Asynchronous inference for security applications Asynchronous inference for security applications Ασύγχρονος συμπερασμός για εφαρμογές ασφάλειας Διπλωματική Εργασία Diploma Work 2022-10-182022enThis work studies distributed inference for privacy-non intrusive security applications. Specifically, minimum mean square estimation (MMSE) of channel parameters is proposed, with asynchronous and distributed implementation of Gaussian Belief Propagation (GBP). Wireless channel is measured by distributed wireless terminals/sensors, which exchange messages to infer the wireless channel through asynchronous GBP. Variations of the channel between consecutive measurements indicate the presence (or absence) of a moving person, without other means (e.g., cameras, microphones or infrared sensors). The method is tested in simulations and compared to synchronous GBP in terms of convergence; in addition, the method is contrasted against centralized least-squares (LS) and centralized MMSE, at the WiFi carrier frequency of 2.4 GHz, at various signal-to-noise ratios. It is found that mobility changes channel estimate metrics by at least one order of magnitude, compared to the static (immobile) case. The method could be exploited in indoor environments, where distributed presence of embedded wireless radios is widespread, without reverting to privacy intrusive microphones or cameras. In addition, the method is truly asynchronous and distributed, and hence more robust compared to synchronous counterparts. http://creativecommons.org/licenses/by/4.0/Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών ΥπολογιστώνSifakis_Zacharias-Iosif_Dip_2022.pdfChania [Greece]Library of TUC2022-10-17application/pdf818.8 kBembargo Sifakis Zacharias-Iosif Σηφακης Ζαχαριας-Ιωσηφ Bletsas Aggelos Μπλετσας Αγγελος Lagoudakis Michail Λαγουδακης Μιχαηλ Karystinos Georgios Καρυστινος Γεωργιος Πολυτεχνείο Κρήτης Technical University of Crete Asynchrony MMSE LS Channel estimation Distributed GBP Inference Asynchronous inference