Sensor Networks for Railway Monitoring: Detecting Trains from their Distributed Vibration Footprints

We report in this paper on a wireless sensor network deployment at railway tracks to monitor and analyze the vibration patterns caused by trains passing by. We investigate in particular a system that relies on having a distributed network of sensor nodes that individually contain efficient feature extraction algorithms and classifiers that fit the restricted hardware resources, rather than using few complex and specialized sensors. A feasibility study is described on the raw data obtained from a real-world deployment on one of Europe's busiest railroad sections, which was annotated with the help of video footage and contains vibration patterns of 186 trains. These trains were classified in 6 types by various methods, the best performing at an accuracy of 97%. The trains' length in wagons was estimated with a mean-squared error of 3.98. Visual inspection of the data shows further opportunities in the estimation of train speed and detection of worn-out cargo wheels.