Secrecy Offloading Rate Maximization for Multi-Access Mobile Edge Computing Networks

TL;DR

This paper proposes an optimization framework for maximizing secrecy offloading rates in multi-access MEC networks by jointly optimizing resource allocation, demonstrating improved security performance over benchmarks.

Contribution

It introduces a joint optimization scheme for power, task partition, and resource allocation to enhance secrecy offloading in multi-access MEC networks, considering physical layer security.

Findings

Secrecy offloading rate improved by up to 17.35% under certain conditions.

Joint optimization outperforms benchmark schemes in security performance.

Highlights the importance of multi-server computation offloading for security.

Abstract

This letter considers a multi-access mobile edge computing (MEC) network consisting of multiple users, multiple base stations, and a malicious eavesdropper. Specifically, the users adopt the partial offloading strategy by partitioning the computation task into several parts. One is executed locally and the others are securely offloaded to multiple MEC servers integrated into the base stations by leveraging the physical layer security to combat the eavesdropping. We jointly optimize power allocation, task partition, subcarrier allocation, and computation resource to maximize the secrecy offloading rate of the users, subject to communication and computation resource constraints. Numerical results demonstrate that our proposed scheme can respectively improve the secrecy offloading rate 1.11%--1.39% and 15.05%--17.35% (versus the increase of tasks' latency requirements), and 1.30%--1.75% and 6.08%--9.22% (versus the increase of the maximum transmit power) compared with the two benchmarks. Moreover, it further emphasizes the necessity of conducting computation offloading over multiple MEC servers.