A dual-layer UAV-assisted mobile edge computing system for disaster rescue: Coordinated optimization of coverage, obstacle-avoidance path planning and task offloading

Abstract

This study addresses critical challenges in urban disaster rescue operations, such as fires, including communication failures, complex environments, and information scarcity. We propose a novel Dual-layer UAV-assisted Mobile Edge Computing (DUAMEC) system, leveraging an air–space–ground collaborative communication framework and intelligent task scheduling to overcome traditional limitations like information blind spots, decision-making delays, and inefficient response. DUAMEC innovatively combines a high-altitude upper-layer UAV (U-UAV) for wide-area coverage and a low-altitude down-layer UAV (D-UAV) for task processing, achieving strong coverage, low latency, and high energy efficiency. The core innovations of the DUAMEC system are manifested in the following aspects: First, we propose a grid-based adaptive multi-stage greedy optimization algorithm for optimal UAV deployment, dynamically generating multi-level candidate grids and employing adaptive step-size contraction. An uncovered-point compensation mechanism ensures continuous area coverage. Second, we design a Multi-Agent TD3 with Hindsight Priority Experience Replay (MATD3-HP) algorithm, utilizing a multi-dimensional state space and compound reward mechanism to optimize resource allocation, path planning, and task offloading in dynamic obstacle environments. Experimental results demonstrate that compared to conventional single-layer UAV-MEC systems and fixed path planning schemes, the DUAMEC system achieves an 66.78% reduction in system overhead while maintaining 98% user coverage. Simultaneously, it sustains stable performance with low task processing latency and energy consumption even in scenarios with dense user distribution and highly dynamic obstacles, thereby providing an efficient and reliable intelligent solution for urban disaster rescue operations.