Universal distributed sensing via random projections

Authors:
Marco F. Duarte;Michael B. Wakin;Dror Baron;Richard G. Baraniuk
Affiliations:
Rice University, Houston, TX;Rice University, Houston, TX;Rice University, Houston, TX;Rice University, Houston, TX
Venue:
Proceedings of the 5th international conference on Information processing in sensor networks
Year:
2006

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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 inter-sensor collaboration. We apply our framework to several real world datasets to validate the framework.