The rapid advancement of sixth-generation (6G) networks and artificial intelligence technologies is leading to the emergence of collaborative intelligent transportation systems (C-ITS), which is regarded as an essential trend in the future of transportation. Integrating Internet of Things (IoT) with C-ITS is an efficient solution to provide real-time data collection and status monitoring for vehicles and infrastructures to improve the intelligence and reliability of C-ITS. In order to address the challenges of limited battery energy and low computing power of IoT nodes, integrating wireless power transfer (WPT) with mobile edge computing (MEC) is considered as a promising solution to improve their lifetime and computational capability for IoT nodes. In this paper, we investigate a distributed dynamic computing offloading model for an MEC-enabled C-ITS, where multiple roadside units (RSUs) collaborate to provide offloading services to wireless devices (WDs). We formulate the task offloading and bandwidth resource allocation as a distributed Stackelberg game. The WDs act as leaders, aiming to maximize their computing rate by offloading tasks to RSU or performing local computing. The RSUs act as followers, optimizing their bandwidth allocation based on the WDs’ offloading decisions, thereby improving the overall system computing rate. We prove the existence of a Stackelberg equilibrium (SE) and propose a multi-agent reinforcement learning algorithm to enable WDs to select offloading decisions and help RSUs optimize bandwidth allocation. Numerical simulation results demonstrate that the proposed scheme offers significant performance improvements over existing methods.

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.

Wireless-powered mobile edge computing (WP-MEC) has gained significant attention for enabling energy-sustainable and low-latency Internet of Things (IoT) applications in domains such as smart cities and industrial automation. Heterogeneous computing resources, which encompass devices with diverse computational capabilities, are essential to support adaptive task-offloading and resource-efficient service provisioning. However, integrating wireless power transfer (WPT) with mobile edge computing (MEC) in dynamic environments remains challenging, particularly in the efficient coordination of heterogeneous resources. This article investigates a distributed optimization problem in a wireless-powered heterogeneous MEC network, where base station association, binary offloading decisions, and distributed resource allocation are closely intertwined. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and propose a novel decentralized solution framework that decomposes it into two subproblems: deep reinforcement learning (DRL) is employed to optimize base station selection and task-offloading strategies, while convex optimization techniques, including the Lagrangian method, are used to allocate bandwidth and computational resources at the base stations. Simulation results demonstrate that the proposed approach significantly enhances the energy efficiency and computational performance of wireless edge computing compared to existing benchmarks, confirming its practical value for resource-constrained MEC systems.