An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameInformation Processing in Sensor Networksen_US
dc.citation.firstpage243
dc.citation.lastpage250
dc.citation.locationNashville, TNen_US
dc.contributor.authorWagner, Raymonden_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.authorDu, Shuen_US
dc.contributor.authorJohnson, David B.en_US
dc.contributor.authorCohen, Alberten_US
dc.date.accessioned2007-10-31T01:08:30Z
dc.date.available2007-10-31T01:08:30Z
dc.date.issued2006-04-01en
dc.date.modified2006-06-26en_US
dc.date.note2006-04-26en_US
dc.date.submitted2006-04-01en_US
dc.descriptionConference paperen_US
dc.description.abstractDistributed 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.en_US
dc.description.sponsorshipTexas Instrumentsen_US
dc.description.sponsorshipOffice of Naval Researchen_US
dc.description.sponsorshipNational Science Foundationen_US
dc.description.sponsorshipAir Force Office of Scientific Researchen_US
dc.identifier.citationR. Wagner, R. G. Baraniuk, S. Du, D. B. Johnson and A. Cohen, "An Architecture for Distributed Wavelet Analysis and Processing in Sensor Networks," 2006.
dc.identifier.urihttps://hdl.handle.net/1911/20422
dc.language.isoeng
dc.relation.projecthttp://compass.cs.rice.eduen_US
dc.relation.softwarehttp://www.ece.rice.edu/~rwagner/pubs.htmlen_US
dc.subjectdistributed wavelet analysis*
dc.subjectirregular grid wavelet analysis*
dc.subjectsensor networks*
dc.subjectcompression*
dc.subjectmultiscale analysis*
dc.subject.keyworddistributed wavelet analysisen_US
dc.subject.keywordirregular grid wavelet analysisen_US
dc.subject.keywordsensor networksen_US
dc.subject.keywordcompressionen_US
dc.subject.keywordmultiscale analysisen_US
dc.titleAn Architecture for Distributed Wavelet Analysis and Processing in Sensor Networksen_US
dc.typeConference paper
dc.type.dcmiText
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