Clustering of inference algorithms in communication networksClustering of inference algorithms in communication networksΟμαδοποίηση αλγορίθμων συμπερασμού σε δίκτυα επικοινωνιών Διπλωματική Εργασία Diploma Work 2022-09-282022enThis 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).http://creativecommons.org/licenses/by/4.0/Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών ΥπολογιστώνKariotakis_Emmanouil_Dip_2022.pdfChania [Greece]Library of TUC2022-09-28application/pdf5.8 MBembargo Kariotakis Emmanouil Καριωτακης Εμμανουηλ Bletsas Aggelos Μπλετσας Αγγελος Zervakis Michail Ζερβακης Μιχαηλ Karystinos Georgios Καρυστινος Γεωργιος Πολυτεχνείο Κρήτης Technical University of Crete Affine updates Asynchronous scheduling Clustering Αλγόριθμοι συμπερασμού Ασύρματα δίκτυα αισθητήρων Inference algorithms Wireless sensor networks