Robust Distributed Estimation in Sensor Networks using the Embedded Polygons Algorithm

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dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameInformation Processing in Sensor Networksen_US
dc.citation.firstpage405en_US
dc.citation.lastpage413en_US
dc.citation.locationBerkeley, CAen_US
dc.contributor.authorDelouille, Veroniqueen_US
dc.contributor.authorNeelamani, Rameshen_US
dc.contributor.authorBaraniuk, Richard G.en_US
dc.contributor.orgDigital Signal Processing (http://dsp.rice.edu/)en_US
dc.date.accessioned2007-10-31T00:42:39Zen_US
dc.date.available2007-10-31T00:42:39Zen_US
dc.date.issued2004-04-01en_US
dc.date.modified2006-06-20en_US
dc.date.note2004-03-03en_US
dc.date.submitted2004-04-01en_US
dc.descriptionConference Paperen_US
dc.description.abstractWe propose a new iterative distributed algorithm for linear minimum mean-squared-error (LMMSE) estimation in sensor networks whose measurements follow a Gaussian hidden Markov graphical model with cycles. The <i>embedded polygons algorithm</i> decomposes a loopy graphical model into a number of linked embedded polygons and then applies a parallel block Gauss-Seidel iteration comprising local LMMSE estimation on each polygon (involving inversion of a small matrix) followed by an information exchange between neighboring nodes and polygons. The algorithm is robust to temporary communication faults such as link failures and sleeping nodes and enjoys guaranteed convergence under mild conditions. A simulation study indicates that energy consumption for iterative estimation increases substantially as more links fail or nodes sleep. Thus, somewhat surprisingly, energy conservation strategies such as low-powered transmission and aggressive sleep schedules could actually be counterproductive.en_US
dc.identifier.citationV. Delouille, R. Neelamani and R. G. Baraniuk, "Robust Distributed Estimation in Sensor Networks using the Embedded Polygons Algorithm," 2004.en_US
dc.identifier.doihttp://dx.doi.org/10.1145/984622.984681en_US
dc.identifier.urihttps://hdl.handle.net/1911/19855en_US
dc.language.isoengen_US
dc.subjectSensor networksen_US
dc.subjectdistributed estimationen_US
dc.subjectgraphical modelsen_US
dc.subjecthidden Markov modelsen_US
dc.subjectWiener filteren_US
dc.subjectmatrix splitting distributed estimationen_US
dc.subjectgraphical modelsen_US
dc.subjecthidden Markov modelsen_US
dc.subjectWiener filteren_US
dc.subjectmatrix splittingen_US
dc.subject.keywordSensor networksen_US
dc.subject.keyworddistributed estimationen_US
dc.subject.keywordgraphical modelsen_US
dc.subject.keywordhidden Markov modelsen_US
dc.subject.keywordWiener filteren_US
dc.subject.keywordmatrix splitting distributed estimationen_US
dc.subject.keywordgraphical modelsen_US
dc.subject.keywordhidden Markov modelsen_US
dc.subject.keywordWiener filteren_US
dc.subject.keywordmatrix splittingen_US
dc.subject.otherSignal Processing Applicationsen_US
dc.titleRobust Distributed Estimation in Sensor Networks using the Embedded Polygons Algorithmen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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