Resilience-Aware Completion Time Minimization in Cooperative Multi-UAV Data Processing System

Recent advances in mobile edge computing (MEC) have transformed unmanned aerial vehicles (UAVs) into intelligent platforms capable of distributed sensing, computing, and communication. However, coordinating multiple UAVs presents significant challenges in task assignment, path planning, and resource allocation. To address these issues, we propose a joint task assignment and resource allocation optimization (TARO) approach for efficient target point (TP) visit in multi-UAV MEC systems. Specifically, considering resilience in this work, each TP may require multiple visits, while each UAV is restricted to visiting any given TP only once, leading to non-overlapping visit requirements. We formulate an optimization model as a mixed-integer nonlinear programming (MINLP) to minimize the maximum task completion time among all UAVs.To solve the problem efficiently, we adopt a Benders-inspired decomposition framework with infeasibility elimination, which separates the original problem into a routing master problem and two continuous subproblems.In addition, efficient solution approaches (e.g., closed-form solution) are developed to solve them respectively. Simulation results show that TARO significantly reduces latency and enforces temporally separated repeated visits to the same TP under various mission configurations and bandwidth constraints, thereby improving resilient mission performance.