Joint Offloading and Resource Allocation of UAV-assisted Mobile Edge Computing with Delay Constraints

Due to the increase of mobile applications and their users, and the limited coverage of infrastructure, computing resources will inevitably become insufficient. Motivated by this, we consider a UAV-assisted mobile edge computing system with multiple users, an edge server, a remote cloud server, and an unmanned aerial vehicle (UAV). Unmanned aerial vehicles can provide users with a wide range of communication and certain computing power. It can not only process the tasks received by users but also unload the tasks to the edge server or the cloud server. Our proposed scheme aims to optimize the unloading decision of the tasks among all users and the allocation of computing and communication resources so as to minimize the overall energy consumption and the costs of computing and maximum delay. In order to solve the joint optimization problem, we propose an efficient USS algorithm, which includes UAV location optimization algorithm, semi-qualitative relaxation method, and self-adaptive adjustment method. Our numerical results show that the proposed algorithm can significantly reduce the unloading cost of multi-user task compared with other four unloading decisions such as traditional cloud computing which only uses the edge server.