Enhancing Energy Efficiency in Cluster Based WSN using Grey Wolf Optimization
Abstract Wireless sensor networks (WSNs) are typically made up of small, low-power sensor nodes (SNs) equipped with capability for wireless communication, processing, and sensing. These nodes collaborate with each other to form a self-organizing network. They can collect data from their surrounding environment, such as temperature, humidity, light intensity, or motion, and transmit it to a central base station (BS) or gateway for additional processing and analysis. LEACH and TSEP are examples of cluster-based protocols developed for WSNs. These protocols require careful design and optimization of CH selection algorithms, considering factors such as energy consumption, network scalability, data aggregation, load balancing, fault tolerance, and adaptability to dynamic network conditions. Various research efforts have been made to develop efficient CH selection algorithms in WSNs, considering these challenges and trade-offs. In this paper, the Grey Wolf Optimization (GWO) algorithm is employed to address the problem of selecting CHs (CHs) in WSNs. The proposed approach takes into account two parameters: Residual Energy (RE) and the distance of node (DS)s from the BS. By visualizing and analyzing the GWO algorithm under variable parameters in WSNs, this research identifies the most appropriate node from all normal nodes for CH selection. The experimental results demonstrate that the proposed model, utilizing GWO, outperforms other approaches in terms of performance.
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Abbasi, A. A., & Younis, M., 2007. A survey on clustering algorithms for wireless sensor networks. Computer communications, 30(14-15), 2826–2841. 10.1016/j.comcom.2007.05.024
Agrawal, D., & Pandey, S., 2017. FLIHSBC: fuzzy logic and improved harmony search based clustering algorithm for wireless sensor networks to prolong the network lifetime. In International Conference on Ubiquitous Computing and Ambient Intelligence (pp. 570–578). Springer, Cham. 10.1007/978-3-319-67585-5_56
Agrawal, D., & Pandey, S., 2018. FUCA: fuzzy-based unequal clustering algorithm to prolong the lifetime of wireless sensor networks. International Journal of Communication Systems, 31(2), e3448. 10.1002/dac.3448
Bara'a, A. A., & Khalil, E. A., 2012. A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing, 12(7), 1950–1957. 10.1016/j.asoc.2011.04.007
Guru, S. M., Halgamuge, S. K., & Fernando, S., 2005. Particle swarm optimisers for cluster formation in wireless sensor networks. In Intelligent Sensors, Sensor Networks and Information Processing Conference. Proceedings of the 2005 International Conference (pp. 319–324). IEEE. 10.1109/ISSNIP.2005.1595599
Kousar, A., Mittal, N., & Singh, P., 2020. An improved hierarchical clustering method for mobile wireless sensor network using type-2 fuzzy logic. In Proceedings of ICETIT 2019 (pp. 128–140). Springer, Cham. 10.1007/978-3-030-30577-2_11
Kulkarni, R. V., Forster, A., & Venayagamoorthy, G. K., 2011. Computational intelligence in wireless sensor networks: A survey. IEEE communications surveys & tutorials, 13(1), 68–96. 10.1109/SURV.2011.040310.00002
Kulkarni, R. V., & Venayagamoorthy, G. K., 2011. Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(2), 262–267. 10.1109/TSMCC.2010.2054080
Latiff, N. A., Tsimenidis, C. C., & Sharif, B. S., 2007. Energy-aware clustering for wireless sensor networks using particle swarm optimization. In Personal, Indoor and Mobile Radio Communications, 2007. PIMRC 2007. IEEE 18th International Symposium (pp. 1–5). IEEE. 10.1109/PIMRC.2007.4394521
Parwekar, P., 2018. SGO a new approach for energy efficient clustering in WSN. International Journal of Natural Computing Research (IJNCR), 7(3), 54–72. 10.4018/IJNCR.2018070104
Rai, A. K. and Daniel, A. K., 2023. FEEC: fuzzy based energy efficient clustering protocol for WSN. International Journal of Systems Assurance Engineering and Management, 14, 297–307. 10.1007/s13198-022-01796-x
Rai, A. K., & Daniel, A. K., 2022. Energy-Efficient Model for Intruder Detection Using Wireless Sensor Network. Journal of Interconnection Networks, 22, 1–22. 10.1142/S0219265921490025
Rao, P. S., & Banka, H., 2017. Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach. Wireless Networks, 23(2), 433–452. 10.1007/s11276-015-1156-0
Rao, P. 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. 10.1007/s11276-016-1270-7
Saravanan, M., & Madheswaran, M., 2014. A hybrid optimized weighted minimum spanning tree for the shortest intrapath selection in wireless sensor network. Mathematical Problems in Engineering. 10.1155/2014/713427
Singh, B., & Lobiyal, D. K., 2012. A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-Centric Computing and Information Sciences, 2(1), 13. 10.1186/2192-1962-2-13
Verma, S., Sood, N., & Sharma, A. K., 2019. Genetic algorithm-based optimized cluster head selection for single and multiple data sinks in heterogeneous wireless sensor network. Applied Soft Computing, 85, 105788. 10.1016/j.asoc.2019.105788
Vijayalakshmi, K., & Anandan, P., 2019. A multi-objective Tabu particle swarm optimization for effective cluster head selection in WSN. Cluster Computing, 22(5), 12275–12282. 10.1007/s10586-017-1608-7
Ye, Z., & Mohamadian, H., 2014. Adaptive clustering based dynamic routing of wireless sensor networks via generalized ant colony optimization. Ieri Procedia, 10, 2–10. 10.1016/j.ieri.2014.09.063
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Zhao, X., Zhu, H., Aleksic, S., & Gao, Q., 2018. Energy-efficient routing protocol for wireless sensor networks based on improved grey wolf optimizer. KSII Transactions on Internet & Information Systems, 12(6), 4. 10.3837/tiis.2018.06.011
Rai, A. K., Tyagi, L. K., Kumar, A., Srivastava, S., & Fatima, N. (2023). Enhancing Energy Efficiency in Cluster Based WSN using Grey Wolf Optimization. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 12(1), e30632. https://doi.org/10.14201/adcaij.30632
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