Model-based Data Aggregation for Structural Monitoring Employing Smart Sensors

Smart sensors densely distributed over structures can provide rich information for structural monitoring using their computational and wireless communication capabilities. One key issue in such monitoring is data aggregation. The sensors are typically sampled at high frequencies, producing large amounts of data; limited network resources (e.g., battery power, storage space, bandwidth, etc.) make acquiring and processing this data quite challenging. Efficient data aggregation with data compression is needed to achieve scalable sensor networks for structural monitoring. Model-based data aggregation is proposed using both structural and network analyses. A structural analysis algorithm, the Natural Excitation Technique, motivates adaptation of correlation function estimation to smart sensor networks. The data size is reduced by a factor of 20 to 40, depending on the degree of averaging in the aggregation. This averaging also addresses the wireless communication data loss problem. The algorithm is implemented on Mica2s and experimentally validated using a scale-model building. Keywordscoordinated computing; data compression; distributed algorithm; smart sensors

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