Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/31224
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorZhang, D-
dc.creatorGuo, M-
dc.creatorZhou, J-
dc.creatorKang, D-
dc.creatorCao, J-
dc.date.accessioned2014-12-19T07:09:44Z-
dc.date.available2014-12-19T07:09:44Z-
dc.identifier.issn0167-739X-
dc.identifier.urihttp://hdl.handle.net/10397/31224-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectContext reasoningen_US
dc.subjectEvidence theoryen_US
dc.subjectPervasive computingen_US
dc.titleContext reasoning using extended evidence theory in pervasive computing environmentsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage207-
dc.identifier.epage216-
dc.identifier.volume26-
dc.identifier.issue2-
dc.identifier.doi10.1016/j.future.2009.08.005-
dcterms.abstractMost existing context reasoning approaches implicitly assume that contexts are precise and complete. This assumption cannot be held in pervasive computing environments, where contexts are often imprecise and incomplete due to unreliable connectivity, user mobility and resource constraints. To this end, we propose an approach called CRET: Context Reasoning using extended Evidence Theory. CRET applies the evidence theory to context reasoning in pervasive computing environments. Because evidence theory is limited by two fundamental problems-computation-intensiveness and Zadeh paradox, CRET presents evidence selection and conflict resolution strategies. Empirical study shows that CRET is desirable for pervasive applications.-
dcterms.bibliographicCitationFuture generation computer systems, 2010, v. 26, no. 2, p. 207-216-
dcterms.isPartOfFuture generation computer systems-
dcterms.issued2010-
dc.identifier.scopus2-s2.0-70349764321-
dc.identifier.eissn1872-7115-
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