Edge server deployment strategy based on multi-agent reinforcement learning in the internet of vehicles

被引：0
|
作者：
Li, Chuang [1 ]
Ji, Jianqiao [1 ]
Hu, Zhigang [2 ]
Zhou, Zhou [3 ]
机构：
[1] School of Computing, Hunan University of Technology and Business, Changsha,410205, China
[2] School of Computer Science and Engineering, Central South University, Changsha,410075, China
[3] School of Computer Science and Engineering, Changsha University, Changsha,410022, China
来源：
Zhongnan Daxue Xuebao (Ziran Kexue Ban)/Journal of Central South University (Science and Technology) | 2024年 / 55卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Deployment strategy - Edge computing - Edge server - Energy-consumption - Hard problems - Load-Balancing - Multi-agent reinforcement learning - Reinforcement learnings - Server deployment - Vehicle networkings;
D O I：
10.11817/j.issn.1672-7207.2024.07.011
中图分类号：
学科分类号：
摘要：
20
引用
收藏
页码：2567 / 2577
相关论文
共 50 条
  • [21] Multi-agent reinforcement learning for autonomous vehicles: a survey
    Dinneweth J.
    Boubezoul A.
    Mandiau R.
    Espié S.
    Autonomous Intelligent Systems, 2022, 2 (01):
  • [22] Reliability deployment of service function chain based on multi-agent reinforcement learning
    Liu, Guangyuan
    Huang, Shucui
    Li, Kai
    2022 IEEE 6TH ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC), 2022, : 1574 - 1578
  • [23] A cooperative lateral and vertical control strategy for autonomous vehicles based on multi-agent deep reinforcement learning
    Liu, Qianjie
    Xiong, Peixiang
    Zhu, Qingyuan
    Xiao, Wei
    Li, Gang
    Hu, Guoliang
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING, 2025,
  • [24] Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning
    Lu, Haifeng
    Gu, Chunhua
    Luo, Fei
    Ding, Weichao
    Zheng, Shuai
    Shen, Yifan
    IEEE ACCESS, 2020, 8 : 202573 - 202584
  • [25] A Task Offloading and Resource Allocation Strategy Based on Multi-Agent Reinforcement Learning in Mobile Edge Computing
    Jiang, Guiwen
    Huang, Rongxi
    Bao, Zhiming
    Wang, Gaocai
    FUTURE INTERNET, 2024, 16 (09)
  • [26] Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learning
    Lin, Yan
    Bao, Jinming
    Zhang, Yijin
    Li, Jun
    Shu, Feng
    Hanzo, Lajos
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2023, 72 (06) : 8256 - 8261
  • [27] Cooperative service caching and peer offloading in Internet of vehicles based on multi-agent meta-reinforcement learning
    Ning Z.
    Zhang K.
    Wang X.
    Guo L.
    Tongxin Xuebao/Journal on Communications, 2021, 42 (06): : 118 - 130
  • [28] Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning
    Wang, Cong
    Wang, Yaoming
    Yuan, Ying
    Peng, Sancheng
    Li, Guorui
    Yin, Pengfei
    NEURAL NETWORKS, 2024, 179
  • [29] Multi-Agent Reinforcement Learning for task allocation in the Internet of Vehicles: Exploring benefits and paving the future
    Ullah, Inam
    Singh, Sushil Kumar
    Adhikari, Deepak
    Khan, Habib
    Jiang, Weiwei
    Bai, Xiaoshan
    SWARM AND EVOLUTIONARY COMPUTATION, 2025, 94
  • [30] A collaborative energy management strategy based on multi-agent reinforcement learning for fuel cell hybrid electric vehicles
    Xiao, Yao
    Fu, Shengxiang
    Choi, Jongwoo
    Zheng, Chunhua
    2023 IEEE 98TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-FALL, 2023,
12345