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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Published in:IPSN 2006 : the Fifth International Conference on Information Processing in Sensor Networks : April 19-21, 2006, Nashville, Tennessee, USA pp. 134 - 142
Main Authors: Bajwa, Waheed, Haupt, Jarvis, Sayeed, Akbar, Nowak, Robert
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 19.04.2006
IEEE
Series:ACM Conferences
Subjects:
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
uncoded communications
compressive sampling
wireless sensor networks
ISBN:9781595933348, 1595933344
Online Access:Get full text
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Summary: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.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9781595933348
1595933344
DOI:10.1145/1127777.1127801