Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space

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dc.contributor.authorHribar, Jernejen
dc.contributor.authorMarinescu, Andreien
dc.contributor.authorChiumento, Alessandroen
dc.contributor.authorDaSilva, Luiz A.en
dc.date.accessioned2022-01-04T19:44:51Zen
dc.date.available2022-01-04T19:44:51Zen
dc.date.issued2021-01-01en
dc.date.updated2022-01-04T19:44:49Zen
dc.description.abstractMillions of battery-powered sensors deployed for monitoring purposes in a multitude of scenarios, e.g., agriculture, smart cities, industry, etc., require energy-efficient solutions to prolong their lifetime. When these sensors observe a phenomenon distributed in space and evolving in time, it is expected that collected observations will be correlated in time and space. This paper proposes a () based scheduling mechanism capable of taking advantage of correlated information. The designed solution employs () algorithm. The proposed mechanism can determine the frequency with which sensors should transmit their updates, to ensure accurate collection of observations, while simultaneously considering the energy available. The solution is evaluated with multiple datasets containing environmental observations obtained in multiple real deployments. The real observations are leveraged to model the environment with which the mechanism interacts as realistically as possible. The proposed solution can significantly extend the sensors’ lifetime and is compared to an idealized, all-knowing scheduler to demonstrate that its performance is near-optimal. Additionally, the results highlight the unique feature of proposed design, energy-awareness, by displaying the impact of sensors’ energy levels on the frequency of updates.en
dc.description.versionAccepted versionen
dc.format.extentPages 1-1en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/JIOT.2021.3114102en
dc.identifier.eissn2327-4662en
dc.identifier.issn2327-4662en
dc.identifier.issue99en
dc.identifier.urihttp://hdl.handle.net/10919/107356en
dc.identifier.volumePPen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject0805 Distributed Computingen
dc.subject1005 Communications Technologiesen
dc.titleEnergy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Spaceen
dc.title.serialIEEE Internet of Things Journalen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherJournal Articleen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/University Research Institutesen
pubs.organisational-group/Virginia Tech/All T&R Facultyen

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