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Compressive wireless sensing

Compressive wireless sensing Compressive Wireless Sensing Waheed Bajwa, Jarvis Haupt, Akbar Sayeed, and Robert Nowak Department of Electrical and Computer Engineering University of Wisconsin-Madison — [email protected], [email protected], [email protected], [email protected] ABSTRACT Compressive Sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of Compressive Wireless Sensing for sensor networks in which a fusion center retrieves signal eld information from an ensemble of spatially distributed sensor nodes. Energy and bandwidth are scarce resources in sensor networks and the relevant metrics of interest in our context are 1) the latency involved in information retrieval; and 2) the associated power-distortion trade-o €. It is generally recognized that given su ƒcient prior knowledge about the sensed data (e.g., statistical characterization, homogeneity etc.), there exist schemes that have very favorable power-distortion-latency trade-o €s. We propose a distributed matched source-channel communication scheme, based in part on recent results in compressive sampling theory, for estimation of sensed data at the fusion center and analyze, as a function of number of sensor nodes, the trade-o €s between power, distortion and latency. Compressive wireless sensing http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Compressive wireless sensing

Bajwa, Waheed; Haupt, Jarvis; Sayeed, Akbar; Nowak, Robert
Association for Computing Machinery — Apr 19, 2006
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References (14)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2006 by ACM Inc.
ISBN
1-59593-334-4
doi
10.1145/1127777.1127801
Publisher site
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Abstract

Compressive Wireless Sensing Waheed Bajwa, Jarvis Haupt, Akbar Sayeed, and Robert Nowak Department of Electrical and Computer Engineering University of Wisconsin-Madison — [email protected], [email protected], [email protected], [email protected] ABSTRACT Compressive Sampling is an emerging theory that is based on the fact that a relatively small number of random projections of a signal can contain most of its salient information. In this paper, we introduce the concept of Compressive Wireless Sensing for sensor networks in which a fusion center retrieves signal eld information from an ensemble of spatially distributed sensor nodes. Energy and bandwidth are scarce resources in sensor networks and the relevant metrics of interest in our context are 1) the latency involved in information retrieval; and 2) the associated power-distortion trade-o €. It is generally recognized that given su ƒcient prior knowledge about the sensed data (e.g., statistical characterization, homogeneity etc.), there exist schemes that have very favorable power-distortion-latency trade-o €s. We propose a distributed matched source-channel communication scheme, based in part on recent results in compressive sampling theory, for estimation of sensed data at the fusion center and analyze, as a function of number of sensor nodes, the trade-o €s between power, distortion and latency. Compressive wireless sensing

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