
Proceedings Paper
Recovery of lost data for wireless sensor network used in structural health monitoringFormat | Member Price | Non-Member Price |
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Paper Abstract
In a wireless sensor network, data loss often occurs during the data transmission between wireless sensor nodes and the
base station, which decreases the communication reliability in wireless sensor network applications. Errors caused by
data loss inevitably affect the data analysis of the structure and subsequent decision making. This paper proposed an
approach to recover lost data in a wireless sensor network based on the compressive sampling (CS) technique. The main
idea in this approach is to project the transmitted data from x onto y, where y is the linear projection of x on a random
matrix. The data vector y is permitted to lose part of the original data x in wireless transmissions between the sensor
nodes and the base station. After the base station receives the imperfect data, the original data vector x can be
reconstructed based on the data y using the CS method. The acceleration data collected from the vibration test of
Shandong Harbin Sifangtai Bridge by wireless sensors is used to analyze the data loss recovery ability of the proposed
method.
Paper Details
Date Published: 20 April 2012
PDF: 10 pages
Proc. SPIE 8348, Health Monitoring of Structural and Biological Systems 2012, 83482C (20 April 2012); doi: 10.1117/12.915830
Published in SPIE Proceedings Vol. 8348:
Health Monitoring of Structural and Biological Systems 2012
Tribikram Kundu, Editor(s)
PDF: 10 pages
Proc. SPIE 8348, Health Monitoring of Structural and Biological Systems 2012, 83482C (20 April 2012); doi: 10.1117/12.915830
Show Author Affiliations
Xiaodan Sun, Harbin Engineering Univ. (China)
Jinping Ou, Harbin Institute of Technology (China)
Dalian Univ. of Technology (China)
Jinping Ou, Harbin Institute of Technology (China)
Dalian Univ. of Technology (China)
Published in SPIE Proceedings Vol. 8348:
Health Monitoring of Structural and Biological Systems 2012
Tribikram Kundu, Editor(s)
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