EE‐FGO: Energy‐Efficient Fuzzy Grey Wolf Optimization for Cluster Head Selection in WSN

A crucial component of the Internet of Things (IoT) is the wireless sensor network (WSN), which comprises numerous sensor nodes detecting various atmospheric variations such as temperature, pressure and humidity. These sensor nodes rely on internal batteries to power their sensors and communicate data with other nodes within the network. Given the significance of energy consumption in WSN nodes, there is a growing focus on developing advanced models to address network challenges by minimizing overhead and optimizing energy efficiency. This paper introduces a novel approach that combines fuzzy logic with the Energy‐Efficient Fuzzy Grey Wolf Optimization (EE‐FGO) to facilitate cluster formation in determining optimal solutions for selecting aggregation points using cluster heads (CH) and computing the most efficient data transmission path from CHs to the base station (BS) inside the network. By strategically selecting multiple aggregation points, the proposed approach aims to maximise the node’s lifetime. Simulation results illustrate that the EE‐FGO approach outperforms several existing protocols, leading to significant network lifetime extension.