Title: Task offloading and resource allocation for intersection scenarios in vehicular edge computing

Authors: Benhong Zhang; Chenchen Zhu; Limei Jin; Xiang Bi

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) is a promising paradigm to relieve the overload on corresponding edge servers by utilising the idle resources of nearby vehicles. However, due to the high mobility of vehicles, the vehicles perhaps drive out of the communication range of user equipments (UEs) during task processing. Therefore, it is important to select the appropriate vehicles as service nodes. In this paper, we propose a task offloading and resource allocation scheme for UEs near the intersection. We first study the availability of vehicles according to characteristics movement of vehicles at the intersection. Then, considering the delay constraint of tasks, and the computing capacity of vehicles and the edge server, a double deep Q-network approach is adopted to obtain the optimal policy of task offloading and resource allocation. Simulation results show that the proposed scheme has better performance in improving the average utility of UEs and task success rate.

Keywords: vehicular edge computing; VEC; intersection; computation offloading; resource allocation; deep reinforcement learning.

DOI: 10.1504/IJSNET.2023.131251

International Journal of Sensor Networks, 2023 Vol.42 No.1, pp.1 - 14

Received: 26 Oct 2022
Accepted: 04 Mar 2023
Published online: 01 Jun 2023 *

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