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Asynchronous inference for security applications

Sifakis Zacharias-Iosif

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Year 2022
Type of Item Diploma Work
Bibliographic Citation Zacharias-Iosif Sifakis, "Asynchronous inference for security applications", Diploma Work, School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece, 2022
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This 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.

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