Title: A novel offloading algorithm for cost-sensitive tasks in VEC networks using deep reinforcement learning

Authors: Benhong Zhang; Hao Xu; Xiang Bi; Qiwei Hu

Addresses: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei – 230009, China; Engineering Research Centre of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei – 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei – 230009, China; Engineering Research Centre of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei – 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei – 230009, China; Engineering Research Centre of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei – 230009, China ' School of Computer Science and Information Engineering, Hefei University of Technology, Hefei – 230009, China; Engineering Research Centre of Safety Critical Industrial Measurement and Control Technology, Ministry of Education, Hefei – 230009, China

Abstract: Vehicular edge computing (VEC) provides a fundamental condition for the fast and complete realisation of complex intelligent functions in autonomous driving vehicles. However, different tasks have different requirements on time cost, for example, the tasks related to safe driving have strict requirements on real-time, which brings challenges to task offloading and resource allocation in VEC. This paper first defines different utility evaluation functions that measure the delay requirements of different tasks. Then, an optimisation problem is presented by considering the task types, the dynamic generation feature of tasks and the price cost that measures the willingness of the service providers. Finally, the task offloading and resource allocation process is modelled as a Markov decision process (MDP) and a D3QN-based algorithm is designed to solve our problem. Simulation results show that the proposed algorithm has better performance on utility and task success rate compared to other algorithms.

Keywords: vehicle edge computing; VEC; task offloading; resource allocation; Markov decision process; MDP; duelling double deep Q network; D3QN.

DOI: 10.1504/IJSNET.2025.149124

International Journal of Sensor Networks, 2025 Vol.49 No.2, pp.69 - 80

Received: 01 Feb 2024
Accepted: 28 Mar 2025
Published online: 14 Oct 2025 *

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