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