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

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M.W.P Maduranga 1,* Ruvan Abeysekera 1

1. IIC University of Technology, Phnom Penh, 121206, The Kingdom of Cambodia

* Corresponding author.


Received: 4 Aug. 2021 / Revised: 1 Sep. 2021 / Accepted: 18 Sep. 2021 / Published: 8 Oct. 2021

Index Terms

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


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.

Cite This Paper

M.W.P Maduranga, Ruvan Abeysekera, "TreeLoc: An Ensemble Learning-based Approach for Range Based Indoor Localization", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.11, No.5, pp. 18-25, 2021. DOI: 10.5815/ijwmt.2021.05.03


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