Asynchronous inference in ambiently-powered wireless sensor networksAsynchronous inference in ambiently-powered wireless sensor networksΑσύγχρονος συμπερασμός σε ασύρματα δίκτυα αισθητήρων με ενέργεια από το περιβάλλον
Διπλωματική Εργασία
Diploma Work
2021-10-152021enWireless Sensor Networks (WSNs) are cost effective and ultra-low power networks that have recently become an integral part of many Internet-of-Things (IoT) applications. They consist of a certain number of nodes (or terminals), each of which is connected to a large number of sensors. Typically, the ambient information that the sensors are able to collect is wirelessly tranferred to some kind of centralized processing unit, which usually involves cloud or edge technologies.
In this work, we consider a WSN that is batteryless and solely powered by the environment. Our goal is to utilize such a network removing the centralized processing unit, and, by carefully balancing the computation and communication cost of modern inference algorithms, allow it to make autonomous, in network decisions itself; all that, exploiting its asynchronous operation that stems from the fact that it is ambiently powered: at some point of time certain WSN nodes may fail to operate.
In particular, we consider a linear fixed point problem and mathematically formulate its asynchronous variant, aiming to capture the asynchronous operation of the WSN, according to which some parts of the calculated vector may not be updated at some iterations. We propose a k-means based clustering method of assigning different parts of a vector to different WSN nodes. Next, we describe two algorithms that are both expressed as linear fixed point problems: a) Gaussian Belief Propagation and b) Average Consensus, as well as their asynchronous variants introduced in this work. Analysis as well as numerical results of this work show that the asynchronous operation of a WSN can be a key in the convergence of Gaussian Belief Propagation; indeed, we show that different asynchronous schedulings vastly affect its convergence speed, and – in some cases – asynchrony can make a divergent instance (in synchronous operation) to converge. On the other hand, in the case of Average Consensus, we derive a statistical condition that, when satisfied, leads to in expectation convergence of the algorithm. Hence, it is possible to execute Average Consensus in an ambiently powered WSN; the caveat here is an increased delay, since independent repetitions of the algorithm are necessary for an accurate result.http://creativecommons.org/licenses/by/4.0/Πολυτεχνείο Κρήτης::Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών ΥπολογιστώνPapageorgiou_Vasileios_Dip_2021.pdfChania [Greece]Library of TUC2021-10-15application/pdf1.1 MBembargo
Papageorgiou Vasileios
Παπαγεωργιου Βασιλειος
Bletsas Aggelos
Μπλετσας Αγγελος
Karystinos Georgios
Καρυστινος Γεωργιος
Lagoudakis Michail
Λαγουδακης Μιχαηλ
Πολυτεχνείο Κρήτης
Technical University of Crete
Asynchronous computations
Inference
Wireless sensor networks