2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 1 , PP: 75-88, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
K. Rajeswari 1 * , B. Arun Kumar 2
Doi: https://doi.org/10.54216/JISIoT.170106
In vehicle ad hoc networks (VANETs), vehicles often need to perform complex computing tasks that may exceed their processing capabilities within the required period to provide enhanced services. A common approach to improving service performance is to offload tasks to roadside units (RSUs). However, RSUs might not always have sufficient resources to manage all task assignments effectively. Given the increasing processing power of modern vehicles, task delegation to other vehicles presents a viable alternative to relying solely on RSUs. To achieve this, we first introduce a probabilistic approach that relaxes discrete actions, such as cloud server selection, into a continuous space. We then implement a Supportive Multi-Agent Deep Reinforcement Learning (SMADRL) technique that minimizes total system costs, including Vehicle device energy consumption and cloud server rental charges, by utilizing a centralized training and distributed execution approach. In this framework, each Vehicle device operates as an independent agent, learning efficient decentralized policies that reduce computing pressure on the devices. Experimental results show that the proposed SMADRL framework effectively learns dynamic offloading policies for each Vehicle device and notably outperforms four state-of-the-art DRL-based agents and two heuristic frameworks, resulting in reduce overall system costs.
Task Offloading , Collaborative Model , Multi-agent Deep Reinforcement Learning (MADRL)
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