Efficient Multitask Scheduling for Completion Time Minimization in UAV-Assisted Mobile Edge Computing

Mobile edge computing (MEC) can alleviate the computing resource shortage problem of mobile user equipment (UEs). However, due to long communication distance or the obstruction of big obstacles, the direct communication link may not exist between a UE and a MEC node. It thus hinders the task offloading in MEC. Unmanned aerial vehicles (UAVs) have high degree of mobility and can carry lightweight computation and storage modules. This paper presents a UAV-assisted MEC method, in which the UAV can relay the task-input data of a UE to the MEC node and can also utilize the airborne computation and storage resource to shorten the execution time of the offloaded tasks. Considering the strict order dependency among multiple offloaded tasks, this paper optimizes the task scheduling and the UAV flight path in a joint manner. A heuristic algorithm based on particle swarm optimization (PSO) is also developed to find the optimal solution. The simulation results show that the proposed multitask scheduling method can always find the best tradeoff between the UAV's position and the wireless channel condition. In comparison to the other three baseline scheduling methods, the proposed method can use the minimum execution time to complete all the offloaded tasks.