An efficient Cluster Head Selection Approach in IoT Using Hybrid Grey Wolf Optimization and Squirrel Search Algorithm
TL;DR
The GWO-SSA approach provides a robust, scalable, and energy-efficient cluster solution for an IoT environment, making it suitable for real-time and large-scale sensor network applications.
Abstract
Internet of Things (IoT) involves a large number of interconnected sensor nodes, which sense, communicate, and become active in data processing in resource-constrained settings. This paper proposes a hybrid grey wolf optimization and squirrel search algorithm (GWO-SSA) for optimal cluster head (CH) selection in an IoT network to enhance energy efficiency, reduce delay, and prolong network lifetime. The proposed model integrates SSA into GWO to enhance exploration and exploitation balance, enabling efficient selection of CHs based on temperature, delay, energy, load, and cost function. Experimental results demonstrate that GWO-SSA significantly outperforms existing methods such as GA, ACO, PSO, IPSO, GWO, SSA, and BCO. The proposed approach reduces temperature by 11.93 % and delay by 32.82 % compared to GA, while achieving an energy efficiency improvement of 22.61%. Additionally, the number of alive nodes increased by 21.27%, indicating a substantial enhancement in network lifetime. Load handling capability is improved by 12.70 %, and the cost function is reduced by 15.35 %, confirming effective optimized performance. The GWO-SSA approach provides a robust, scalable, and energy-efficient cluster solution for an IoT environment. The significant improvements across multiple performance metrics validate the effectiveness of the hybrid approach, making it suitable for real-time and large-scale sensor network applications.