Trainspotting: Combining fast features to enable detection on resource-constrained sensing devices

This paper focuses on spotting and classifying complex and sporadic phenomena directly on a sensor node, whereby a relatively long sequence of sensor samples needs to be considered at a time. Using fast feature extraction from streaming data that can be implemented on the sensor nodes, we show that on-sensor event classification can be achieved. This approach is of particular interest for wireless sensor networks as it promises to reduce wireless traffic significantly, as only events need to be transmitted instead of potentially large chunks of inertial data. The presented approach characterizes the essence of an event's signal by combining several simple features on low-cost MEMS inertial data. Using a scenario and real data from vibration signatures generated by passing trains, we show how with this approach the classification of passing trains is possible on miniature nodes placed near the railroad tracks. Experiments show that, at the cost of slightly more local processing, the chosen features produce good train type classification with up to 90% of trains correctly identified.