Optimizing UAV Swarm Communication in Urban Scenarios: An Enhanced Fuzzy LEACH Algorithm Based Approach

Abstract

With the rapid improvement in drone technology, autonomous unmanned aerial vehicle (UAV) swarms are increasingly finding applications in traffic management, pollution monitoring, package delivery, security surveillance, and other essential services. In this paper, we propose an enhanced low-energy adaptive clustering hierarchy (LEACH) algorithm for UAV swarm communication in urban scenarios. We consider that the cluster head (CH) is selected based on attributes like cluster size, distance from the base station (BS), battery percentage, and line-of-sight (LOS), which enhances the effective packet delivery and helps the system sustain a longer duration. We propose a two-level hierarchy where the CHs are selected based on the enhanced LEACH algorithm from a dedicated pool of parent drones (PDs). We ensure that the PDs are always in the LOS of the BS to prevent the link outage. We perform LOS and Non-LOS link loss analysis based on the COST 231 Walfisch-Ikegami model. We show through simulation results that the proposed algorithm optimizes the existing LEACH algorithm, increasing system lifetime by around 66%, improving packet throughput, and the overall packet delivery by almost double.