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Proceedings of the 3rd international symposium on Information processing in sensor networks
Matched source-channel communication for field estimation in wireless sensor networks
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Source-channel communication in sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Signal Reconstruction From Noisy Random Projections
IEEE Transactions on Information Theory
Estimating inhomogeneous fields using wireless sensor networks
IEEE Journal on Selected Areas in Communications
Power, spatio-temporal bandwidth, and distortion in large sensor networks
IEEE Journal on Selected Areas in Communications
Distributed sparse random projections for refinable approximation
Proceedings of the 6th international conference on Information processing in sensor networks
Information fusion for wireless sensor networks: Methods, models, and classifications
ACM Computing Surveys (CSUR)
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ACM Transactions on Sensor Networks (TOSN)
Compressive data gathering for large-scale wireless sensor networks
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IEEE Transactions on Signal Processing
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Journal of Visual Communication and Image Representation
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Ultra-low power compressive wireless sensing for distributed wireless networks
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
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IEEE Transactions on Signal Processing
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Efficient measurement generation and pervasive sparsity for compressive data gathering
IEEE Transactions on Wireless Communications
Generalized reconstruction algorithm for compressed sensing
Computers and Electrical Engineering
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EWSN'10 Proceedings of the 7th European conference on Wireless Sensor Networks
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Wireless Communications & Mobile Computing
Compression in wireless sensor networks: A survey and comparative evaluation
ACM Transactions on Sensor Networks (TOSN)
Light-weight Online Predictive Data Aggregation for Wireless Sensor Networks
Proceedings of Workshop on Machine Learning for Sensory Data Analysis
Nesterov's algorithm solving dual formulation for compressed sensing
Journal of Computational and Applied Mathematics
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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.