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Inference techniques in low-cost sensor networks

Apostolakis Georgios

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URIhttp://purl.tuc.gr/dl/dias/45F15E3A-2A5F-409F-B894-85DC0FA229A0-
Identifierhttps://doi.org/10.26233/heallink.tuc.95446-
Languageen-
Extent2.5 megabytesen
Extent55 pagesen
TitleInference techniques in low-cost sensor networksen
TitleΤεχνικές συμπερασμού σε δίκτυα αισθητήρων χαμηλού κόστουςel
CreatorApostolakis Georgiosen
CreatorΑποστολακης Γεωργιοςel
Contributor [Thesis Supervisor]Bletsas Aggelosen
Contributor [Thesis Supervisor]Μπλετσας Αγγελοςel
Contributor [Committee Member]Lagoudakis Michailen
Contributor [Committee Member]Λαγουδακης Μιχαηλel
Contributor [Committee Member]Deligiannakis Antoniosen
Contributor [Committee Member]Δεληγιαννακης Αντωνιοςel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστώνel
Content SummaryDistributed execution of algorithms across resource-constrained terminals has become increasingly popular, especially when fault tolerance is required. Asynchronous operation is brought to light in such scenarios, and in particular, probabilistic asynchronous operation, which models the failure probability of each terminal. The focus of this work is on the affine update model, which is applicable to a wide range of distributed inference algorithms. Applications include estimation of the average, solving linear systems, linear minimum mean square error estimation and spectral clustering, presented in detail in this thesis. Multimodal inference is also investigated, where two or more alternate sources of data are exploited for increased prediction accuracy. In that context, two variations of linear regression are presented, with uniform or Gaussian prior, which are equivalent to iterative affine updates. Furthermore, this work offers an asymptotic analysis for the arithmetic mean of the state vector, across a finite number of experiments, for the discovery of fixed points. It is shown that there are cases where the arithmetic mean behaves differently than the expected mean, and a sufficient condition is provided for convergence of the arithmetic mean to a fixed point. The lack of necessity for this condition is explained and subcases where the arithmetic mean converges, diverges, or has unpredictable behaviour are highlighted. Additionally, cases where the individual iterations never converge but their arithmetic mean does and offers fixed point are offered. Simulations corroborate the theoretical findings for various affine model setups. Finally, implementation details of a distributed low-cost sensor network are presented; the low-cost sensor network estimates the arithmetic mean of temperature across the network, by executing average consensus. The implementation is divided into hardware, network and software layers, and each layer is presented separately.en
Type of ItemΜεταπτυχιακή Διατριβήel
Type of ItemMaster Thesisen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-04-05-
Date of Publication2023-
SubjectΣυμπερασμόςel
SubjectInferenceen
SubjectΔίκτυα αισθητήρωνel
SubjectSensor networksen
SubjectΑφινικό μοντέλοel
SubjectAffine modelen
SubjectAsynchronyen
SubjectConvergence regionen
SubjectAsymptotic propertiesen
SubjectArithmetic meanen
SubjectDistributed implementationen
Subjectπεριοχή σύγκλισηςel
Subjectαριθμητικός μέσος όροςel
Subjectκατανεμημένη υλοποίησηel
Bibliographic CitationGeorgios Apostolakis, "Inference techniques in low-cost sensor networks", Master Thesis, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2023en
Bibliographic CitationΓεώργιος Αποστολάκης, "Τεχνικές συμπερασμού σε δίκτυα αισθητήρων χαμηλού κόστους", Μεταπτυχιακή Διατριβή, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2023el

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