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An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks

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Title: An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks
Author: Wagner, Raymond; Baraniuk, Richard G.; Du, Shu; Johnson, David B.; Cohen, Albert
Type: Conference Paper
Keywords: distributed wavelet analysis; irregular grid wavelet analysis; sensor networks; compression; multiscale analysis
Citation: R. Wagner, R. G. Baraniuk, S. Du, D. B. Johnson and A. Cohen,"An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks," in Information Processing in Sensor Networks,, pp. 243-250.
Abstract: Distributed wavelet processing within sensor networks holds promise for reducing communication energy and wireless bandwidth usage at sensor nodes. Local collaboration among nodes de-correlates measurements, yielding a sparser data set with significant values at far fewer nodes. Sparsity can then be leveraged for subsequent processing such as measurement compression, de-noising, and query routing. A number of factors complicate realizing such a transform in real-world deployments, including irregular spatial placement of nodes and a potentially prohibitive energy cost associated with calculating the transform in-network. In this paper, we address these concerns head-on; our contributions are fourfold. First, we propose a simple interpolatory wavelet transform for irregular sampling grids. Second, using ns-2 simulations of network traffic generated by the transform, we establish for a variety of network configurations break-even points in network size beyond which multiscale data processing provides energy savings. Distributed lossy compression of network measurements provides a representative application for this study. Third, we develop a new protocol for extracting approximations given only a vague notion of source statistics and analyze its energy savings over a more intuitive but naive approach. Finally, we extend the 2-dimensional (2-D) spatial irregular grid transform to a 3-D spatio-temporal transform, demonstrating the substantial gain of distributed 3-D compression over repeated 2-D compression.
Date Published: 2006-04-01

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  • ECE Publications [1082 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.