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CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensemblesoa mark
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dc.contributor.authorAkram, Beenish Ayesha-
dc.contributor.authorAkbar, Ali Hammad-
dc.contributor.authorKim, Ki Hyung-
dc.date.issued2018-01-01-
dc.identifier.issn1875-905X-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/30420-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055319204&origin=inward-
dc.description.abstractIndoor localization has continued to garner interest over the last decade or so, due to the fact that its realization remains a challenge. Fingerprinting-based systems are exciting because these embody signal propagation-related information intrinsically as compared to radio propagation models. Wi-Fi (an RF technology) is best suited for indoor localization because it is so widely deployed that literally, no additional infrastructure is required. Since location-based services depend on the fingerprints acquired through the underlying technology, smart mechanisms such as machine learning are increasingly being incorporated to extract intelligible information. We propose CEnsLoc, a new easy to train-And-deploy Wi-Fi localization methodology established on GMM clustering and Random Forest Ensembles (RFEs). Principal component analysis was applied for dimension reduction of raw data. Conducted experimentation demonstrates that it provides 97% accuracy for room prediction. However, artificial neural networks, k-nearest neighbors, K, FURIA, and DeepLearning4J-based localization solutions provided mean 85%, 91%, 90%, 92%, and 73% accuracy on our collected real-world dataset, respectively. It delivers high room-level accuracy with negligible response time, making it viable and befitted for real-Time applications.-
dc.description.sponsorship(is research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07048697).-
dc.description.sponsorshipMinistry of Education (NRF-2018R1D1A1B07048697)-
dc.language.isoeng-
dc.publisherHindawi Limited-
dc.subject.meshClassification ensembles-
dc.subject.meshDimension reduction-
dc.subject.meshIndoor localization-
dc.subject.meshK-nearest neighbors-
dc.subject.meshRadio propagation models-
dc.subject.meshReal-time application-
dc.subject.meshSignal propagation-
dc.subject.meshWi-Fi localizations-
dc.titleCEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles-
dc.typeArticle-
dc.citation.titleMobile Information Systems-
dc.citation.volume2018-
dc.identifier.bibliographicCitationMobile Information Systems, Vol.2018-
dc.identifier.doi10.1155/2018/3287810-
dc.identifier.scopusid2-s2.0-85055319204-
dc.identifier.urlhttp://www.hindawi.com/journals/misy/contents/-
dc.type.otherArticle-
dc.identifier.pissn1574-017X-
dc.description.isoatrue-
dc.subject.subareaComputer Science Applications-
dc.subject.subareaComputer Networks and Communications-
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