CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles
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-F...
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doaj-664b79b45fdf4f93b7c4593f655dee112021-07-02T06:34:00ZengHindawi LimitedMobile Information Systems1574-017X1875-905X2018-01-01201810.1155/2018/32878103287810CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification EnsemblesBeenish Ayesha Akram0Ali Hammad Akbar1Ki-Hyung Kim2Department of Computer Science and Engineering, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Science and Engineering, University of Engineering and Technology, Lahore, PakistanDepartment of Computer Engineering, Graduate School, Ajou University, Suwon, Republic of KoreaIndoor 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.http://dx.doi.org/10.1155/2018/3287810 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Beenish Ayesha Akram Ali Hammad Akbar Ki-Hyung Kim |
spellingShingle |
Beenish Ayesha Akram Ali Hammad Akbar Ki-Hyung Kim CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles Mobile Information Systems |
author_facet |
Beenish Ayesha Akram Ali Hammad Akbar Ki-Hyung Kim |
author_sort |
Beenish Ayesha Akram |
title |
CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles |
title_short |
CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles |
title_full |
CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles |
title_fullStr |
CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles |
title_full_unstemmed |
CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles |
title_sort |
censloc: infrastructure-less indoor localization methodology using gmm clustering-based classification ensembles |
publisher |
Hindawi Limited |
series |
Mobile Information Systems |
issn |
1574-017X 1875-905X |
publishDate |
2018-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2018/3287810 |
work_keys_str_mv |
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