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Towards ambiently powered inference on wireless sensor networks: asynchrony is the key!

Papageorgiou Vasileios, Nichoritis Athanasios, Vasilakopoulos Panagiotis, Vougioukas Georgios, Bletsas Aggelos

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/54FB21E3-0EFE-4364-AB50-474C8BF371AF-
Αναγνωριστικόhttps://doi.org/10.1109/DCOSS52077.2021.00077-
Αναγνωριστικόhttps://ieeexplore.ieee.org/document/9600006-
Γλώσσαen-
Μέγεθος8 pagesen
ΤίτλοςTowards ambiently powered inference on wireless sensor networks: asynchrony is the key!en
ΔημιουργόςPapageorgiou Vasileiosen
ΔημιουργόςΠαπαγεωργιου Βασιλειοςel
ΔημιουργόςNichoritis Athanasiosen
ΔημιουργόςΝηχωριτης Αθανασιοςel
ΔημιουργόςVasilakopoulos Panagiotisen
ΔημιουργόςΒασιλακοπουλος Παναγιωτηςel
ΔημιουργόςVougioukas Georgiosen
ΔημιουργόςΒουγιουκας Γεωργιοςel
ΔημιουργόςBletsas Aggelosen
ΔημιουργόςΜπλετσας Αγγελοςel
ΕκδότηςInstitute of Electrical and Electronics Engineersen
ΠερίληψηIs it possible to build ultra-low power wireless sensor networks (WSN) that exploit the inherent parallel and distributed nature of powerful message passing/inference algorithms, embrace ultra-low power communication principles and make autonomous, in-network decisions, solely powered by the environment? While edge and cloud computing emerge, this work points towards the opposite direction, inspired by the fact that ambient energy, either from radio frequency (RF), sun, motion, temperature or even living organisms, has fixed (on average) density per surface (or volume). It is shown, perhaps for the first time in the literature (to the best of our knowledge), a proof of concept, where a WSN harvests energy from the environment and processes itself the collected information in a distributed manner, by converting the (network) inference task to a probabilistic, message passing problem. Examples from Gaussian Belief Propagation and Average Consensus are offered; ambient energy harvesting and availability are quantified, controling the probability of successful (or not) message passing. Such interrupted communication requires distributed algorithms robust to asynchrony, at the expense of increased overall delay. Simulation and experimental validation are offered in a WSN testbed with solar energy harvesting. Future work will focus on overall delay minimization.en
ΤύποςΠλήρης Δημοσίευση σε Συνέδριοel
ΤύποςConference Full Paperen
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-05-26-
Ημερομηνία Δημοσίευσης2021-
Θεματική ΚατηγορίαWireless communicationen
Θεματική ΚατηγορίαWireless sensor networksen
Θεματική ΚατηγορίαMessage passingen
Θεματική ΚατηγορίαSolar energyen
Θεματική ΚατηγορίαSensor systemsen
Θεματική ΚατηγορίαDelaysen
Θεματική ΚατηγορίαLow-power electronicsen
Βιβλιογραφική ΑναφοράV. Papageorgiou, A. Nichoritis, P. Vasilakopoulos, G. Vougioukas and A. Bletsas, "Towards ambiently powered inference on wireless sensor networks: asynchrony is the key!," in 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), Pafos, Cyprus, 2021, pp. 458-465, doi: 10.1109/DCOSS52077.2021.00077.en

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