Reconfigurable Intelligent Surface-Enabled Green and Secure Offloading for Mobile Edge Computing Networks

2025-07-22

AI Summary

This work investigates secure task offloading in a reconfigurable intelligent surface (RIS)-assisted multi-user uplink mobile edge computing (MEC) network under multi-antenna eavesdropping. Focusing on non-orthogonal multiple access (NOMA), it jointly optimizes user local computation, uplink transmit power, RIS phase-shift coefficients, and the access point’s linear detection matrix to maximize secrecy energy efficiency. Its key contributions are: (i) the first integration of RIS into a secure MEC offloading framework; and (ii) a robust optimization algorithm designed for both perfect and imperfect eavesdropper channel state information. The resulting non-convex problem is tackled via block coordinate descent, successive convex approximation, semidefinite relaxation, and the S-procedure. Simulation results demonstrate stable convergence and show that the proposed scheme reduces system energy consumption by up to 60% compared to an RIS-free baseline, significantly enhancing secrecy energy efficiency.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB
• Multiagent Systems: Mechanism Design

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

This paper investigates a multi-user uplink mobile edge computing (MEC) network, where the users offload partial tasks securely to an access point under the non-orthogonal multiple access policy with the aid of a reconfigurable intelligent surface (RIS) against a multi-antenna eavesdropper. We formulate a non-convex optimization problem of minimizing the total energy consumption subject to secure offloading requirement, and we build an efficient block coordinate descent framework to iteratively optimize the number of local computation bits and transmit power at the users, the RIS phase shifts, and the multi-user detection matrix at the access point. Specifically, we successively adopt successive convex approximation, semi-definite programming, and semidefinite relaxation to solve the problem with perfect eavesdropper's channel state information (CSI), and we then employ S-procedure and penalty convex-concave to achieve robust design for the imperfect CSI case. We provide extensive numerical results to validate the convergence and effectiveness of the proposed algorithms. We demonstrate that RIS plays a significant role in realizing a secure and energy-efficient MEC network, and deploying a well-designed RIS can save energy consumption by up to 60% compared to that without RIS. We further reveal impacts of various key factors on the secrecy energy efficiency, including RIS element number and deployment position, user number, task scale and duration, and CSI imperfection.

Problem

• Minimize energy in secure RIS-assisted MEC offloading
• Optimize RIS phase shifts and user power allocation
• Achieve robust design under imperfect eavesdropper CSI

Innovation

• Uses RIS for secure MEC offloading
• Optimizes energy with block coordinate descent
• Employs S-procedure for robust CSI design