Title: Data recovery via hybrid sensor networks for vibration monitoring of civil structures

Authors: Roman Klis; Eleni N. Chatzi

Addresses: Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Switzerland ' Department of Civil, Environmental and Geomatic Engineering, ETH Zürich, Switzerland

Abstract: Sensor networks are increasingly used for acquiring data on the basis of which other mathematical tools, such as system identification methods, may infer valuable information regarding the monitored object. This is especially important within the context of monitoring for civil structures, where minor variations in environmental and operational conditions bear a pronounced effect on structural response. In order to accurately monitor engineered systems these effects should be discernible in the acquired response, which inevitably mandates the transmission of large volumes of data. Such a task is oftentimes prohibitive for the case of wireless sensor networks (WSNs), a solution which enjoys an increasing share of popularity for the monitoring of large-scale infrastructure due to its low cost and ease of deployment. The work presented herein proposes an amendment to this shortcoming by merging the herein introduced concept of a leading node with a recently surfaced compressive sensing paradigm, relying on robust signal reconstruction techniques. The concept builds on the fact that fusion of a minimal number of tethered sensors with wireless nodes in a so-called hybrid sensor network, improves the information content of the transmitted data. The latter allows for dense, yet low-cost, and sufficiently accurate deployments featuring wireless nodes. To this end, this work outlines, in a step-by-step process, the separate stages for time-series recovery from the partially transmitting nodes of the WSN.

Keywords: l1 norm optimisation; compressive sensing; NESTA algorithm; leading node; wireless sensors; hybrid sensor networks; HSNs; structural health monitoring; SHM; large-scale civil structures; data recovery; vibration monitoring; signal reconstruction; sensor fusion.

DOI: 10.1504/IJSMSS.2015.078373

International Journal of Sustainable Materials and Structural Systems, 2015 Vol.2 No.1/2, pp.161 - 184

Received: 17 Nov 2015
Accepted: 21 Apr 2016

Published online: 15 Aug 2016 *

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