A Novel Data Aggregation in Wireless Smart Sensors for Structure Monitoring

Structural monitoring is the collection and analysis of structural response to ambient or forced excitation. One key issue in such monitoring is data aggregation. The sensors are typically sampled at high frequencies, producing large amounts of data; limietd network resources make acquiring and processing this data quite challenging. 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.