Timely and sustainable: Utilising correlation in status updates of battery-powered and energy-harvesting sensors using Deep Reinforcement Learning

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dc.contributor.authorHribar, Jernejen
dc.contributor.authorDaSilva, Luiz A.en
dc.contributor.authorZhou, Shengen
dc.contributor.authorJiang, Zhiyuanen
dc.contributor.authorDusparic, Ivanaen
dc.date.accessioned2022-10-21T12:36:59Zen
dc.date.available2022-10-21T12:36:59Zen
dc.date.issued2022-08-01en
dc.description.abstractIn a system with energy-constrained sensors, each transmitted observation comes at a price. The price is the energy the sensor expends to obtain and send a new measurement. The system has to ensure that sensors' updates are timely, i.e., their updates represent the observed phenomenon accurately, enabling services to make informed decisions based on the information provided. If there are multiple sensors observing the same physical phenomenon, it is likely that their measurements are correlated in time and space. To take advantage of this correlation to reduce the energy use of sensors, in this paper we consider a system in which a gateway sets the intervals at which each sensor broadcasts its readings. We consider the presence of battery-powered sensors as well as sensors that rely on Energy Harvesting (EH) to replenish their energy. We propose a Deep Reinforcement Learning (DRL)-based scheduling mechanism that learns the appropriate update interval for each sensor, by considering the timeliness of the information collected measured through the Age of Information (AoI) metric, the spatial and temporal correlation between readings, and the energy capabilities of each sensor. We show that our proposed scheduler can achieve near-optimal performance in terms of the expected network lifetime.en
dc.description.notesThis work was funded in part by the European Regional Development Fund through the SFI Research Centres Programme under Grant No. 13/RC/2077_P2 SFI CONNECT and by the SFI-NSFC Partnership Programme Grant Number 17/NSFC/5224. It is also supported by the Commonwealth Cyber Initiative at Virginia Tech.en
dc.description.sponsorshipEuropean Regional Development Fund through the SFI Research Centres Programme [13/RC/2077_P2 SFI CONNECT]; SFI-NSFC Partnership Programme [17/NSFC/5224]; Commonwealth Cyber Initiative at Virginia Techen
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1016/j.comcom.2022.05.030en
dc.identifier.eissn1873-703Xen
dc.identifier.issn0140-3664en
dc.identifier.urihttp://hdl.handle.net/10919/112251en
dc.identifier.volume192en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectDeep reinforcement learningen
dc.subjectAge of Informationen
dc.subjectEnergy harvestingen
dc.subjectInternet of Thingsen
dc.subjectEnergy efficiencyen
dc.subjectDeep Deterministic Policy Gradienten
dc.titleTimely and sustainable: Utilising correlation in status updates of battery-powered and energy-harvesting sensors using Deep Reinforcement Learningen
dc.title.serialComputer Communicationsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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