Detailed Information

Cited 0 time in webofscience Cited 21 time in scopus
Metadata Downloads

IoT service classification and clustering for integration of IoT service platforms

Full metadata record
DC Field Value Language
dc.contributor.authorLee, Daewon-
dc.contributor.authorLee, HwaMin-
dc.date.accessioned2021-08-11T11:23:59Z-
dc.date.available2021-08-11T11:23:59Z-
dc.date.issued2018-12-
dc.identifier.issn0920-8542-
dc.identifier.issn1573-0484-
dc.identifier.urihttps://scholarworks.bwise.kr/sch/handle/2021.sw.sch/5461-
dc.description.abstractWith the rapid development of sensors, wireless communication, and cloud computing, information technology today focuses on service environments created by the Internet of Things (IoT). IoT technologies have become widely used in various contexts including smart homes, building management, surveillance services, and smart farms. Some IoT applications such as Siri are popular in everyday life. IoT requires communication and interaction between various devices and services. To solve the various complex problems associated with IoT services, earlier research focused on IoT service platforms such as gateways and mobile edge computing services. However, the similarities and reusabilities of IoT services have received little attention. In this paper, we develop an IoT service classification and clustering system. We classify the operation of an IoT service into four steps that differ in their characteristics. Based on this classification, we extend the classic EM (expectation-maximization) algorithm to cluster IoT services in terms of their similarities. To validate our proposed classification and clustering system, we divide over 100 commercial IoT services into five clusters, showing that such services are well clustered by similarity and purpose.-
dc.format.extent17-
dc.language영어-
dc.language.isoENG-
dc.publisherKluwer Academic Publishers-
dc.titleIoT service classification and clustering for integration of IoT service platforms-
dc.typeArticle-
dc.publisher.location네델란드-
dc.identifier.doi10.1007/s11227-018-2288-7-
dc.identifier.scopusid2-s2.0-85046451801-
dc.identifier.wosid000452081500026-
dc.identifier.bibliographicCitationJournal of Supercomputing, v.74, no.12, pp 6859 - 6875-
dc.citation.titleJournal of Supercomputing-
dc.citation.volume74-
dc.citation.number12-
dc.citation.startPage6859-
dc.citation.endPage6875-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasssci-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalWebOfScienceCategoryComputer Science, Hardware & Architecture-
dc.relation.journalWebOfScienceCategoryComputer Science, Theory & Methods-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.subject.keywordPlusALGORITHM-
dc.subject.keywordPlusINTERNET-
dc.subject.keywordAuthorInternet of Things (IoT)-
dc.subject.keywordAuthorService similarity-
dc.subject.keywordAuthorModule reusability-
dc.subject.keywordAuthorIntegrated IoT service platform-
dc.subject.keywordAuthorIoT classification-
dc.subject.keywordAuthorIoT clustering-
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Engineering > Department of Computer Software Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE