Cooperative Multi-UAV Edge Computing: Delay Minimization in Emergency Communications

Uncrewed aerial vehicles (UAVs) have emerged as an effective solution for emergency disaster-relief missions, providing communication services in place of damaged ground base stations (BSs) and offloading computational tasks for applications such as target recognition. Serving as mobile edge computing (MEC) platforms, UAVs enable near-source data processing, which is crucial when conventional communication infrastructures are unavailable. To maximize the system’s potential, we introduce a cooperative multi-UAV edge computing framework with inter-UAV offloading, specifically designed to support delay-sensitive computing tasks for mobile terminals in emergency communication networks. In this framework, we formulate a delay minimization problem through on a dynamic slot allocation, which jointly optimizes computing task scheduling, UAV computation resource allocation, and 3D UAV trajectory planning. This complex mixed integer nonlinear problem is decomposed into three manageable subproblems, which are solved sequentially using tailored algorithms, including a penalty-based task scheduling algorithm, a Karush-Kuhn-Tucker (KKT)-based computation resource allocation algorithm, and a successive convex approximation (SCA) method for 3D trajectory planning. We then employ the block coordinate descent method to develop a low-complexity edge computing offloading optimization algorithm. Simulation results show that the proposed algorithm efficiently utilizes the cooperative computing capabilities of multi-UAV systems. Compared with the benchmark algorithms, it achieves up to 18.3% and 44,1% reductions in total task delay, respectively, under different data input levels.