Continuous Monitoring of Train Parameters Using IoT Sensor and Edge Computing

The speed and the overall loading of a train are of great importance in determining whether the entire train is running in a safe and stable manner. We present here our work in developing a wireless rail monitoring system using IoT (Internet-of-Things) sensors and edge computing to provide accurate real-time running state parameters of trains continuously over time. By placing a wireless MEMS-based sensing system (overall size of 50 × 50 × 17 mm) on a rail to collect the vibration information of the rail under excitation by the wheels of passing trains, and by using edge computing technique to analyze these captured vibration data, we calculated the state parameters of the vehicle with the microprocessor integrated with the MEMS sensors. Then, the final analysis results were uploaded to a cloud server via Narrow Band Internet-of-Things (NB-IoT) networks. The entire system consists of a solar power module, a cloud server, and an IoT sensor. Compared with traditional train state detection systems (i.e., computer vision or IR detectors) this system has the advantages of low cost, self-powering, and no line occupation. We have demonstrated that 24-hour real-time wireless monitoring without occupation of track resources is feasible using this system, which greatly improves the efficiency and quality of railway track detection. We conducted field experiments on the Datong-Qinhuangdao Railway line using our system. Experimental results showed that the absolute error in speed is 0.2 Km/h and no error in carriages quantity detection. It is expected to play a key role in the real-time monitoring of railways and trains in the future.