UAV clustering scheme for FANETs using elbow-hybrid metaheuristic techniques

Abstract

Great strides have been made to realistically deploy multiple Unmanned Aerial Vehicles (UAVs) within the commercial domain, which demands a proper coordination and reliable communication among the UAVs. UAVs suffer from limited time of flight. Conventional techniques suffer from high delay, low throughput, and early node death due to aerial topology of UAV networks. To deal with these issues, this paper proposes a UAV parameter vector which considers node energy, channel state information and mobility of UAVs. By intelligently estimating the proposed parameter, the state of UAV can be predicted closely. Accordingly, efficient clustering may be achieved by using suitable metaheuristic techniques. In the current work, Elbow method has been used to determine optimal cluster count in the deployed FANET. The proposed UAV parameter vector is then integrated into two popular hybrid metaheuristic algorithms, namely, water cycle-moth flame optimization (WCMFO) and Grey Wolf-Particle Swarm optimization (GWPSO), thereby enhancing the lifespan of the system. A methodology based on the holistic approach of parameter and signal formulation, estimation model for intelligent clustering, and statistical parameters for performance analysis is carried out by the energy consumption of the network and the alive node analysis. Rigorous simulations are run to demonstrate node density variations to validate the theoretical developments for various proportions of network system sizes. The proposed method presents significant improvement over conventional state-of-the-art methods.