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Clustering of inference algorithms in communication networks

Kariotakis Emmanouil

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URIhttp://purl.tuc.gr/dl/dias/28516A04-017A-4F5F-BB83-58B1025C51EE-
Identifierhttps://doi.org/10.26233/heallink.tuc.93442-
Languageen-
Extent5.8 megabytesen
Extent77 pagesen
TitleClustering of inference algorithms in communication networksen
TitleΟμαδοποίηση αλγορίθμων συμπερασμού σε δίκτυα επικοινωνιώνel
CreatorKariotakis Emmanouilen
CreatorΚαριωτακης Εμμανουηλel
Contributor [Thesis Supervisor]Bletsas Aggelosen
Contributor [Thesis Supervisor]Μπλετσας Αγγελοςel
Contributor [Committee Member]Zervakis Michailen
Contributor [Committee Member]Ζερβακης Μιχαηλel
Contributor [Committee Member]Karystinos Georgiosen
Contributor [Committee Member]Καρυστινος Γεωργιοςel
PublisherΠολυτεχνείο Κρήτηςel
PublisherTechnical University of Creteen
Academic UnitTechnical University of Crete::School of Electrical and Computer Engineeringen
Academic UnitΠολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστώνel
Content SummaryThis work offers an algorithmic framework for in-network inference, using message passing among ambiently powered wireless sensor network (WSN) terminals. The stochastic nature of ambient energy harvesting dictates intermittent operation of each WSN terminal and as such, the message passing inference algorithms should be robust to asynchronous operation. A version of Gaussian Belief Algorithm (GBP) is described, which can be reduced to an affine fixed point (AFP) problem, used to solve linear systems of equations. To achieve this, we have to cluster the Probabilistic Graphical Model (PGM) behind GBP, in order to map it to the WSN terminals. We propose two different clustering approaches, namely edge and node clustering. For the first approach, we explain the reasons why a previous method does not produce the expected results and we offer another method, which performs better. We also explain limitations of edge-based clustering. On the other hand, node clustering has a clear metric for performance, which is relevant to the number of edges connecting the different clusters. For this approach, we utilize three different clustering algorithms, the k-means, the spectral clustering and an autonomous, in-network clustering algorithm. Furthermore, we show in both theory and simulation that there is strong connection between spectral radius and the convergence rate of AFP problems with probabilistic asynchronous scheduling. The latter corroborates known theory for synchronous scheduling. Interestingly, it is shown through simulations that different clustering offers similar convergence rate, when probabilistic asynchronous scheduling is utilized with carefully selected probabilities that accelerate convergence rate in the mean sense. Finally, we show an existing distinction between convergence rate and energy consumption of the network and we present experimental results comparing the different clustering methods. In most cases, spectral clustering outperforms the rest, with reduced energy consumption (by a factor of 2 compared to k-means in specific cases).en
Type of ItemΔιπλωματική Εργασίαel
Type of ItemDiploma Worken
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2022-09-28-
Date of Publication2022-
SubjectAffine updatesen
SubjectAsynchronous schedulingen
SubjectClusteringen
SubjectΑλγόριθμοι συμπερασμούel
SubjectΑσύρματα δίκτυα αισθητήρωνel
SubjectInference algorithmsen
SubjectWireless sensor networksen
Bibliographic CitationEmmanouil Kariotakis, "Clustering of inference algorithms in communication networks", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022en
Bibliographic CitationΕμμανουήλ Καριωτάκης, "Ομαδοποίηση αλγορίθμων συμπερασμού σε δίκτυα επικοινωνιών", Διπλωματική Εργασία, Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών, Πολυτεχνείο Κρήτης, Χανιά, Ελλάς, 2022el

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