Enhancing Secrecy Energy Efficiency in RIS-Aided Aerial Mobile Edge Computing Networks: A Deep Reinforcement Learning Approach

TL;DR

This paper proposes a deep reinforcement learning-based optimization framework for RIS-assisted UAV mobile edge computing networks to enhance secrecy energy efficiency, ensuring secure task offloading with minimized UAV energy consumption.

Contribution

It introduces a joint optimization strategy for UAV trajectory, task offloading, and RIS phase shifts to improve secure energy-efficient task offloading in aerial MEC networks.

Findings

Effective safeguarding of task offloading transmissions.

Significant improvement in secrecy energy efficiency.

Preservation of UAV energy resources.

Abstract

This paper studies the problem of securing task offloading transmissions from ground users against ground eavesdropping threats. Our study introduces a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV)-mobile edge computing (MEC) scheme to enhance the secure task offloading while minimizing the energy consumption of the UAV subject to task completion constraints. Leveraging a data-driven approach, we propose a comprehensive optimization strategy that jointly optimizes the aerial MEC (AMEC)'s trajectory, task offloading partitioning, UE transmission scheduling, and RIS phase shifts. Our objective centers on optimizing the secrecy energy efficiency (SEE) of UE task offloading transmissions while preserving the AMEC's energy resources and meeting the task completion time requirements. Numerical results show that the proposed solution can effectively safeguard legitimate task offloading transmissions while preserving AMEC energy.