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Smart Grid and Internet of Things. 7th EAI International Conference, SGIoT 2023, TaiChung, Taiwan, November 18-19, 2023, Proceedings

Research Article

Research on Vehicle Networking Resource Management Based on Trust Model in Intersection Scene

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BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-55976-1_5,
        author={Yanyi Li and Zhaonian Li and Wenhui Wang and Zhenjiang Zhang},
        title={Research on Vehicle Networking Resource Management Based on Trust Model in Intersection Scene},
        proceedings={Smart Grid and Internet of Things. 7th EAI International Conference, SGIoT 2023, TaiChung, Taiwan, November 18-19, 2023, Proceedings},
        proceedings_a={SGIOT},
        year={2024},
        month={3},
        keywords={Internet of Vehicles Edge Computing Resource Allocation Trust Model Reinforcement Learning},
        doi={10.1007/978-3-031-55976-1_5}
    }
    
  • Yanyi Li
    Zhaonian Li
    Wenhui Wang
    Zhenjiang Zhang
    Year: 2024
    Research on Vehicle Networking Resource Management Based on Trust Model in Intersection Scene
    SGIOT
    Springer
    DOI: 10.1007/978-3-031-55976-1_5
Yanyi Li1, Zhaonian Li1, Wenhui Wang1, Zhenjiang Zhang1,*
  • 1: Beijing Jiaotong University, No. 3, Shangyuan Village
*Contact email: zhangzhenjiang@bjtu.edu.cn

Abstract

Recently, the traditional cloud computing network of vehicle networking has some problems to be solved: 1) the security trust between the vehicle and the subgrade unit; 2) The vehicle may be attacked by potentially malicious edge servers during task unloading. In this paper, in order to solve the above problems, aiming at the security problem of the edge computing network in the vehicle task unloading and resource allocation problems of multi-vehicle and multi-subgrade units in the urban intersection scene, the vehicle task unloading and resource management optimization algorithm and trust model based on the vehicle edge computing network are constructed, and the approximate optimal simulation is carried out for the urban intersection scene. Simulation results show that the proposed algorithm can effectively improve the overall efficiency of the system.

Keywords
Internet of Vehicles Edge Computing Resource Allocation Trust Model Reinforcement Learning
Published
2024-03-15
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-55976-1_5
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