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dc.contributor.authorRodway, James
dc.contributor.authorMusilek, Petr
dc.date.accessioned2017-07-03T11:26:37Z
dc.date.available2017-07-03T11:26:37Z
dc.date.issued2017
dc.identifier.citationEnergies. 2017, vol. 10, issue 5, art. no. 607.cs
dc.identifier.issn1996-1073
dc.identifier.urihttp://hdl.handle.net/10084/117161
dc.description.abstractWireless sensor networks can be used to collect data in remote locations, especially when energy harvesting is used to extend the lifetime of individual nodes. However, in order to use the collected energy most effectively, its consumption must be managed. In this work, forecasts of diurnal solar energies were made based on measurements of atmospheric pressure. These forecasts were used as part of an adaptive duty cycling scheme for node level energy management. This management was realized with a fuzzy logic controller that has been tuned using differential evolution. Controllers were created using one and two days of energy forecasts, then simulated in software. These controllers outperformed a human-created reference controller by taking more measurements while using less reserve energy during the simulated period. The energy forecasts were comparable to other available methods, while the method of tuning the fuzzy controller improved overall node performance. The combination of the two is a promising method of energy management.cs
dc.format.extent846237 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofseriesEnergiescs
dc.relation.urihttp://dx.doi.org/10.3390/en10050607cs
dc.rights© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectwireless sensor networkscs
dc.subjectenergy forecastcs
dc.subjectdifferential evolutioncs
dc.subjectenergy managementcs
dc.titleHarvesting-aware energy management for environmental monitoring WSNcs
dc.typearticlecs
dc.identifier.doi10.3390/en10050607
dc.rights.accessopenAccess
dc.type.versionpublishedVersioncs
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume10cs
dc.description.issue5cs
dc.description.firstpageart. no. 607cs
dc.identifier.wos000403048400026


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© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.