Minimizing Time Overhead in VANET Task Offloading: A Novel Preparatory-Based Edge-Cloud Collaborative Model

 

K. Rajeswari1,*, B. Arun Kumar2

1Research scholar, Department of Computer Science and Engineering, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

2Professor and Head, Department of Artificial Intelligence and Data Science, Faculty of Engineering, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

Emails: rajeswarikamalk@gmail.com; arunkumar.oct06@gmail.com

Text Box: Abstract 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. Received: January 31, 2025 Revised: March 08, 2025 Accepted: April 12, 2025

 

Keywords: Task Offloading; Collaborative Model; Multi-agent Deep Reinforcement Learning (MADRL)