A Nature-Inspired Energy-Efficient Clustering Protocol Based on Improved Hybrid Grey Wolf Optimization for FANETs

Flying ad hoc networks (FANETs) are composed of multiple UA V nodes that are distributed across a wide area and linked via wireless communication to gather and transmit environmental data. However, the battery-powered UAVs in FANETs imposes significant energy limitations, thereby shortening the network's operational lifespan. This paper proposes an energy-efficient clustering protocol based on improved hybrid grey wolf optimization (EEC-IHGWO), which can optimally select cluster heads (CHs), significantly extend network lifetime and maximize energy efficiency. The selection criteria include factors like remaining energy, the distance from cluster members to CHs, and node density. The performance of the EEC-IHGWO method, measured by network lifetime, the number of living nodes, and remaining energy, is assessed against traditional algorithms such as LEACH, as well as the more recent PSO and FCM. The simulation results demonstrate that EEC-IHGWO sustains a longer network lifetime and higher energy efficiency.