HybLoc: Hybrid Indoor Wi-Fi Localization using Soft Clustering based Random Decision Forest Ensembles
Indoor localization has garnered attention of researchers over the past two decades due to diverse and numerous applications. The existing works either provide room-level or latitude-longitude prediction instead of a hybrid solution, catering only to specific application needs. This paper proposes a new infrastructure-less, indoor localization system named HybLoc using Wi-Fi fingerprints. The system employs Gaussian Mixture Model (GMM) based soft clustering and Random Decision Forest ensembles for hybrid indoor localization i.e. both room-level and latitude-longitude prediction. GMM based soft clustering allows to find natural data subsets helping cascaded classifiers better learn underlying data dynamics. Random Decision Forest ensembles enhance the capabilities of Decision Trees providing better generalization. A publically available Wi-Fi fingerprints dataset UJIIndoorLoc (multi-floor; multi-building) has been used for experimental evaluation. The results describe the potential of HybLoc to provide hybrid location of user viz a viz the reported literature for both levels of prediction. For room estimation, HybLoc has demonstrated mean 85% accuracy, 89% precision as compared to frequently used kNN and ANN based approaches with 56% accuracy, 60% precision and 42% accuracy, 48% precision respectively averaged over all buildings. We also compared HybLoc performance with baseline Random Forest providing 79% accuracy and 82% precision which clearly demonstrates the enhanced performance by HybLoc. In terms of latitude-longitude prediction, HybLoc, kNN, ANN, and baseline Random Forest had 6.29m, 8.1m, 180.7m and 10.2m mean error over complete dataset. We also present useful results on how number of samples and missing data replacement value affect the performance of the system.
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Akram, B.A. (Beenish A.), Akbar, A.H. (Ali H.), & Shafiq, M.O. (2018). HybLoc: Hybrid Indoor Wi-Fi Localization using Soft Clustering based Random Decision Forest Ensembles. IEEE Access. doi:10.1109/ACCESS.2018.2852658