Embedded Data Processing in Wireless Sensor Networks for Structural Health Monitoring

Abstract

Eight wireless accelerometer sensor nodes (Imote2) equipped with energy harvesting solar panels were deployed continuously on an operational pedestrian footbridge in Singapore for two weeks. Each node periodically processed vibration data using a novel embedded data processing algorithm, referred to as the Filtered Hilbert-Huang transform, which resulted in a data reduction of 96%. From the processed results which the nodes transmitted to the base station, it was possible to conclude that resonant response from pedestrian walking excitation led to increased vibration levels during peak usage times. The maximum recorded peak and RMS acceleration were 52mg and 35mg respectively, which are within the limits allowed by several major design guidelines. This wireless sensor network deployment demonstrated the potential of decentralised, embedded data processing for wireless medium- and long-term structural health monitoring of civil infrastructure.