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Squirrel Search Optimization-Based Cluster Head Selection Technique for Prolonging Lifetime in WSN’s

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Abstract

The rapid advancement of technologies of wireless sensor network is gaining maximized attentioned across the scientific community due to its reliable coverage in real life applications. It has evolved as an indispensable technology with diverisifed capabilities as it facilitates potential information to the end users regarding a region of target under real time monitoring process. However, the characteristics of WSNs such as resource-constrained nature and infrastructure-less deployment has the possibility of introducing diversified problems that influences the network performance. Moreover, the process of handling the issues of suitable cluster head selection, energy stability and network lifetime improvement are still considered as herculean task of concern. In this paper, a Squirrel Search Optimization-based Cluster Head Selection Technique (SSO-CHST) is proposed for prolonging the lifetime in the sensor networks by utilizing a gliding factor that aids in the better determination of cluster head selection during the process of data aggregation and dissemination. It estimates the fitness value of sensor nodes and arranges them in ascending order, such that the node with least fitness value is identified as the cluster memner. On the other hand, the sensor nodes with high fitness value is confirmed as the potential cluster head. The simulation results of the proposed SSO-CHST with minimum number of rounds used for selecting cluster head confirmed better throughput of 13.48% and improved network lifetime of 17.92% with minimized energy consumptions of 15.29%, remarkable to the benchmarked schemes.

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Correspondence to N. Arunachalam.

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Arunachalam, N., Shanmugasundaram, G. & Arvind, R. Squirrel Search Optimization-Based Cluster Head Selection Technique for Prolonging Lifetime in WSN’s. Wireless Pers Commun 121, 2681–2698 (2021). https://doi.org/10.1007/s11277-021-08843-z

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