Secure and Energy-Efficient Mobile Edge Computing with UAV-Mounted- RIS Assistance

This paper studies secure and energy-efficient mobile edge computing with UAV-mounted RIS assistance, in the presence of multiple users (UEs) and an eavesdropper (EV). With an objective of maximizing the minimum energy efficiency (EE) among all UEs in the company of an EV, we jointly optimize parameters including user scheduling, phase-shifts of the programmable reflecting elements, UAV trajectory design, UEs' power allocation, and their computation task and CPU frequency allocation. To ensure secure communication, we adopt power-splitting based artificial noise transmission. We solve this challenging problem by employing successive convex approximation and block coordinate descent techniques. We showcase the superiority of our proposed algorithm over existing studies through simulation results. Particularly, our proposed algorithm manages to achieve the EE very close to the benchmark performance, which assumes the absence of an EV.