Citation Export
DC Field | Value | Language |
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dc.contributor.author | Akram, Beenish Ayesha | - |
dc.contributor.author | Akbar, Ali Hammad | - |
dc.contributor.author | Kim, Ki Hyung | - |
dc.date.issued | 2018-01-01 | - |
dc.identifier.issn | 1875-905X | - |
dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/30420 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055319204&origin=inward | - |
dc.description.abstract | Indoor 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.sponsorship | Ministry of Education (NRF-2018R1D1A1B07048697) | - |
dc.language.iso | eng | - |
dc.publisher | Hindawi Limited | - |
dc.subject.mesh | Classification ensembles | - |
dc.subject.mesh | Dimension reduction | - |
dc.subject.mesh | Indoor localization | - |
dc.subject.mesh | K-nearest neighbors | - |
dc.subject.mesh | Radio propagation models | - |
dc.subject.mesh | Real-time application | - |
dc.subject.mesh | Signal propagation | - |
dc.subject.mesh | Wi-Fi localizations | - |
dc.title | CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles | - |
dc.type | Article | - |
dc.citation.title | Mobile Information Systems | - |
dc.citation.volume | 2018 | - |
dc.identifier.bibliographicCitation | Mobile Information Systems, Vol.2018 | - |
dc.identifier.doi | 10.1155/2018/3287810 | - |
dc.identifier.scopusid | 2-s2.0-85055319204 | - |
dc.identifier.url | http://www.hindawi.com/journals/misy/contents/ | - |
dc.type.other | Article | - |
dc.identifier.pissn | 1574-017X | - |
dc.description.isoa | true | - |
dc.subject.subarea | Computer Science Applications | - |
dc.subject.subarea | Computer Networks and Communications | - |
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