DC FieldValueLanguage
dc.contributor.authorAnagnostopoulos, Theodoros-
dc.contributor.authorFerreira, Denzil-
dc.contributor.authorVelloso, Eduardo-
dc.contributor.authorFlores, Huber-
dc.contributor.authorVan Berkel, Niels-
dc.contributor.authorSarsenbayeva, Zhanna-
dc.contributor.authorKlakegg, Simon-
dc.contributor.authorVisuri, Aku-
dc.contributor.authorLuo, Chu-
dc.contributor.authorMöttönen, Antti-
dc.contributor.authorKostakos, Vassilis-
dc.contributor.authorGoncalves, Jorge-
dc.date.accessioned2024-07-05T11:24:34Z-
dc.date.available2024-07-05T11:24:34Z-
dc.date.issued2019-02-04-
dc.identifierscopus-85058496336-
dc.identifier.issn1617-4909-
dc.identifier.other85058496336-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2681-
dc.description.abstractWe investigate the predictability of the next unlock event on smartphones, using machine learning and smartphone contextual data. In a 2-week field study with 27 participants, we demonstrate that it is possible to predict when the next unlock event will occur. Additionally, we show how our approach can improve accuracy and energy efficiency by solely relying on software-related contextual data. Based on our findings, smartphone applications and operating systems can improve their energy efficiency by utilising short-term predictions to minimise unnecessary executions, or launch computation-intensive tasks, such as OS updates, in the locked state. For instance, by inferring the next unlock event, smartphones can pre-emptively collect sensor data or prepare timely content to improve the user experience during the subsequent phone usage session.en_US
dc.language.isoenen_US
dc.relation.ispartofPersonal and Ubiquitous Computingen_US
dc.subjectContext-awarenessen_US
dc.subjectMachine learningen_US
dc.subjectSensorsen_US
dc.subjectSmartphonesen_US
dc.titleEnergy-efficient prediction of smartphone unlockingen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s00779-018-01190-0en_US
dc.identifier.scopus2-s2.0-85058496336-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.relation.volume23en_US
dc.relation.issue1en_US
dc.identifier.spage159en_US
dc.identifier.epage177en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusverifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.orcid0000-0002-5587-2848-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
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