Research Article
Energy-Efficient Joint Offloading and Resource Allocation Strategy in Vehicular Networks
@INPROCEEDINGS{10.1007/978-3-030-93398-2_59, author={Wei Wu and Ning Wang and Xuanli Wu and Lin Ma}, title={Energy-Efficient Joint Offloading and Resource Allocation Strategy in Vehicular Networks}, proceedings={Wireless and Satellite Systems. 12th EAI International Conference, WiSATS 2021, Virtual Event, China, July 31 -- August 2, 2021, Proceedings}, proceedings_a={WISATS}, year={2022}, month={1}, keywords={Vehicular networks Mobile edge computing Partial offloading Communication and computation resource allocation}, doi={10.1007/978-3-030-93398-2_59} }- Wei Wu
Ning Wang
Xuanli Wu
Lin Ma
Year: 2022
Energy-Efficient Joint Offloading and Resource Allocation Strategy in Vehicular Networks
WISATS
Springer
DOI: 10.1007/978-3-030-93398-2_59
Abstract
In the vehicular networks integrated with mobile edge computing (MEC), vehicle users are permitted to offload latency-sensitive and computation-intensive tasks to nearby MEC servers, which can extend battery life of the vehicle while improving the experience of users. In this paper, we consider a multi-user computation offloading scenario in vehicular networks with MEC server, in which tasks are executed at vehicle and MEC server parallelly through partial offloading. However, the finite communication and computation resource limit the flexibility of offloading. We propose a joint offloading and resource allocation algorithm based on improved hybrid particle swarm and simulated annealing to reduce the system energy consumption as much as possible. The simulation results demonstrate that our algorithm performs well in convergence and energy consumption under strict time constraint.
