Distributed Remote Vector Gaussian Source Coding for Wireless Acoustic Sensor Networks

Zahedi, Adel; Østergaard, Jan; Jensen, Søren Holdt; Naylor, Patrick A.; Bech, Søren

doi:10.1109/dcc.2014.27

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…In particular, we assume that the additive noise terms n i , i = 1, ..., N in the measurements are mutually uncorrelated. 3 We further assume that the distortions D i , i = 1, ..., N are in the interval 0 ≺ D i Σ y i , so the rate-distortion functions are given by (12). The problem (21) can then be rewritten as: (18) and (19), we have Σ v = Σ n +Σ ν , and thus from the assumption that the additive noise terms at different nodes are mutually uncorrelated, we conclude that Σ v is block-diagonal, and from (17), the ith matrix on the diagonal is given by:…”

Section: B Rate-distortion Functionsmentioning

confidence: 99%

Coding and Enhancement in Wireless Acoustic Sensor Networks

Zahedi

Østergaard

Jensen

et al. 2015

2015 Data Compression Conference

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We formulate a new problem which bridges between source coding and enhancement in wireless acoustic sensor networks. We consider a network of wireless microphones, each of which encoding its own measurement under a covariance matrix distortion constraint and sending it to a fusion center. To process the data at the center, we use a recent spatio-temporal prediction filter. We assume that a weighted sum-rate for the network is specified. The problem is to allocate optimal distortion matrices to the nodes in order to achieve a maximum output SNR at the fusion center after processing the received data, while the weighted sum-rate for the network is no more than the specified value. We formulate this problem as an optimization problem for which we derive a set of equalities imposed on the solution by studying the KKT conditions. In particular, for the special case of scalar sources with two microphones and a sum-rate constraint, we derive the distortion allocation in closed form and will show that if the given sum-rate is higher than a critical value, the stationary points from the KKT conditions lead to distortion allocations which maximize the output SNR of the filter. Data Compression Conference

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Section: B Rate-distortion Functionsmentioning

confidence: 99%

Coding and Enhancement in Wireless Acoustic Sensor Networks

Zahedi

Østergaard

Jensen

et al. 2015

2015 Data Compression Conference

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“…where A, B, C, G and Γ are the coefficients of linear estimation, depending only on the covariance and cross-covariance matrices of x, y, z and u, and n i , i = 1, 2, 3 are estimation errors with covariance matrices Σ n1 , Σ n2 = Σ x|yz , and Σ n3 = Σ y|z , respectively. (See the Appendix in [4] for more details.)…”

Section: A Notation and Problem Statementmentioning

confidence: 99%

“…In [4], the RDF for (4) was recently found for the somewhat restrictive case where n x = n y = n z , and C in (7) is invertible, and the distortion constraint satisfies Σ x|yz ≺ D Σ x|z . Under these assumptions, the RDF was shown to be:…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Distributed remote vector gaussian source coding with covariance distortion constraints

Zahedi¹,

Østergaard²,

Jensen³

et al. 2014

2014 IEEE International Symposium on Information Theory

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Abstract-In this paper, we consider a distributed remote source coding problem, where a sequence of observations of source vectors is available at the encoder. The problem is to specify the optimal rate for encoding the observations subject to a covariance matrix distortion constraint and in the presence of side information at the decoder. For this problem, we derive lower and upper bounds on the rate-distortion function (RDF) for the Gaussian case, which in general do not coincide. We then provide some cases, where the RDF can be derived exactly. We also show that previous results on specific instances of this problem can be generalized using our results. We finally show that if the distortion measure is the mean squared error, or if it is replaced by a certain mutual information constraint, the optimal rate can be derived from our main result.

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“…Due to the scale of mass data generated in IoT networks, it is difficult to continuously gather the original data from the network, since such collection usually requires considerable effort of communication and storage at intermediate nodes. A traditional way of solving this problem includes wavelet-based collaborative aggregation [1], cluster-based aggregation and compression [2,3], and distributed source coding [4,5]. All of them utilize the spatial correlation of device readings among device nodes.…”

Section: Introductionmentioning

confidence: 99%

MRCS: matrix recovery-based communication-efficient compressive sampling on temporal-spatial data of dynamic-scale sparsity in large-scale environmental IoT networks

Zhang

Shen

et al. 2019

J Wireless Com Network

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In the past few years, a large variety of IoT applications has been witnessed by fast proliferation of IoT devices (e.g., environment surveillance devices, wearable devices, city-wide NB-IoT devices). However, launching data collection from these mass IoT devices raises a challenge due to limited computation, storage, bandwidth, and energy support. Existing solutions either rely on traditional data gathering methods by relaying data from each node to the sink, which keep data unaltered but suffering from costly communication, or tackle the spacial data in a proper basis to compress effectively in order to reduce the magnitude of data to be collected, which implicitly assumes the sparsity of the data and inevitably may result in a poor data recovery on account of the risk of sparsity violation. Note that these data collection approaches focus on either the fidelity or the magnitude of data, which can solve either problem well but never both simultaneously. This paper presents a new attempt to tackle both problems at the same time from theoretical design to practical experiments and validate in real environmental datasets. Specifically, we exploit data correlation at both temporal and spatial domains, then provide a cross-domain basis to collect data and a low-rank matrix recovery design to recover the data. To evaluate our method, we conduct extensive experimental study with real datasets. The results indicate that the recovered data generally achieve SNR 10 times (10 db) better than compressive sensing method, while the communication cost is kept the same.

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Distributed Remote Vector Gaussian Source Coding for Wireless Acoustic Sensor Networks

Cited by 6 publications

References 10 publications

Coding and Enhancement in Wireless Acoustic Sensor Networks

Coding and Enhancement in Wireless Acoustic Sensor Networks

Distributed remote vector gaussian source coding with covariance distortion constraints

MRCS: matrix recovery-based communication-efficient compressive sampling on temporal-spatial data of dynamic-scale sparsity in large-scale environmental IoT networks

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