An Intelligent Wireless Displacement Sensor for Landslide Monitoring and Early Warning

Abstract

Landslide poses a great threat to the safety of local communities, engineering construction and operation. Accurate monitoring and successful early-warning of rapid landslides using conventional techniques with constant and sparse sampling strategy remains a challenge in geo-disaster research and mitigation. Sampling and uploading all sensor data to cloud imposes huge burden on the power supply and radio bandwidth, which greatly reduces the long-term stability and robustness of the wireless sensor in field. With aim to achieve the best powerful sensing ability based on limited power and bandwidth of sensor, we proposed a low-cost and energy efficient wireless crack-meter sensor integrated with intelligent computing architecture for rapid deformation monitoring. A self-adaptive data sampling and uploading algorithm based on the variation of objective physical value was implemented in the embedded microcontroller of sensor node, and only the valuable information derived from sensor data to the cloud server. Experimental results demonstrated that the intelligent sensing approach can effectively collect the crucial data at a high frequency during accelerating deformation of landslide, while only update status at several hours during non-deformation phase. Several landslide monitoring applications have proved that this intelligent sensing can significantly and effectively enhance the success rate and real-time performance of early-warning for rapid landslide.