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dc.contributor.authorFang, Lei
dc.contributor.authorYe, Juan
dc.contributor.authorDobson, Simon Andrew
dc.date.accessioned2019-07-30T10:30:02Z
dc.date.available2019-07-30T10:30:02Z
dc.date.issued2019-06-16
dc.identifier.citationFang , L , Ye , J & Dobson , S A 2019 , Distributed self-monitoring sensor networks via Markov switching Dynamic Linear Models . in Proceedings 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) . , 8780572 , IEEE Computer Society , pp. 33-42 , 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019) , Umeå , Sweden , 16/06/19 . https://doi.org/10.1109/SASO.2019.00014en
dc.identifier.citationconferenceen
dc.identifier.isbn9781728127316
dc.identifier.otherPURE: 260325151
dc.identifier.otherPURE UUID: 7132410b-6730-4a21-94c4-a13285c04aba
dc.identifier.otherScopus: 85070545410
dc.identifier.otherORCID: /0000-0002-2838-6836/work/68280957
dc.identifier.otherORCID: /0000-0001-9633-2103/work/70234156
dc.identifier.otherWOS: 000501005500005
dc.identifier.urihttp://hdl.handle.net/10023/18201
dc.description.abstractWireless sensor networks empowered with low-cost sensing devices and wireless communications present an opportunity to enable continuous, fine-grained data collection over a wide environment. However, the quality of data collected is susceptible to the hardware conditions and also adversarial external factors such as high variance in temperature and humidity. Over time, the sensors report erroneous readings, which deviate from true readings. To tackle the problem, we propose an efficient self-monitoring, self-managing and self-adaptive sensing framework based on a dynamic hybrid Bayesian network that combines Hidden Markov Model and Dynamic Linear Model. The framework does not only enable automatic on-line inference of true readings robustly but also monitor the working status of sensor nodes at the same time, which can uncover important insights on hardware management. The whole process also benefits from the derived approximation algorithm and thus supports on-line one-pass computation with minimum human intervention, which make the accurate formal inference affordable for distributed edge processing.
dc.language.isoeng
dc.publisherIEEE Computer Society
dc.relation.ispartofProceedings 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2019)en
dc.rights© 2019, IEEE. This work has been made available online in accordance with the publisher's policies. This is the author created accepted version manuscript following peer review and as such may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1109/SASO.2019.00014en
dc.subjectSelf-managementen
dc.subjectSensor networksen
dc.subjectMachine learningen
dc.subjectDLMen
dc.subjectMarkov switching modelen
dc.subjectState space modelen
dc.subjectHybrid dynamic networken
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectT Technologyen
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccTen
dc.titleDistributed self-monitoring sensor networks via Markov switching Dynamic Linear Modelsen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.contributor.institutionUniversity of St Andrews.Sir James Mackenzie Institute for Early Diagnosisen
dc.identifier.doihttps://doi.org/10.1109/SASO.2019.00014
dc.identifier.urlhttps://www.simondobson.org/static/sd/softcopy/saso-ms-dlm-19.pdfen


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