A distributed and adaptive signal processing approach to exploiting correlation in sensor networks

Chou, J.; Petrović, Dragan; Ramchandran, Kannan

doi:10.1016/j.adhoc.2003.09.001

Cited by 21 publications

(19 citation statements)

References 12 publications

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“…from a discrete and finite distribution P over N binary strings, as in Chou, Petrovic and Ramchandran (2004) and Adler (2005). The component strings of…”

Section: Distributed Function Computation In Asymmetric Communicationmentioning

confidence: 98%

“…C B : minimum number of informant bits required in the worst-case to solve DSC problem at the sink Notes 1. Our formalism can also be applied to the communication scenarios where even the sink does not know P, but can estimate it as it collects the data from the informants, drawn from P. For example, in Chou et al (2004), a linear predictive model is used to estimate the correlation structure. It should be noted that we assume nothing about this distribution, except that it is a discrete distribution with finite alphabet.…”

Section: Nmentioning

confidence: 99%

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Worst-case asymmetric distributed function computation

Agnihotri

Venkatachalapathy

2013

International Journal of General Systems

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We consider a distributed function computation problem where an information sink communicates with N correlated information sources to compute a given deterministic function of source data. A data-vectorx ¼ ðx 1 ; . . . ; x N Þ , P is drawn from a discrete and finite probability distribution P ¼ pðx 1 ; . . . ; x N Þ and component x i is revealed to ith, 1 # i # N, source. We address this problem in asymmetric communication scenarios where only the sink knows the distribution P. We are interested in computing the minimum number of source bits required, in the worst-case, to solve this problem. We propose the notion of functional ambiguity to carry out the worst-case informationtheoretic analysis of distributed function computation problems. We establish its various characteristics and prove that it leads to a valid information measure. Then, we provide a constructive solution for the distributed function computation problem in terms of an interactive communication protocol and prove its optimality. Finally, we establish two equivalence classes of compressible and incompressible functions to classify the set of all computable multivariate functions based on the minimum number of source bits needed in the worst-case to compute a function in the distributed function computation set-up.

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“…from a discrete and finite distribution P over N binary strings, as in Chou, Petrovic and Ramchandran (2004) and Adler (2005). The component strings of…”

Section: Distributed Function Computation In Asymmetric Communicationmentioning

confidence: 98%

Section: Nmentioning

confidence: 99%

Worst-case asymmetric distributed function computation

Agnihotri

Venkatachalapathy

2013

International Journal of General Systems

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“…Aside from spatial correlation among sensor nodes, temporal correlation also exists in sensory data due to the nature of the physical phenomena observed by WSNs [Chou et al 2004;Tang and Raghavendra 2004]. Several approaches have been proposed to achieve energy efficiency by invoking less communication within the network [Vuran and Akyildiz 2006].…”

Section: Related Workmentioning

confidence: 98%

Energy-efficient data gathering in wireless sensor networks with asynchronous sampling

Wang

Liu

Das

2010

ACM Trans. Sen. Netw.

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A low sampling rate leads to reduced congestion and hence energy consumption in the resourceconstrained wireless sensor networks. In this article, we propose asynchronous sampling that shifts the sampling time instances of sensor nodes from each other. For lossy data gathering scenarios, the proposed approach provides more information about the physical phenomena in terms of increased entropy at a low sampling rate. For lossless data gathering scenarios, on the other hand, the sampling rate is lowered without sacrificing critical knowledge required for signal reconstruction. As lower sampling rates lead to smaller energy consumption for processing and transmitting the collected sensory data, the proposed asynchronous sampling strategies are capable of achieving a better trade-off between the lifetime of the network and the quality of collected information. In addition to mathematical analysis, simulation results based on real data also verify the benefits of our asynchronous sampling.

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“…We are interested in exploring interactive communication based solutions to this problem, as in [6][7][8].…”

Section: Asymmetric Distributed Source Codi-ng: Worst-case Analysismentioning

confidence: 99%

Information ambiguity

Agnihotri

Jamadagni

2008

2008 International Symposium on Information Theory and Its Applications

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A distributed and adaptive signal processing approach to exploiting correlation in sensor networks

Cited by 21 publications

References 12 publications

Worst-case asymmetric distributed function computation

Worst-case asymmetric distributed function computation

Energy-efficient data gathering in wireless sensor networks with asynchronous sampling

Information ambiguity

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