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dc.contributor.authorAbdi, Younes
dc.contributor.authorRistaniemi, Tapani
dc.date.accessioned2021-05-25T04:20:55Z
dc.date.available2021-05-25T04:20:55Z
dc.date.issued2021
dc.identifier.citationAbdi, Y., & Ristaniemi, T. (2021). Modeling and Mitigating Errors in Belief Propagation for Distributed Detection. <i>IEEE Transactions on Communications</i>, <i>69</i>(5), 3286-3297. <a href="https://doi.org/10.1109/TCOMM.2021.3056679" target="_blank">https://doi.org/10.1109/TCOMM.2021.3056679</a>
dc.identifier.otherCONVID_51396335
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/75927
dc.description.abstractWe study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed multidimensional hypothesis test over binary random variables. The joint statistical behavior of the sensor observations is modeled by a Markov random field whose parameters are used to build the BP messages exchanged between the sensing nodes. Through linearization of the BP message-update rule, we analyze the behavior of the resulting erroneous decision variables and derive closed-form relationships that describe the impact of stochastic errors on the performance of the BP algorithm. We then develop a decentralized distributed optimization framework to enhance the system performance by mitigating the impact of errors via a distributed linear data-fusion scheme. Finally, we compare the results of the proposed analysis with the existing works and visualize, via computer simulations, the performance gain obtained by the proposed optimization.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartofseriesIEEE Transactions on Communications
dc.rightsCC BY 4.0
dc.subject.otherdistributed systems
dc.subject.othercooperative communications
dc.subject.otherlikelihood-ratio test
dc.subject.othercommunication errors
dc.subject.othercomputation errors
dc.subject.otherblind signal processing
dc.subject.othermessage-passing algorithms
dc.subject.otherlinear data-fusion
dc.subject.otherfactor graphs
dc.titleModeling and Mitigating Errors in Belief Propagation for Distributed Detection
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202105253186
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.contributor.oppiaineTietotekniikkafi
dc.contributor.oppiaineMathematical Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.description.reviewstatuspeerReviewed
dc.format.pagerange3286-3297
dc.relation.issn0090-6778
dc.relation.numberinseries5
dc.relation.volume69
dc.type.versionpublishedVersion
dc.rights.copyright© Authors, 2021
dc.rights.accesslevelopenAccessfi
dc.subject.ysooptimointi
dc.subject.ysosignaalinkäsittely
dc.subject.ysolangaton tiedonsiirto
dc.subject.ysoalgoritmit
dc.subject.ysohajautetut järjestelmät
dc.subject.ysosensoriverkot
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p13477
jyx.subject.urihttp://www.yso.fi/onto/yso/p12266
jyx.subject.urihttp://www.yso.fi/onto/yso/p5445
jyx.subject.urihttp://www.yso.fi/onto/yso/p14524
jyx.subject.urihttp://www.yso.fi/onto/yso/p21082
jyx.subject.urihttp://www.yso.fi/onto/yso/p24338
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1109/TCOMM.2021.3056679


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