Edge intelligence-assisted routing protocol for Internet of vehicles via reinforcement learning - Bingyi LIU; Yuhao LIU; Weizhen HAN; Zhenchang XIA; Libing WU; Shengwu XIONG (Tongxin xuebao)
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).