Linear Predictor-Based Lossless Compression of Vibration Sensor Data: Systems Approach
Publication: Journal of Engineering Mechanics
Volume 133, Issue 4
Abstract
This paper presents a novel systems approach to compressing sensor network data. Unlike previous data compression methods, the proposed lossless linear predictor-based sensor data compression method utilizes structural system information to minimize the signal correlation in sensor network data. In the proposed method, linear predictor is derived in a system identification framework in which auto-regressive (AR) model is used as its model structure and the instrumental variables (IV) method is used to calculate the predictor parameters. A parametric study was carried out to study the effects of changes in system property, number of sensors, and sensor noise level on the compression performance of the proposed method. Both numerical simulation and experimental results show that the proposed sensor data compression method has a better compression performance than conventional linear predictor-based data compression method for single sensor.
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Acknowledgments
The research work described in this paper was partially supported by the National Science Foundation under Grant No. CMS-0546963 (Program Director: S.-C. Liu). The authors are also grateful to the Pa. Infrastructure Technology Alliance and Lehigh Univ. for providing additional financial support for this research. However, the findings, opinions, and conclusions expressed in this paper are solely those of the writers and do not necessarily reflect the views of the sponsors.
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Information & Authors
Information
Published In
Journal of Engineering Mechanics
Volume 133 • Issue 4 • April 2007
Pages: 431 - 441
Copyright
© 2007 ASCE.
History
Received: Mar 8, 2005
Accepted: Aug 2, 2006
Published online: Apr 1, 2007
Published in print: Apr 2007
Notes
Note. Associate Editor: Lambros S. Katafygiotis
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