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Sensors 2015, 15(4), 8131-8145; doi:10.3390/s150408131

Decentralized System Identification Using Stochastic Subspace Identification for Wireless Sensor Networks

1
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 689-798, Korea
2
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard Reindl
Received: 5 February 2015 / Revised: 6 March 2015 / Accepted: 31 March 2015 / Published: 8 April 2015
(This article belongs to the Section Sensor Networks)
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Abstract

Wireless sensor networks (WSNs) facilitate a new paradigm to structural identification and monitoring for civil infrastructure. Conventional structural monitoring systems based on wired sensors and centralized data acquisition systems are costly for installation as well as maintenance. WSNs have emerged as a technology that can overcome such difficulties, making deployment of a dense array of sensors on large civil structures both feasible and economical. However, as opposed to wired sensor networks in which centralized data acquisition and processing is common practice, WSNs require decentralized computing algorithms to reduce data transmission due to the limitation associated with wireless communication. In this paper, the stochastic subspace identification (SSI) technique is selected for system identification, and SSI-based decentralized system identification (SDSI) is proposed to be implemented in a WSN composed of Imote2 wireless sensors that measure acceleration. The SDSI is tightly scheduled in the hierarchical WSN, and its performance is experimentally verified in a laboratory test using a 5-story shear building model. View Full-Text
Keywords: wireless sensor; structural health monitoring; decentralized processing; system identification; stochastic subspace identification wireless sensor; structural health monitoring; decentralized processing; system identification; stochastic subspace identification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cho, S.; Park, J.-W.; Sim, S.-H. Decentralized System Identification Using Stochastic Subspace Identification for Wireless Sensor Networks. Sensors 2015, 15, 8131-8145.

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