Multi-Slot Secure Offloading and Resource Management in VEC Networks: A Deep Reinforcement Learning-Based Method - Zhen Li; Jialong Gong; Xiong Xiong; Dong Wang (IEEE Access)
Vehicular Edge Computing (VEC) networks have emerged as an efficient paradigm to support a range of computation-intensive applications. However, potential eavesdropping attacks pose significant threats to massive confidential information. Furthermore, the rapid growth of Intelligent Connected Vehicles (ICVs) intensifies the scarcity of communication and computational resources, generating an urgent need for improving resource utilization. Since both security concerns and resource limitation influence decisions on offloading strategies and transmission rate, it is necessary to investigate a joint optimization scheme. In this paper, we employ Physical Layer Security (PLS) and design a workflow to address the Joint Secure Offloading and Resource Allocation (JSORA) problem in VEC networks. This workflow models the interaction patterns of multiple ICVs with resource cluster on edge servers, and accurately reflects the occupancy and release of each resource unit. Given the dynamic nature and high complexity of the JSORA problem, we propose a Filtered Deep Reinforcement Learning-based Secure Offloading and Allocation (FDRL-SOA) algorithm to control the offloading and resource allocation within the cluster. Finally, our simulation results demonstrate significant improvements over benchmark methods, with energy consumption reduced by 5.16%, latency decreased by 1.4%, and system cost was minimized by 3.3%.