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

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).