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Communications and Networking. 16th EAI International Conference, ChinaCom 2021, Virtual Event, November 21-22, 2021, Proceedings

Research Article

Joint Computation Offloading and Wireless Resource Allocation in Vehicular Edge Computing Networks

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  • @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
Jiao Zhang1, Zhanjun Liu1, Bowen Gu2, Chengchao Liang1, Qianbin Chen1
  • 1: School of Communication and Information Engineering
  • 2: Faculty of Information Technology, Macau University of Science and Technology, Avenida Wai Long

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.

Keywords
Vehicular network Resource allocation Energy consumption Edge computing
Published
2022-04-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-99200-2_29
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