A Novel Routing Method Based on Train Status for The Wireless Ad Hoc Network of TACS

The train autonomous circumambulate system (TACS) puts forward higher requirement on the real-time performance and reliability of data transmission. Traditional infrastructured networks have problems of high construction and maintenance costs, large latency, and low communication survivability. Therefore, this paper proposes a architecture of clustered ad hoc network for TACS. A multipath routing algorithm based on location information and speed of train (LSBR) is proposed. The method configures the static routes of the trackside nodes based on the location information, and generates a path set. Then, the set of paths is taken as input, and the complete paths are selected dynamically using a reinforcement learning method. This method realizes low-delay and high-reliability communication between trains. Simulation results show, when the train is running at high speed (120-160km/h), the packet delivery rate (PDR) is 57% and 38% higher than the RMQS-au and QMAR-AODV algorithms respectively, reaching 95%. In the worst case, the end-to-end delay $(D_{e2e})$ is $6ms$ and $2.7ms$ lower than RMQS-au and QMAR-AODV, respectively, only $9.8ms$ . The performance of delay and reliability meets the train-to-train communication requirements of TACS.