DRL-Based Secure Video Offloading in MEC-Enabled IoT Networks

Abstract

Wireless offloading in mobile-edge-computing (MEC)-enabled Internet of Things (IoT) networks inevitably suffers the risk of eavesdropping. Physical-layer security (PLS) approaches can be applied to prevent eavesdropping. However, the existing PLS techniques are not well targeted for videos due to the fact that video’s distortion characteristics, which allow encoding parameters to be flexibly adjusted to enhance security in offloading, are ignored. A deep reinforcement learning (DRL)-based real-time, secure, and efficient video offloading scheme is proposed in this article, where video frame resolution, one key parameter of video’s distortion characteristics, is introduced and jointly optimized with PLS scheme to guarantee video’s security, improve users’ Quality of Experience (QoE) and save energy consumption. We formulate a joint optimization problem of video frame resolution selection, computation offloading, and resource allocation strategy, to minimize energy consumption and maximize QoE in terms of delay and analytic accuracy, while subject to security rate, computing capability, and transmission power. To solve the formulated NP-hard problem with the form of high-dimensional nonlinear mixed-integer programming, the hierarchical reward-function-based DRL (JVFRS-CO-RA-MADDPG) algorithm is proposed to guide the agents to obtain the optimal policy efficiently. Finally, the simulation results show that the proposed algorithm outperforms the existing algorithms in terms of delay, energy consumption, and security level.