AMO-Driven Multi-UAV Deployment for Equitable User Allocation and Reduced Power Consumption

Abstract

Unmanned Aerial Vehicles (UAVs) have swiftly emerged as essential tools in civilian applications, particularly in disaster response and indoor wireless communication. However, the optimization of the three-dimensional placement of multiple UAVs to ensure equitable user allocation while enhancing power efficiency remains a complex challenge. This study introduces the Animal Migration optimization (AMO) algorithm to address this challenge comprehensively.Our investigation delves into diverse building scenarios encompassing varying dimensions and user distributions. We employ a multi-UAV strategy wherein users are systematically assigned to UAVs based on fitness values, fostering balanced resource allocation. Our experiments consistently demonstrate that the multi-UAV AMO approach outperforms single UAV strategies in both best and mean values across these diverse scenarios.Our research contributes critical insights for improving UAV deployments, with a specific focus on equitable user allocation in disaster response and indoor communication scenarios. As UAV applications continue to proliferate, our findings provide a robust foundation for enhancing deployment efficiency. Future research endeavors can further refine and advance these multi-UAV strategies, driving progress in this dynamic field. © 2023 IEEE.