Joint Computation Offloading and Communication Design for Secure UAV-Enabled MEC Systems

In this paper, we investigate a secure unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system, where multiple ground users and a UAV collaborate to complete computing tasks in the presence of multiple eavesdroppers on the ground with exact locations. The UAV acts as a computing server and users can offload part of computing tasks to the air in order to relieve calculation pressure. The total energy consumption of the UAV is minimized by jointly optimizing the task allocation, the transmit power of each user equipment, and the trajectory of UAV, subject to the secrecy offloading rate constrains, the transmit power and UAV's trajectory constrains. To solve the difficult nonconvex optimization problem, an alternating algorithm based on the successive convex approximation is proposed to optimize the parameters iteratively. The simulation results demonstrate that the developed algorithm is effective and can decrease the energy consumption compared with other benchmark methods.