A New Energy Prediction Algorithm for Energy-Harvesting Wireless Sensor Networks With Q-Learning

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dc.authorid36975673200
dc.contributor.authorKosunalp S.
dc.date.accessioned20.04.201910:49:12
dc.date.accessioned2019-04-20T21:43:43Z
dc.date.available20.04.201910:49:12
dc.date.available2019-04-20T21:43:43Z
dc.date.issued2016
dc.departmentBayburt Üniversitesien_US
dc.description.abstractTraditional wireless sensor networks (WSNs) face the problem of a limited-energy source, typically batteries, resulting in the need for careful and effective utilization of the energy source. However, inevitable energy depletion will eventually disturb the operation of a WSN. Energy harvesting (EH) technology is acquiring particular interest, because it has the potential to provide a continuous energy supply in battery-powered WSNs. Solar energy is the most effective environmental energy for EH-WSNs because of its high energy intensity, which comes from a non-controllable source. Therefore, the prediction of future energy availability is a critical issue, as the amount of the harvestable energy may vary over time. In this paper, a novel solar energy prediction algorithm with Q-learning, called Q-learning-based solar energy prediction (QL-SEP), is proposed. Q-learning is an effective way of predicting future actions based on past observations. The distinctive feature of QL-SEP is that not only past days' observations but also the current weather conditions are considered for prediction. The performance of QL-SEP is simulated in this paper using real-world measurements obtained from a solar panel in comparison with the state-of-art approaches. © 2016 IEEE.en_US
dc.identifier.doi10.1109/ACCESS.2016.2606541
dc.identifier.endpage5763
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85027046574en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage5755
dc.identifier.urihttps://dx.doi.org/10.1109/ACCESS.2016.2606541
dc.identifier.urihttps://hdl.handle.net/20.500.12403/654
dc.identifier.volume4
dc.identifier.wosWOS:000386078300017en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEnergy harvesting
dc.subjectprediction algorithms
dc.subjectsolar energy
dc.subjectwireless sensor networks
dc.subjectElectric batteries
dc.subjectEnergy harvesting
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectSolar energy
dc.subjectContinuous energy
dc.subjectEnergy depletion
dc.subjectEnergy intensity
dc.subjectEnergy prediction
dc.subjectEnvironmental energy
dc.subjectLimited energies
dc.subjectPrediction algorithms
dc.subjectWireless sensor network (WSNs)
dc.subjectWireless sensor networks
dc.subjectEnergy harvesting
dc.subjectprediction algorithms
dc.subjectsolar energy
dc.subjectwireless sensor networks
dc.subjectElectric batteries
dc.subjectEnergy harvesting
dc.subjectForecasting
dc.subjectLearning algorithms
dc.subjectSolar energy
dc.subjectContinuous energy
dc.subjectEnergy depletion
dc.subjectEnergy intensity
dc.subjectEnergy prediction
dc.subjectEnvironmental energy
dc.subjectLimited energies
dc.subjectPrediction algorithms
dc.subjectWireless sensor network (WSNs)
dc.subjectWireless sensor networks
dc.titleA New Energy Prediction Algorithm for Energy-Harvesting Wireless Sensor Networks With Q-Learningen_US
dc.typeArticleen_US

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