Compressive wireless sensing
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 retr...
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| Vydáno v: | IPSN 2006 : the Fifth International Conference on Information Processing in Sensor Networks : April 19-21, 2006, Nashville, Tennessee, USA s. 134 - 142 |
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| Jazyk: | angličtina |
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New York, NY, USA
ACM
19.04.2006
IEEE |
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| ISBN: | 9781595933348, 1595933344 |
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| 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 field 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-off. It is generally recognized that given sufficient prior knowledge about the sensed data (e.g., statistical characterization, homogeneity etc.), there exist schemes that have very favorable power-distortion-latency trade-offs. 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-offs between power, distortion and latency. Compressive wireless sensing is a universal scheme in the sense that it requires no prior knowledge about the sensed data. This universality, however, comes at the cost of optimality (in terms of a less favorable power-distortion-latency trade-off) and we quantify this cost relative to the case when sufficient prior information about the sensed data is assumed. |
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| AbstractList | 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 field 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-off. It is generally recognized that given sufficient prior knowledge about the sensed data (e.g., statistical characterization, homogeneity etc.), there exist schemes that have very favorable power-distortion-latency trade-offs. 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-offs between power, distortion and latency. Compressive wireless sensing is a universal scheme in the sense that it requires no prior knowledge about the sensed data. This universality, however, comes at the cost of optimality (in terms of a less favorable power-distortion-latency trade-off) and we quantify this cost relative to the case when sufficient prior information about the sensed data is assumed. |
| Author | Haupt, Jarvis Sayeed, Akbar Nowak, Robert Bajwa, Waheed |
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| Keywords | uncoded communications compressive sampling wireless sensor networks |
| Language | English |
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| PublicationTitle | IPSN 2006 : the Fifth International Conference on Information Processing in Sensor Networks : April 19-21, 2006, Nashville, Tennessee, USA |
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| Snippet | 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... 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... |
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| SubjectTerms | Bandwidth Character recognition Computing methodologies -- Modeling and simulation -- Simulation theory -- Systems theory Context Cost function Delay Information retrieval Information systems -- Data management systems -- Data structures -- Data layout -- Data compression Mathematics of computing -- Information theory Sampling methods Security and privacy -- Cryptography -- Mathematical foundations of cryptography Sensor fusion Sensor phenomena and characterization Theory of computation -- Computational complexity and cryptography -- Communication complexity Wireless sensor networks |
| Title | Compressive wireless sensing |
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