Resource allocation for multi‐UAV‐assisted mobile edge computing to minimize weighted energy consumption
An Li, Longbin Dai, Lisu Yu
Abstract
Abstract This paper studies a multi‐unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) system, where multiple UAVs equipped with MEC server are deployed to provide computing offloading services for ground users with limited local computing resources. Specifically, each user divides its computing tasks into two parts: one part is offloaded to the associated UAV for calculation, and the remaining part is calculated locally. It is aimed to minimize the weighted energy consumption of all UAVs and all users by jointly optimizing the UAV trajectory, user scheduling, central processing unit (CPU) calculation frequency allocation, and offloading time allocation. However, the formulated problem is a mixed integer non‐convex problem, which is challenging to obtain an optimal solution. In order to effectively solve this, an efficient two‐stage iterative algorithm based on alternative optimization is proposed by decomposing the original problem into two sub‐problems to obtain a suboptimal solution. The simulation results show that the proposed scheme is superior to other benchmark schemes in reducing system energy consumption.