Research Article
Joint Computation Offloading and Wireless Resource Allocation in Vehicular Edge Computing Networks
@INPROCEEDINGS{10.1007/978-3-030-99200-2_29, author={Jiao Zhang and Zhanjun Liu and Bowen Gu and Chengchao Liang and Qianbin Chen}, title={Joint Computation Offloading and Wireless Resource Allocation in Vehicular Edge Computing Networks}, proceedings={Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings}, proceedings_a={CHINACOM}, year={2022}, month={4}, keywords={Vehicular network Resource allocation Energy consumption Edge computing}, doi={10.1007/978-3-030-99200-2_29} }- Jiao Zhang
Zhanjun Liu
Bowen Gu
Chengchao Liang
Qianbin Chen
Year: 2022
Joint Computation Offloading and Wireless Resource Allocation in Vehicular Edge Computing Networks
CHINACOM
Springer
DOI: 10.1007/978-3-030-99200-2_29
Abstract
In vehicular edge computing (VEC) networks, a promising strategy for in-vehicle user equipments (UEs) with limited battery capacity is to offload data-intensive or/and latency-sensitive services to VEC servers via vehicle-to-infrastructure (V2I) links to reduce their energy consumption (EC). However, limited power supply, inadequate computation capability, and dynamic task latency make it extremely challenging to implement. In this work, we focus on designing a system-centric EC scheme in a VEC network. In particular, a joint computation offloading and wireless resource allocation problem is formulated to minimize the system EC by optimizing power control, offloading decision as well as subcarrier allocation while taking into account the dynamic and fickle time delay of each UE’s task. In light of the intractability of the problem, we propose an effective block coordinate descent (BCD)-based algorithm with greedy search to find a high-quality sub-optimal solution. Simulation results illustrate that the proposed algorithm has better energy savings than the benchmarks.
