https://doi.org/10.1007/s00500-016-2429-y.
Sree Ranjini, S. R., & Murugan, S. (2017). Memory based hybrid Dragonfly algorithm for numerical optimization problems. Expert Systems with Applications,83, 63–78. https://doi.org/10.1016/j.eswa.2017.04.033.
Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. A. (2008). Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation,12(1), 64–79.
Chen, Y., & Zhao, Q. (2005). On the lifetime of wireless sensor networks. IEEE Communications Letters,9(11), 976–978. https://doi.org/10.1109/LCOMM.2005.11010.
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Singh, H., Singh, D. An Energy Efficient Scalable Clustering Protocol for Dynamic Wireless Sensor Networks. Wireless Pers Commun 109, 2637–2662 (2019). https://doi.org/10.1007/s11277-019-06701-7
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DOI: https://doi.org/10.1007/s11277-019-06701-7