Distributed Deep Multi-Agent Reinforcement Learning for Cooperative Edge Caching in Internet-of-Vehicles

Abstract

Mobile edge computing (MEC) is becoming popular in many civilian applications. However, due to the broadcast nature of wireless connections, traditional MEC is open to eavesdropping attacks that endanger mobile users’ information confidentiality. Friendly jamming (FJ), as one of the physical layer security techniques, can efficiently protect users from such threats by degrading attackers’ wiretap channel capacities. In this paper, we propose an FJ-based physical layer security (PLS) mechanism to safeguard the confidentiality of data uploading in MEC services. We design a comprehensive security scheme that uses FJ from both base station (BS) and nearby mobile users to cover upload transmission. Accordingly, the eavesdropping risk zone (ERZ) is formulated as an area under high secrecy outage probability (SOP). To promote FJ from users, we further introduce Device-to-device (D2D) communication-based FJ, which is inspired by non-orthogonal multiple access (NOMA) techniques. Then, we formulate, simplify, and solve the problem by optimizing the MEC secrecy performance under the required risk, with efficiencies in data uploading, energy consumption, and incentive delivery. Furthermore, we tackle the challenge of efficient incentive design under information asymmetry by introducing Contract Theory and providing differentiated rewards. By simulations, we evaluate the mechanism in terms of secrecy performance and incentive efficiency under information asymmetry, and the results accordingly show the effectiveness of the proposed security mechanism.