Joint Source–Channel Communication for Distributed Estimation in Sensor Networks

Bajwa, Waheed U.; Haupt, Jarvis; Sayeed, A.M.; Nowak, Robert

doi:10.1109/tit.2007.904835

Cited by 147 publications

(121 citation statements)

References 34 publications

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“…One of the earliest studies that exploit CS in a distributed communication scheme is [9], which targets the energy efficient estimation of sensed data in a WSN. Multi-hop communication and in-network data processing are not considered.…”

Section: Introductionmentioning

confidence: 99%

Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

Masiero

Quer

Munaretto

et al. 2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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Abstract-In this paper we look at the problem of accurately reconstructing distributed signals through the collection of a small number of samples at a data gathering point. The techniques that we exploit to do so are Compressive Sensing (CS) and Principal Component Analysis (PCA). PCA is used to find transformations that sparsify the signal, which are required for CS to retrieve, with good approximation, the original signal from a small number of samples. Our approach dynamically adapts to non-stationary real world signals through the online estimation of their correlation properties in space and time; these are then exploited by PCA to derive the transformations for CS. The approach is tunable and robust, independent of the specific routing protocol in use and able to substantially outperform standard data collection schemes. The effectiveness of our recovery algorithm, in terms of number of transmissions in the network vs reconstruction error, is demonstrated for synthetic as well as for real world signals which we gathered from an actual wireless sensor network (WSN) deployment. We stress that our solution is not limited to WSNs but it can be readily applied to other types of network infrastructures that require the online approximation of large and distributed data sets.

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Section: Introductionmentioning

confidence: 99%

Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

Masiero

Quer

Munaretto

et al. 2009

GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference

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“…The sensor nodes collect observations of a physical phenomenon and collaborate with each other to perform a certain signal processing task, e.g., localization, detection or estimation of certain signals or parameters. Some approaches require a socalled fusion center (e.g., [1]- [6]) that gathers all the sensor signals, whereas other algorithms are distributed such that all processing happens inside the network (e.g., [7]- [23]). The latter is usually preferred, especially when it is scalable in terms of communication bandwidth and processing power.…”

Section: A Distributed Signal Estimation In Wireless Sensor Networkmentioning

confidence: 99%

“…The goal is now to achieve the centralized LCMV beamformer (6) and to generate observations of the corresponding beamformer outputd =ŵ H y at each node, without letting each node broadcast all M k channels of the multi-channel (vector) signal y k . Instead, each node k will only transmit observations of a single-channel (scalar) signal z k which is a linear combination of its sensor signals, i.e., z k = r H k y k where r k is a fusion vector.…”

Section: A Algorithm Descriptionmentioning

confidence: 99%

Distributed LCMV Beamforming in a Wireless Sensor Network With Single-Channel Per-Node Signal Transmission

Bertrand

Moonen

2013

IEEE Trans. Signal Process.

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Abstract-Linearly constrained minimum variance (LCMV) beamforming is a popular spatial filtering technique for signal estimation or signal enhancement in many different fields. We consider distributed LCMV (D-LCMV) beamforming in wireless sensor networks (WSNs) with either a fully connected or a tree topology. In the D-LCMV beamformer algorithm, each node fuses its multiple sensor signals into a single-channel signal of which observations are then transmitted to other nodes. We envisage an adaptive/time-recursive implementation where each node adapts its local LCMV beamformer to changes in the local sensor signal statistics, as well as to changes in the statistics of the wirelessly received signals. Although the per-node signal transmission and computational power is greatly reduced compared to a centralized realization, we show that it is possible for each node to generate the centralized LCMV beamformer output as if it had access to all sensor signals in the entire network, without an explicit computation of the network-wide sensor signal covariance matrix. We provide sufficient conditions for convergence and optimality of the D-LCMV beamformer. The theoretical results are validated by means of Monte-Carlo simulations, which demonstrate the performance of the D-LCMV beamformer.

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“…Some work to date has answered this question in the affirmative. For example, if the entries of an unknown vector x ∈ R N are spread among a field of sensors (e.g., if x represents a concatenation of the ambient temperatures recorded by N sensors at a single instant), then certain protocols have been proposed for efficiently computing y = Φx through proper coordination of the sensors [8][9][10][11]. Given y, standard CS recovery schemes can then be used to recover x using a model for its sparse structure.…”

Section: Introductionmentioning

confidence: 99%

Measurement Bounds for Sparse Signal Ensembles via Graphical Models

Duarte

Wakin

Baron

et al. 2013

IEEE Trans. Inform. Theory

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Abstract-In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing (DCS) extends this framework by defining ensemble sparsity models, allowing a correlated ensemble of sparse signals to be jointly recovered from a collection of separately acquired compressive measurements. In this paper, we introduce a framework for modeling sparse signal ensembles that quantifies the intra-and inter-signal dependencies within and among the signals. This framework is based on a novel bipartite graph representation that links the sparse signal coefficients with the measurements obtained for each signal. Using our framework, we provide fundamental bounds on the number of noiseless measurements that each sensor must collect to ensure that the signals are jointly recoverable.

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Joint Source–Channel Communication for Distributed Estimation in Sensor Networks

Cited by 147 publications

References 34 publications

Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

Data Acquisition through Joint Compressive Sensing and Principal Component Analysis

Distributed LCMV Beamforming in a Wireless Sensor Network With Single-Channel Per-Node Signal Transmission

Measurement Bounds for Sparse Signal Ensembles via Graphical Models

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