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dc.contributor.authorDelouille, Veronique
Neelamani, Ramesh
Baraniuk, Richard G.
dc.creatorDelouille, Veronique
Neelamani, Ramesh
Baraniuk, Richard G.
dc.date.accessioned 2007-10-31T00:42:41Z
dc.date.available 2007-10-31T00:42:41Z
dc.date.issued 2006-08-01
dc.date.submitted 2006-07-24
dc.identifier.citation V. Delouille, R. Neelamani and R. G. Baraniuk, "Robust Distributed Estimation Using the Embedded Subgraphs Algorithm," IEEE Transactions on Signal Processing, vol. 54, no. 8, 2006.
dc.identifier.urihttps://hdl.handle.net/1911/19856
dc.description Journal Paper
dc.description.abstract We propose a new iterative, distributed approach for linear minimum mean-square-error (LMMSE) estimation in graphical models with cycles. The embedded subgraphs algorithm (ESA) decomposes a loopy graphical model into a number of linked embedded subgraphs and applies the classical parallel block Jacobi iteration comprising local LMMSE estimation in each subgraph (involving inversion of a small matrix) followed by an information exchange between neighboring nodes and subgraphs. Our primary application is sensor networks, where the model encodes the correlation structure of the sensor measurements, which are assumed to be Gaussian. The resulting LMMSE estimation problem involves a large matrix inverse, which must be solved in-network with distributed computation and minimal intersensor communication. By invoking the theory of asynchronous iterations, we prove that ESA is robust to temporary communication faults such as failing links and sleeping nodes, and enjoys guaranteed convergence under relatively mild conditions. Simulation studies demonstrate that ESA compares favorably with other recently proposed algorithms for distributed estimation. Simulations also indicate that energy consumption for iterative estimation increases substantially as more links fail or nodes sleep. Thus, somewhat surprisingly, sensor network energy conservation strategies such as low-powered transmission and aggressive sleep schedules could actually prove counterproductive. Our results can be replicated using MATLAB code from www.dsp.rice.edu/software.
dc.description.sponsorship Texas Instruments
dc.description.sponsorship Defense Advanced Research Projects Agency
dc.description.sponsorship Office of Naval Research
dc.description.sponsorship National Science Foundation
dc.description.sponsorship Air Force Office of Scientific Research
dc.language.iso eng
dc.subjectAsynchronous iterations
Wiener filter
distributed estimation
graphical models
matrix splitting
sensor networks
dc.subject.otherDSP for Communications
dc.title Robust Distributed Estimation Using the Embedded Subgraphs Algorithm
dc.type Journal article
dc.citation.bibtexName article
dc.citation.journalTitle IEEE Transactions on Signal Processing
dc.date.modified 2006-07-24
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)
dc.subject.keywordAsynchronous iterations
Wiener filter
distributed estimation
graphical models
matrix splitting
sensor networks
dc.citation.volumeNumber 54
dc.citation.issueNumber 8
dc.type.dcmi Text
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1109/TSP.2006.874839
dc.citation.firstpage 2998
dc.citation.lastpage 3010


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  • DSP Publications [508]
    Publications by Rice Faculty and graduate students in digital signal processing.
  • ECE Publications [1336]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students

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