Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24972
Title: Distributed sensing for high-quality structural health monitoring using WSNs
Authors: Liu, X
Cao, J 
Song, WZ
Guo, P
He, Z
Keywords: Distributed algorithms
Structural health monitoring
Wireless sensor networks
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on parallel and distributed systems, 2014, v. 26, no. 3, 6776483, p. 738-747 How to cite?
Journal: IEEE transactions on parallel and distributed systems 
Abstract: Due to the low cost and ease of deployment, wireless sensor networks (WSNs) are emerging as sensing paradigms that the structural engineering field has begun to consider as substitutes for traditional tethered structural health monitoring (SHM) systems. Different from other applications of WSNs such as environmental monitoring, SHM applications are much more data intensive and it is not feasible to stream the raw data back to the server due to the severe bandwidth and energy limitations of low-power sensor networks. In-network processing is a promising approach to address this problem but designing distributed versions for the sophisticated SHM algorithms is much more challenging because SHM algorithms are computationally intensive, and involve data-level collaboration of multiple sensors. In this paper, we select a classical SHM algorithm: the eigen-system realization algorithm (ERA), and propose a few distributed ERAs suitable for WSNs. In particular, we first design a method to incrementally calculate the ERA and then propose three schemes upon which the incremental ERA can be carried out along an Hamiltonian path, along a path in the minimum connected dominating set (MCDS) and along the shortest path tree (SPT). The efficacy of these schemes are demonstrated and compared through both simulation experiment. We believe the proposed schemes can also serve as a guideline when applying WSNs for other applications like SHM which are also data-intensive and involve sophisticated signal processing of collected information.
URI: http://hdl.handle.net/10397/24972
ISSN: 1045-9219 (print)
1558-2183 (online)
DOI: 10.1109/TPDS.2014.2312911
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