Title | ||
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CEnsLoc: Infrastructure-Less Indoor Localization Methodology Using GMM Clustering-Based Classification Ensembles. |
Abstract | ||
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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. |
Year | DOI | Venue |
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2018 | 10.1155/2018/3287810 | MOBILE INFORMATION SYSTEMS |
Field | DocType | Volume |
Data mining,Dimensionality reduction,Computer science,Response time,Raw data,Cluster analysis,Artificial neural network,Random forest,Radio propagation,Principal component analysis,Distributed computing | Journal | 2018 |
ISSN | Citations | PageRank |
1574-017X | 0 | 0.34 |
References | Authors | |
9 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Beenish A. Akram | 1 | 1 | 0.69 |
Ali Hammad Akbar | 2 | 79 | 12.73 |
Ki-Hyung Kim | 3 | 249 | 37.07 |