Universal distributed sensing via random projections

Marco F. Duarte, Michael B. Wakin, Dror Baron, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

111 Scopus citations

Abstract

This paper develops a new framework for distributed coding and compression in sensor networks based on distributed compressed sensing (DCS). DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity; just a few measurements of a jointly sparse signal ensemble contain enough information for reconstruction. DCS is well-suited for sensor network applications, thanks to its simplicity, universality, computational asymmetry, tolerance to quantization and noise, robustness to measurement loss, and scalability. It also requires absolutely no intersensor collaboration. We apply our framework to several real world datasets to validate the framework.

Original languageEnglish (US)
Title of host publicationProceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06
Pages177-185
Number of pages9
DOIs
StatePublished - 2006
EventFifth International Conference on Information Processing in Sensor Networks, IPSN '06 - Nashville, TN, United States
Duration: Apr 19 2006Apr 21 2006

Publication series

NameProceedings of the Fifth International Conference on Information Processing in Sensor Networks, IPSN '06
Volume2006

Other

OtherFifth International Conference on Information Processing in Sensor Networks, IPSN '06
CountryUnited States
CityNashville, TN
Period4/19/064/21/06

Keywords

  • Compressed sensing
  • Correlation
  • Greedy algorithms
  • Linear programming
  • Sensor networks
  • Sparsity

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing
  • Electrical and Electronic Engineering

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