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Proceedings Paper

Distributed signal decorrelation in wireless sensor networks using the sparse matrix transform
Author(s): Leonardo R. Bachega; Srikanth Hariharan; Charles A. Bouman; Ness Shroff
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Paper Abstract

In this paper, we propose the vector SMT, a new decorrelating transform suitable for performing distributed anomaly detection in wireless sensor networks (WSN). Here, we assume that each sensor in the network performs vector measurements, instead of a scalar ones. The proposed transform decorrelates a sequence of pairs of vector sensor measurements, until the vectors from all sensors are completely decorrelated. We perform simulations with a network of cameras, where each camera records an image of the monitored environment from its particular viewpoint. Results show that the proposed transform effectively decorrelates image measurements from the multiple cameras in the network. Because it enables joint processing of the multiple images, our method provides significant improvements to anomaly detection accuracy when compared to the baseline case when we process the images independently.

Paper Details

Date Published: 3 June 2011
PDF: 15 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 80580V (3 June 2011); doi: 10.1117/12.887549
Show Author Affiliations
Leonardo R. Bachega, Purdue Univ. (United States)
Srikanth Hariharan, The Ohio State Univ. (United States)
Charles A. Bouman, Purdue Univ. (United States)
Ness Shroff, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

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