Intersensor Collaboration in Distributed Quantization Networks

Sun, John Z.; Goyal, Vivek K

doi:10.1109/tcomm.2013.071813.120833

Cited by 12 publications

(5 citation statements)

References 29 publications

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“…In other words, the penalty in the performance of marginal reconstruction is negligible, when jointly designing the quantizers for two sources versus designing the quantizers for each source independently. The distortion-rate performance of the proposed distributed coding scheme, consisting of scalar quantizers and entropy coders, is compared against theoretically achievable performances given by (8). The comparison is carried out in the following manner.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, [7] demonstrates that for binary symmetric and additive white Gaussian noise channel the correlation between sources can be useful in reducing quantization distortion and protecting data when 2 International Journal of Distributed Sensor Networks transmitted over noisy channels. Discussion on distortion performance gain and quantizer design using intersensor communication is reported in [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Quantization for Robust Distributed Coding

Bais

Sarshar³

2016

International Journal of Distributed Sensor Networks

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A distributed source coding approach is proposed for robust data communications in sensor networks. When sensor measurements are quantized, possible correlations between the measurements can be exploited to reduce the overall rate of communication required to report these measurements. Robust distributed source coding (RDSC) approaches differentiate themselves from other works in that the reconstruction error of all sources will not exceed a given upper bound, even if only a subset of the multiple descriptions of the distributed source code are received. We deal with practical aspects of RDSC in the context of scalar quantization of two correlated sources. As a benchmark to evaluate the performance of the proposed scheme, we derive theoretically achievable distortion-rate performances of an RDSC for two jointly Gaussian sources by applying known results on the classical multiple description source coding.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantization for Robust Distributed Coding

Bais

Sarshar³

2016

International Journal of Distributed Sensor Networks

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“…Upon placing the problem of lossless source coding of analog sources in an information theoretic setting, with a probabilistic model for the source that need not be encoded linearly, Rényi dimension is known to determine fundamental performance limits [5] (see also [6,7]). Several recent studies consider the compressed sensing of a signal with an allowed detection error rate or quantization distortion [8,9]; of multiple signals followed by distributed quantization [10], including a study of scaling laws [11]; or of sub-Nyquist rate sampled signals followed by lossy reconstruction [12]; and rate distortion function for multiple sources with time-shared sampling [13].…”

Section: Prior Workmentioning

confidence: 99%

Sampling rate distortion

Boda

Narayan

2014

2014 IEEE International Symposium on Information Theory

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Consider a memoryless multiple source with m components of which a (possibly randomized) subset of k ≤ m components are sampled at each time instant and jointly compressed with the objective of reconstructing a prespecified subset of the m components under a given distortion criterion. The combined sampling and lossy compression mechanisms are to be designed to perform robustly with or without exact knowledge of the underlying joint probability distribution of the source. In this dissertation, we introduce a new framework of sampling rate distortion to study the tradeoffs among sampling mechanism, encoder-decoder structure, compression rate and the desired level of accuracy in the reconstruction.We begin with a discrete memoryless multiple source whose joint probability mass function (pmf) is taken to be known. A notion of sampling rate distortion function is introduced to study the mentioned tradeoffs, and is characterized first for fixed-set sampling. Next, for independent random sampling performed without the knowledge of the source outputs, it is shown that the sampling rate distortion function is the same whether or not the decoder is informed of the sequence of sampled sets. For memoryless random sampling, with the sampling depending on the source outputs, it is shown that deterministic sampling, characterized by a conditional point-mass, is optimal and suffices to achieve the sampling rate distortion function.Building on this, we consider a universal setting where the joint pmf of a discrete memoryless multiple source is known only to belong to a finite family of pmfs.In Bayesian and nonBayesian settings, single-letter characterizations are provided for the universal sampling rate distortion function for the fixed-set sampling, independent random sampling and memoryless random sampling. We show that these sampling mechanisms successively improve upon each other: (i) in their ability to enable an associated encoder approximate the underlying joint pmf and (ii) in their ability to choose appropriate subsets of the multiple source for compression by the encoder.Lastly, we consider a jointly Gaussian multiple memoryless source, to be reconstructed under a mean-squared error distortion criterion, with joint probability distribution function known only to belong to an uncountable family of probability density functions (characterized by a convex compact subset in Euclidean space).For fixed-set sampling, we characterize the universal sampling rate distortion function in Bayesian and nonBayesian settings. We also provide optimal reconstruction algorithms, of reduced complexity, which compress and reconstruct the sampled source components first under a modified distortion criterion, and then form MMSE estimates for the unsampled components based on reconstructions of the former. AcknowledgmentsThis dissertation started with a joint project between Prof. Prakash Narayan and Prof. Ankur Srivastava, as a principled way to studying an important problem in computer architecture. In multicore processors, an increase in...

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“…Compression strategies are designed to minimize the amount of data being transmitted to the FC. This compression can be achieved via quantization [19]- [22], where only symbols from a finite set are transmitted to the FC. Another, popular way of achieving compression is via linear precoding where the dimensions of the observations are reduced via a compression matrix before transmission to the FC [11], [12], [23]- [30].…”

Section: Introductionmentioning

confidence: 99%

Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network

Cheng,

Khanduri,

Chen

et al. 2020

Preprint

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In this work, we propose a joint collaborationcompression framework for sequential estimation of a random vector parameter in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a collaboration matrix) with each other. Then a subset of sensors selected to communicate with the FC linearly compress their observations before transmission. We design near-optimal collaboration and linear compression strategies under power constraints via alternating minimization of the sequential minimum mean square error. We show that the objective function for collaboration design can be non-convex depending on the network topology. We reformulate and solve the collaboration design problem using quadratically constrained quadratic program (QCQP). Moreover, the compression design problem is also formulated as a QCQP. We propose two versions of compression design, one centralized where the compression strategies are derived at the FC and the other decentralized, where the local sensors compute their individual compression matrices independently. It is noted that the design of decentralized compression strategy is a non-convex problem. We obtain a near-optimal solution by using the bisection method. In contrast to the one-shot estimator, our proposed algorithm is capable of handling dynamic system parameters such as channel gains and energy constraints. Importantly, we show that the proposed methods can also be used for estimating time-varying random vector parameters. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

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Intersensor Collaboration in Distributed Quantization Networks

Cited by 12 publications

References 29 publications

Quantization for Robust Distributed Coding

Quantization for Robust Distributed Coding

Sampling rate distortion

Joint Collaboration and Compression Design for Distributed Sequential Estimation in a Wireless Sensor Network

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