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An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks

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dc.contributor.author Wagner, Raymond
Baraniuk, Richard G.
Du, Shu
Johnson, David B.
Cohen, Albert
dc.creator Wagner, Raymond
Baraniuk, Richard G.
Du, Shu
Johnson, David B.
Cohen, Albert
dc.date.accessioned 2007-10-31T01:08:30Z
dc.date.available 2007-10-31T01:08:30Z
dc.date.issued 2006-04-01
dc.date.submitted 2006-04-01
dc.identifier.uri http://hdl.handle.net/1911/20422
dc.description Conference Paper
dc.description.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.
dc.description.sponsorship Texas Instruments
dc.description.sponsorship Office of Naval Research
dc.description.sponsorship National Science Foundation
dc.description.sponsorship Air Force Office of Scientific Research
dc.language.iso eng
dc.subject distributed wavelet analysis
irregular grid wavelet analysis
sensor networks
compression
multiscale analysis
dc.title An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks
dc.type Conference Paper
dc.date.note 2006-04-26
dc.citation.bibtexName inproceedings
dc.date.modified 2006-06-26
dc.subject.keyword distributed wavelet analysis
irregular grid wavelet analysis
sensor networks
compression
multiscale analysis
dc.citation.pageNumber 243-250
dc.citation.location Nashville, TN
dc.relation.project http://compass.cs.rice.edu
dc.relation.software http://www.ece.rice.edu/~rwagner/pubs.html
dc.citation.conferenceName Information Processing in Sensor Networks
dc.type.dcmi Text
dc.identifier.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.

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  • ECE Publications [1047 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.