|
Title:
|
Universal Distributed Sensing via Random Projections |
|
Author:
|
Wakin, Michael; Duarte, Marco F.; Baraniuk, Richard G.; Baron, Dror
|
|
Type:
|
Conference Paper |
|
Keywords:
|
linear programming; compressed sensing; Sparsity; greedy algorithms; sensor networks; correlation |
|
Citation:
|
M. Wakin, M. F. Duarte, R. G. Baraniuk and D. Baron,"Universal Distributed Sensing via Random Projections," in International Symposium on Integrated Processing in Sensor Networks,, pp. 177-185. |
|
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. |
|
Date Published:
|
2006-04-01 |
