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8 April 2008 Distributed data processing within dense networks of wireless sensors using parallelized model updating techniques
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Abstract
As costs associated with wireless sensing technologies continue to decline, it has become feasible to deploy dense networks of tens, if not hundreds of wireless sensors within a single structural system. Additionally, many state-of-the-art wireless sensing platforms now integrate low-power microprocessors and high-precision analog-to-digital converters in their designs. As a result, data processing tasks can be efficiently distributed across large networks of wireless sensors. In this study, a parallelized model updating algorithm is designed for implementation within a network of wireless sensing prototypes. Using a novel parallel simulated annealing search method optimized for in-network execution, this algorithm efficiently assigns model parameters so as to minimize differences between an analytical model of the structure and wirelessly collected sensor data. Validation of this approach is provided by updating a lumped-mass shear structure model of a six-story steel building exposed to seismic base motion.
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Andrew T. Zimmerman and Jerome P. Lynch "Distributed data processing within dense networks of wireless sensors using parallelized model updating techniques", Proc. SPIE 6934, Nondestructive Characterization for Composite Materials, Aerospace Engineering, Civil Infrastructure, and Homeland Security 2008, 69340V (8 April 2008); https://doi.org/10.1117/12.776470
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