Universal distributed sensing via random projections
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...
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| creator | Duarte, Marco F. Wakin, Michael B. Baron, Dror Baraniuk, Richard G. |
| description | 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. |
| doi_str_mv | 10.1145/1127777.1127807 |
| format | Conference Proceeding |
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| identifier | ISBN: 9781595933348 |
| ispartof | 2006 5th International Conference on Information Processing in Sensor Networks, 2006, p.177-185 |
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| language | eng |
| recordid | cdi_acm_books_10_1145_1127777_1127807 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Applied computing -- Physical sciences and engineering -- Engineering Collaboration Compressed sensing Computer networks correlation Data engineering Design engineering Distributed control greedy algorithms Information systems -- Data management systems -- Data structures -- Data layout -- Data compression Intelligent sensors linear programming Loss measurement sensor networks Sensor phenomena and characterization Sparsity Wireless sensor networks |
| title | Universal distributed sensing via random projections |
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