Interactive sensor network data retrieval and management using principal components analysis transform

With the growing use of large-scale sensor networks, huge volumes of sensor data are being generated from structural health monitoring systems. Vibration sensor data often constitute a large portion of the monitoring data from a structural health monitoring system. Efficient transmission and management of large-size vibration sensor datasets are becoming an increasingly important aspect of structural health monitoring systems. To address this problem of emerging importance, this paper presents a novel method for interactive retrieval and management of sensor network data. Pre-defined features obtained from principal components analysis (PCA) are proposed for the detection of changes in the monitored structure. The PCA transform and linear predictor are also used in the data compression scheme to allow users to retrieve data progressively with significantly reduced data size. The results of a case study involving wireless sensor network data collected from a five-story model building are presented to demonstrate the potential use of the proposed method in the transmission and management of sensor network data. The proposed method is believed to provide data users with the flexibility to select data and retrieve data at multi-resolution levels, reducing raw data size, relaxing the communication bandwidth requirement, and speeding up the data transmission process.