RF Energy Harvesting-Enabled UAV-Assisted FANET: A Multi-Objective Osprey Optimization Framework for Sustainable Clustering and Energy Management

Abstract

Flying Ad Hoc Networks (FANETs) are Unmanned Aerial Vehicles-assisted and have critical energy depletion problems that extremely reduce network lifetime and persistent IoT operation. Small onboard battery capacity, as well as the lack of smart energy replenishment systems, compromises the reliability of communication over dynamic aerial topologies. An Osprey Optimization Algorithm-based multi-objective model incorporating Radio Frequency Energy Harvesting in UAVassisted FANET clustering is proposed. The framework is a joint optimization of three-dimensional UAV positioning and selection of cluster heads based on a composite fitness score. 𝛹𝑖 Moreover, energy sustainability using a four-objective fitness function that balances harvested power, remaining energy, network life, and packet delivery ratio. The simulation outcomes indicate that the given framework can achieve a network lifetime of 347 rounds, a consumption of 0.0024 J/round, a packet delivery ratio of 94.2%, and a harvested power of 35.8 mW at 50 UAV nodes, with an RF-to-DC conversion efficiency of 80.4%. The framework proposed beats the current clustering algorithms, validating its excellence in providing self-sustainable energyefficient UAV-IoT network functionality.