The Network Selection Strategy for Connected Vehicles Based on Mobile Edge Computing

被引:0
|
作者
Wang, Luyan [1 ]
Yang, Shouyi [1 ]
机构
[1] Zhengzhou Univ, Sch Informat Engn, Zhengzhou, Peoples R China
关键词
Autonomous driving; edge computing; network selection; matching game; RESOURCE; ALLOCATION;
D O I
10.1109/ICCSN55126.2022.9817611
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
With the continuous development of wireless communication technology and vehicle intelligence technology, in the new vehicle network, new applications such as autonomous driving, high-precision map distribution and sensor information transmission have put forward high requirements for network bandwidth, delay and reliability. However, the traditional single network and cloud computing center processing mode can not meet the service needs of vehicle applications. Edge computing technology avoids the delay problem of core network congestion by placing some computing capacity on the edge of network. Under the background of coexistence and integration of various types of wireless access networks, it is an effective solution and a hot issue to make the most efficient use of the communication resources of the whole network while making good service for vehicle users through network selection strategy. This paper analyzes the network selection scenarios under multi-user vehicles, and establishes the utility function of vehicle users and wireless networks respectively from the respective interests of vehicle users and wireless networks, and uses the idea of matching game to connect users to the network. The simulation results show that the strategy can meet the requirements of low blocking rate and high satisfaction of both users.
引用
收藏
页码:56 / 62
页数:7
相关论文
共 50 条
  • [31] Hierarchical Resource Distribution Network Based on Mobile Edge Computing
    Ren, Dewang
    Gui, Xiaolin
    Dai, Huijun
    An, Jian
    Liang, Xin
    Lu, Wei
    Chen, Meihong
    2017 IEEE 23RD INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS), 2017, : 57 - 64
  • [32] Network Intelligence Based on Network State Information for Connected Vehicles Utilizing Fog Computing
    Park, Seongjin
    Yoo, Younghwan
    MOBILE INFORMATION SYSTEMS, 2017, 2017
  • [33] Service placement strategy for joint network selection and resource scheduling in edge computing
    Junwei Xu
    Ruijuan Zheng
    Lei Yang
    Muhua Liu
    Jianqiang Song
    Mingchuan Zhang
    Qingtao Wu
    The Journal of Supercomputing, 2022, 78 : 14504 - 14529
  • [34] Service placement strategy for joint network selection and resource scheduling in edge computing
    Xu, Junwei
    Zheng, Ruijuan
    Yang, Lei
    Liu, Muhua
    Song, Jianqiang
    Zhang, Mingchuan
    Wu, Qingtao
    JOURNAL OF SUPERCOMPUTING, 2022, 78 (12): : 14504 - 14529
  • [35] A Novel Edge Computing Server Selection Strategy of LEO Constellation Broadband Network
    He, Meilin
    Zhong, Lei
    Tan, Huidong
    Qu, Ying
    Lai, Junyu
    2020 IEEE WORLD CONGRESS ON SERVICES (SERVICES), 2020, : 276 - 281
  • [36] Mobile Edge Computing and Resource Scheduling of Internet of Vehicles
    Zhang, Ke
    Lyu, Ying
    Zhang, Liguo
    PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE, 2020, : 4290 - 4295
  • [37] Differential Compression for Mobile Edge Computing in Internet of Vehicles
    Hu, Zhijuan
    Wang, Danyang
    Li, Zan
    Sun, Meng
    Wang, Weizhi
    2019 INTERNATIONAL CONFERENCE ON WIRELESS AND MOBILE COMPUTING, NETWORKING AND COMMUNICATIONS (WIMOB), 2019,
  • [38] Quantum Edge Computing for Data Analysis in Connected Autonomous Vehicles
    M Peixoto, Maycon Leone
    2024 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS, ISCC 2024, 2024,
  • [39] A mobile edge computing-based applications execution framework for Internet of Vehicles
    WU Libing
    ZHANG Rui
    LI Qingan
    MA Chao
    SHI Xiaochuan
    Frontiers of Computer Science, 2022, 16 (05)
  • [40] A mobile edge computing-based applications execution framework for Internet of Vehicles
    Libing Wu
    Rui Zhang
    Qingan Li
    Chao Ma
    Xiaochuan Shi
    Frontiers of Computer Science, 2022, 16