Jointly Optimizing Task Offloading and Resource Allocation in MEC With Secure Data Transmission: A Multi-DNNs Approach

Edge computing has emerged as a promising paradigm to enable low-latency and high-bandwidth Internet of Things (IoT) applications. However, owing to the intrinsic characteristics of IoT devices, this emerging paradigm still faces bottlenecks such as energy shortages and security vulnerabilities. In this paper, we consider a physical layer security empowered wireless powered mobile edge computing (WP-MEC) system where wireless devices (WDs) can harvest energy from radio frequency signals, and the delivered data is protected against eavesdropping by using the well-known Wyner's wiretap encoding scheme. We focus on maximizing the secure computation rate by jointly optimizing artificial noise power, resource allocation, and offloading decisions for a multi-antenna scenario with wireless devices and a potential eavesdropper. We formulate this sum secure computation rate (SSCR) maximization problem as a mixed-integer programming problem and design an integrated deep reinforcement learning framework consisting of a discrete policy network module, a continuous policy network module, and an optimization module to derive binary offloading decisions, continuous time allocation, and an artificial noise covariance matrix. We evaluate the proposed approach through extensive simulations, and the results demonstrate that our approach can significantly enhance the security, energy efficiency, and computing capacity of wireless edge computing compared with existing works.