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dc.contributor.authorBråten, Anders Eivind
dc.contributor.authorKraemer, Frank Alexander
dc.contributor.authorPalma, David
dc.date.accessioned2020-02-05T12:08:54Z
dc.date.available2020-02-05T12:08:54Z
dc.date.created2019-12-28T15:51:02Z
dc.date.issued2019
dc.identifier.isbn978-1-7281-2949-5
dc.identifier.urihttp://hdl.handle.net/11250/2639811
dc.description.abstractDevice management can enhance large-scale deployments of IoT nodes in non-stationary environments by supporting prediction and planning of their energy budget. This increases their ability for perpetual operation and is a step towards maintenance-free IoT. In this paper we consider how to accelerate the collection of relevant training data for nodes that are introduced into an existing deployment to increase the accuracy of their predictions. In particular, we investigate how nodes powered by solar energy can learn their energy intake faster and more accurately by using data from selected nodes that are working in similar conditions. We explore an architecture that utilizes different training data selection policies to manage the learning processes. For validation, we perform a case study to explore how nodes with correlated data can contribute to the learning process of other nodes. The obtained results indicate that this approach improves the accuracy of the predictions of a new node by 14%.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.relation.ispartof2019 Sixth International Conference on Internet of Things: Systems, Management and Security (IOTSMS)
dc.relation.urihttps://ieeexplore.ieee.org/document/8939220
dc.titleAdaptive, Correlation-Based Training Data Selection for IoT Device Managementnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber169-176nb_NO
dc.identifier.doi10.1109/IOTSMS48152.2019.8939220
dc.identifier.cristin1764088
dc.description.localcode© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,30,0
cristin.unitnameInstitutt for informasjonssikkerhet og kommunikasjonsteknologi
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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