边缘智能下基于强化学习的车联网路由协议  

Edge intelligence-assisted routing protocol for Internet of vehicles via reinforcement learning

作　　者：刘冰艺 刘煜昊 韩玮祯 夏振厂 吴黎兵[4] 熊盛武[1] LIU Bingyi;LIU Yuhao;HAN Weizhen;XIA Zhenchang;WU Libing;XIONG Shengwu(School of Computer Science and Artificial Intelligence,Wuhan University of Technology,Wuhan 430070,China;Sanya Science and Education Innovation Park,Wuhan University of Technology,Sanya 572000,China;Chongqing Research Institute,Wuhan University of Technology,Chongqing 401135,China;School of Cyber Science and Engineering,Wuhan University,Wuhan 430070,China)

机构地区：[1]武汉理工大学计算机科学与人工智能学院,湖北武汉430070 [2]武汉理工大学三亚科教创新园,海南三亚572000 [3]武汉理工大学重庆研究院,重庆401135 [4]武汉大学国家网络安全学院,湖北武汉430070

出　　处：《通信学报》2023年第11期110-119,共10页Journal on Communications

基　　金：国家自然科学基金资助项目(No.62272357,No.62176194,No.62202348,No.U20A20177,No.62272348);湖北省重点研发计划基金资助项目(No.2022BAA052);海南省重点研发计划基金资助项目(No.ZDYF2021GXJS014);重庆市科学基金资助项目(No.cstc2021jcyj-msxm4264);武汉理工大学重庆研究院研究基金资助项目(No.ZD2021-04,No.ZL2021-05)。

摘　　要：为实现复杂城市车联网环境下高可靠、自适应的数据包路由协议,提出一个端-边-云边缘智能架构,该架构包括终端用户层、边缘协作层和云计算层。在所提边缘智能架构的基础上,设计了一个基于多智能体强化学习的数据包路由协议。实验结果表明,相比于现有的紧急消息传输机制、基于交叉路口雾节点的分布式路由协议和基于双深度Q网络的路由协议,所提协议在消息传输时延和接收率方面分别取得29.65%~44.06%和17.08%~25.38%的优化。To achieve a highly reliable and adaptive packet routing protocol in a complex urban Internet of vehicles,an end-edge-cloud edge intelligence architecture was proposed which consisted of an end user layer,an edge collaboration layer,and a cloud computing layer.Based on the proposed edge intelligence architecture,an packet routing protocol based on multi-intelligent reinforcement learning technologies was designed.The experimental results show that the proposed protocol could significantly improve the transmission delay and the packet reception rate in the interval of 29.65%~44.06%and 17.08%~25.38%compared to the state-of-the-art transmission mechanism for emergency data(TMED),intersection fog-based distributed routing protocol(IDR),and double deep Q-net based routing protocol(DRP).

关 键 词：边缘智能 车联网 多智能体强化学习 数据包路由 

分 类 号：U495[交通运输工程—交通运输规划与管理]