Data Compression for Simultaneous/Sequential Inference Tasks in Sensor Networks

Chen, Mo; Fowler, Mark L.; Noga, Andrew J.

doi:10.1109/icassp.2006.1661461

Cited by 8 publications

(15 citation statements)

References 6 publications

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“…In addition, the energy consumption of communication should be taken into account. In order to address this issues, previous work has proposed the use of a Rate-Energy-Accuracy (R-E-A) metric [Chen 2006].…”

Section: Featuresmentioning

confidence: 99%

Compression in wireless sensor networks

Razzaque

Bleakley

Dobson

2013

ACM Trans. Sen. Netw.

151

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Wireless sensor networks (WSNs) are highly resource constrained in terms of power supply, memory capacity, communication bandwidth, and processor performance. Compression of sampling, sensor data, and communications can significantly improve the e ciency of utilization of three of these resources, namely power supply, memory and bandwidth. Recently, there have been a large number of proposals describing compression algorithms for WSNs. These proposals are diverse and involve di↵erent compression approaches. It is high time that these various individual e↵orts are put into perspective and a more holistic view taken. In this article, we take a step in that direction by presenting a survey of the literature in the area of compression and compression frameworks in WSNs. A comparative study of the various approaches is also provided. In addition, open research issues, challenges and future research directions are highlighted.

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Section: Featuresmentioning

confidence: 99%

Compression in wireless sensor networks

Razzaque

Bleakley

Dobson

2013

ACM Trans. Sen. Netw.

151

View full text Add to dashboard Cite

show abstract

“…Consider an r×c matrix with r and c chosen such that CR is maximized under the constraint that rc = np (or equivalently that r = np/c). For this case the compression ratio becomes (10) which should be maximized as a function of r for a given np. Thus, we must minimize the function 40 12 12…”

Section: Coding and Compression Ratio Analysismentioning

confidence: 99%

“…In [8], [9], [10] we have developed a method that uses Fisher information as a distortion measure that captures the true impact of compression on the TDOA/FDOA accuracy. This paper ties together the separate results we have obtained in those two areas.…”

Section: Introductionmentioning

confidence: 99%

“…This paper ties together the separate results we have obtained in those two areas. We apply the Fisher information-based distortion measure for FDOA accuracy [8], [9], [10] to find the optimal set of pulses to remove from the pulse matrix and then use the SVD for transform-based compression. The removal of pulses is shown to have negligible impact on the FDOA accuracy but does degrade the TDOA accuracy from that achievable using the SVD-based compression without pulse elimination.…”

Section: Introductionmentioning

confidence: 99%

“…To mitigate this, we have developed data compression methods tailored specifically for TDOA/FDOA emitter location systems which can be grouped into two main categories of approach: (i) exploiting redundancy between pulses when the emitter to be located is a radar [5], [6], [7] and (ii) a more general approach of exploiting the relative importance of specific time-frequency components of general signals (i.e. communication or radar signals) through the use of a Fisher information-based distortion measure [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Data compression using SVD and Fisher information for radar emitter location

et al. 2010

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This paper presents a data compression method that can achieve a very large compression ratio for radar pulse trains that are to be used for time-difference-of-arrival/frequency-difference-ofarrival (TDOA/FDOA) multiple-platform emitter location; this method exploits pulse-to-pulse redundancy to get a compression ratio much higher than possible using standard compression methods. We show how to use (i) the ability of the singular value decomposition (SVD) to exploit redundancy between radar pulses, and (ii) a Fisher information-based distortion criterion to enable elimination of pulses that are negligible to the FDOA estimation tasks. To enable the SVD to effectively remove the redundancy it is necessary to first optimally gate the pulses and place them in the rows of a matrix and then align the pulses to arrive at a matrix that is close to having rank of one. Finally, we suggest reasonable coding schemes for the information to be sent and assess the achievable compression level.The Fisher information-based removal of pulses is shown to have negligible impact on the FDOA accuracy but does degrade the TDOA accuracy from that achievable using the SVD-based compression without pulse elimination. However, we demonstrate that the SVD method includes an inherent denoising effect (common in SVD-based signal processing) that provides an improvement in TDOA accuracy over the case of no compression processing; thus, the overall impact on TDOA/FDOA accuracy is negligible while providing compression ratios up to 100:1 for typical radar signals.

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Signal Processing Challenges in Distributed Stream Processing Systems

Frossard

Verscheure

Venkatramani

2006 IEEE International Conference on Acoustics Speed and Signal Processing Proceedings

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Distributed stream processing represents a novel computing paradigm where data, sensed externally and possibly preprocessed, is pushed asynchronously to various connected computing devices with heterogeneous capabilities for processing. It enables novel applications typically characterized by the need to process high-volume data streams in a timely and responsive fashion. Some example applications include sensor networks, location-tracking services, distributed speech recognition, and network management. Recent work in large-scale distributed stream processing tackle various research challenges in both the application domain as well as in the underlying system. The main focus of this paper is to highlight some of the signal processing challenges such a novel computing framework brings. We first briefly introduce the main concepts behind distributed stream processing. Then we define the notion of relevant information from two related information-theoretic approaches. Finally, we browse existing techniques for sensing and quantizing the information given the set of classification, detection and estimation tasks, which we refer to as task-driven signal processing. We also address some of the related unexplored research challenges.

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Data Compression for Simultaneous/Sequential Inference Tasks in Sensor Networks

Cited by 8 publications

References 6 publications

Compression in wireless sensor networks

Compression in wireless sensor networks

Data compression using SVD and Fisher information for radar emitter location

Signal Processing Challenges in Distributed Stream Processing Systems

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