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Sensors 2016, 16(6), 792; doi:10.3390/s16060792

Probabilistic Assessment of High-Throughput Wireless Sensor Networks

1
Fire Research Center, Korea Institute of Civil Engineering and Building Technology, Gyeonggi-do 18544, Korea
2
Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA
3
School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Korea
4
Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA
5
Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Academic Editors: Lyudmila Mihaylova, Byung-Gyu Kim and Debi Prosad Dogra
Received: 6 April 2016 / Revised: 22 May 2016 / Accepted: 26 May 2016 / Published: 31 May 2016
(This article belongs to the Special Issue Scalable Localization in Wireless Sensor Networks)
View Full-Text   |   Download PDF [4285 KB, uploaded 31 May 2016]   |  

Abstract

Structural health monitoring (SHM) using wireless smart sensors (WSS) has the potential to provide rich information on the state of a structure. However, because of their distributed nature, maintaining highly robust and reliable networks can be challenging. Assessing WSS network communication quality before and after finalizing a deployment is critical to achieve a successful WSS network for SHM purposes. Early studies on WSS network reliability mostly used temporal signal indicators, composed of a smaller number of packets, to assess the network reliability. However, because the WSS networks for SHM purpose often require high data throughput, i.e., a larger number of packets are delivered within the communication, such an approach is not sufficient. Instead, in this study, a model that can assess, probabilistically, the long-term performance of the network is proposed. The proposed model is based on readily-available measured data sets that represent communication quality during high-throughput data transfer. Then, an empirical limit-state function is determined, which is further used to estimate the probability of network communication failure. Monte Carlo simulation is adopted in this paper and applied to a small and a full-bridge wireless networks. By performing the proposed analysis in complex sensor networks, an optimized sensor topology can be achieved. View Full-Text
Keywords: wireless sensor networks; network communication reliability; probabilistic assessment; high-throughput data transfer; structural health monitoring wireless sensor networks; network communication reliability; probabilistic assessment; high-throughput data transfer; structural health monitoring
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

Kim, R.E.; Mechitov, K.; Sim, S.-H.; Spencer, B.F.; Song, J. Probabilistic Assessment of High-Throughput Wireless Sensor Networks. Sensors 2016, 16, 792.

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