Joint Task Offloading and Trajectory Control for Multi-UAV-Assisted Mobile Edge Computing

Recent developments in unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) have provided users with flexible and resilient computing services. However, meeting the computing-intensive and latency-sensitive demands of users poses a significant challenge due to the limited energy resources of UAVs. To address this challenge, we present a joint optimization approach for multi-UAV-assisted MEC systems. First, we formulate a problem aimed at minimizing the total task completion delay, reducing the total UAV energy consumption, and maximizing the total amount of offloaded tasks by jointly optimizing task offloading and UAV trajectory control. Since the problem is a mixed-integer non-linear programming (MINLP) problem, we propose a joint task offloading and UAV trajectory control (JTOUTC) algorithm. Specifically, the original problem is divided into the subproblems of task offloading and UAV trajectory control, which are resolved alternately by adopting block alternate descent and successive convex approximation methods. Simulation results show that the proposed JTOUTC has superior system performance compared to other benchmark methods.