TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization

TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization

Автор: M.W.P Maduranga, Ruvan Abeysekera

Журнал: International Journal of Wireless and Microwave Technologies @ijwmt

Статья в выпуске: 5 Vol.11, 2021 года.

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Learning-based localization plays a significant role in wireless indoor localization problems over deterministic or probabilistic-based methods. Recent works on machine learning-based indoor localization show the high accuracy of predicting over traditional localization methods existing. This paper presents a Received Signal Strength (RSS) based improved localization method called TreeLoc(Tree-Based Localization). This novel method is based on ensemble learning trees. Popular Decision Tree Regressor (DTR), Random Forest Regression (RFR), and Extra Tree Regressor have been investigated to develop the novel TreeLoc method. Out of the tested algorithm, the TreeLoc algorithm showed better performances in position estimation for indoor environments with RMSE 8.79 for the x coordinate and 8.83 for the y coordinate.

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Indoor Localization, Machine Learning, Internet of Things, Ensemble Learning, Wireless Sensor Networks

Короткий адрес: https://sciup.org/15017694

IDR: 15017694   |   DOI: 10.5815/ijwmt.2021.05.03

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