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Security and Privacy in New Computing Environments. 6th International Conference, SPNCE 2023, Guangzhou, China, November 25–26, 2023, Proceedings

Research Article

Overview of Vehicle Edge Computing and Its Security

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-73699-5_8,
        author={Shaodong Han and Maojie Wang and Guihong Chen},
        title={Overview of Vehicle Edge Computing and Its Security},
        proceedings={Security and Privacy in New Computing Environments. 6th International Conference, SPNCE 2023, Guangzhou, China, November 25--26, 2023, Proceedings},
        proceedings_a={SPNCE},
        year={2025},
        month={1},
        keywords={Vehicle edge computing Reinforcement learning Federated learning},
        doi={10.1007/978-3-031-73699-5_8}
    }
    
  • Shaodong Han
    Maojie Wang
    Guihong Chen
    Year: 2025
    Overview of Vehicle Edge Computing and Its Security
    SPNCE
    Springer
    DOI: 10.1007/978-3-031-73699-5_8
Shaodong Han1, Maojie Wang1, Guihong Chen1,*
  • 1: Guangdong Polytechnic Normal University
*Contact email: chenguihong@gpnu.edu.cn

Abstract

The Internet of Vehicles (IoV) is a part of the Internet of Things (IoT). With the continuous development of the Internet of Things technology, the Internet of Vehicles technology has also made great progress. However, as more and more vehicles are connected to the Internet of Vehicles, the calculation and transmission of data in the Internet of Vehicles becomes more and more difficult, and at the same time, it is inevitable to face the pressure of data security and privacy protection. In order to solve the above problems, academia and industry have adopted many methods, combining mobile edge computing technology with the Internet of Vehicles to establish a vehicle edge computing network to solve the problems of data computing and transmission. Introducing blockchain technology and federated learning technology into vehicle edge computing. Therefore, the network addresses the issues of data security and privacy protection. Based on these, this paper summarizes the research results of many scholars in related fields, in order to provide reference and reference for subsequent related research.

Keywords
Vehicle edge computing Reinforcement learning Federated learning
Published
2025-01-01
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-73699-5_8
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