Sep 2026· IEEE Internet of Things Journal· Vol 13, pp. 40378-40392· 0 citations· 35 references
Abstract
Vehicular edge computing (VEC) is viewed as an important component for vehicle networks due to the prevalence of smart vehicles. However, stringent requirements such as ultralow latency and minimal energy consumption should be achieved for the emerging transmission-and-computation-intensive applications due to the limited communication and computing resources. To address these challenges, this article proposes a multiagent priority-based twin-delayed deep deterministic policy gradient (MAPTD3) approach for the joint optimization of task offloading and power allocation in a VEC system at the millimeter-wave (mmWave) band. First, the joint task offloading and power allocation problem is formulated as a nonconvex mixed-integer nonlinear program (MINLP) with a multiplicative objective function subject to delay and power constraints. Second, the proposed approach integrates two key techniques: a dynamic variance noise (DVN) mechanism for adaptive exploration and prioritized experience replay (PER) to enhance sample efficiency. Finally, it is shown in the simulation results that MAPTD3 consistently outperforms existing benchmarks with 96%–99% optimality ratios (ORs) under various load scenarios.
Vehicular edge computing (VEC), a key enabler for the Internet of Things (IoT) in intelligent transportation, addresses onboard processing constraints through collaborative task offloading among vehicles, facilitating latency-sensitive applications such as autonomous driving. However, developing efficient offloading strategies remains particularly challenging in high-density vehicular networks, where intensive computational demands coexist with severely constrained intervehicle communication ranges due to signal blockage. To handle this, we propose M4O, a mobility-aware task offloading framework supporting multihop, multiuser, and multitask offloading optimization. M4O intelligently integrates vehicle mobility patterns and enables relay-assisted offloading to enhance system effectiveness and robustness. The framework employs a dual-algorithm approach: the advantage actor–critic (A2C) for indivisible tasks and the hybrid proximal policy optimization (H-PPO) for divisible tasks, both optimized to minimize the temporally coupled composite cost of time and resources. Extensive experiments demonstrate that the deep reinforcement learning (DRL)-based solutions of M4O deliver stable and efficient offloading strategies, outperforming existing benchmarks by significant margins in cost efficiency. Our code is available at https://github.com/Zhouym1028/M4O
Momiao Zhou, Yimin Zhou, Yanshi Sun et al.· IEEE Internet of Things Jour...· 0 citations
With the advancement of autonomous driving and smart navigation, Internet of Vehicles (IoV) systems face stringent requirements for real-time data delivery and processing reliability. Traditional metrics cannot fully capture information timeliness due to network dynamics and packet loss. Existing approaches also struggle with the coupling between task offloading and resource allocation, lacking adaptability in dynamic IoV environments. To address these issues, we propose a joint optimization scheme using a deep Q-network (DQN). Specifically, we build an IoV system model incorporating V2V and V2I communication, and formulate an optimization problem to minimize the average age of information (AAoI) under delay, bandwidth, computing, and energy constraints. We then design a mixed-action DQN algorithm with dual-network architecture, experience replay, and an action mask mechanism to enhance training stability and environmental adaptability. Simulation results show that our DQN-based scheme achieves the lowest AAoI among Random, Greedy, A2C, and DDQN, with reductions of 29.5%, 8.9 %, 7.1 %, and $\mathbf{7. 6 \%}$, respectively. It also exhibits superior delay and energy performance, confirming its effectiveness for dynamic IoV task offloading and resource allocation.
Chao He, Wanting Wang, Dongfeng Fu et al.· 2026 International Conferenc...· 0 citations
A Prioritized Adaptive Weighting based on Deep Deterministic Policy Gradient (PAW-DDPG) as an enhanced Deep Deterministic Policy Gradient (DDPG) algorithm to minimize both processing delay and energy consumption by jointly optimizing user scheduling, partial-task offloading, and UAV trajectory is proposed.
W. Saber, Hanan Algamil, Fifi Farouk et al.· Future Internet· 0 citations
6G vehicular services, including cooperative perception, augmented reality navigation, and high-definition map updating, need computation support close to moving vehicles. Vehicular Edge Computing (VEC) is a natural solution, but the offloading decision becomes difficult when wireless channel conditions, vehicle density, and edge server loads vary simultaneously. In this paper, we study joint task offloading and resource allocation in 6G VEC with high- and low-frequency cooperation (HL-FC). We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP). Each vehicle decides its offloading ratio, transmission power, server association, and edge CPU request from local observations. To evaluate the proposed policy, we build a lightweight equation-driven Python simulator and compare MAPPO with Local-only, Edge-only, Random, and Greedy policies. Compared with Edge-only, MAPPO reduces the average system cost by 32.15%, 23.51%, and 17.13% under 10, 15, and 20 vehicles, respectively. It also improves the task completion rate by 21.00, 20.49, and 17.65 percentage points. Additional blockage experiments show that HL-FC keeps the policy more robust than high-frequency-only transmission under severe high-frequency blockage. The results reveal that MAPPO delivers better performance when edge resources become congested than in lightly loaded scenarios.
Zi-Heng Gu· 2026 8th International Confe...· 0 citations
The results demonstrate that the proposed PP-SAPF is suitable for real-time deployment in intelligent transportation systems (ITS) and autonomous vehicles where low latency, reliable connectivity, and adaptive resource management is significant.
Irshad Khan, Neetha Papanna Umalakshmi, Somshekhar Durgaiah et al.· Bulletin of Electrical Engin...· 0 citations
This paper addresses the joint task offloading and resource allocation problem in multi-user MEC systems and proposes a decentralized control framework based on Multi-Agent Reinforcement Learning (MARL), which achieves lower total system cost and faster convergence than the full-local, full-offload, and heuristic baselines.
Youssef Oukissou, Mohamed Amine Meddaoui, Ayoub Belaidi et al.· International journal of Com...· 0 citations
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