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CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensemblesoa mark
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Publication Year
2018-01-01
Journal
Mobile Information Systems
Publisher
Hindawi Limited
Citation
Mobile Information Systems, Vol.2018
Mesh Keyword
Classification ensemblesDimension reductionIndoor localizationK-nearest neighborsRadio propagation modelsReal-time applicationSignal propagationWi-Fi localizations
All Science Classification Codes (ASJC)
Computer Science ApplicationsComputer Networks and Communications
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.
ISSN
1875-905X
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/30420
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85055319204&origin=inward
DOI
https://doi.org/10.1155/2018/3287810
Journal URL
http://www.hindawi.com/journals/misy/contents/
Type
Article
Funding
(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).Ministry of Education (NRF-2018R1D1A1B07048697)
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Kim, Ki-Hyung 김기형
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