Abstract
K-nearest neighbor (kNN) is one of the most popular methods used in indoor positioning systems. The estimated position is, nevertheless, the center of the area formed by some fingerprints which have the smallest Euclidean distance, regardless of the difference between these distances. This leads to an error in the prediction process. In this paper, to improve the positioning accuracy, some variants of kNN, namely Weighted kNN (WkNN), are suggested. In addition to evaluating the quality of each method, we provide a detailed comparison of all methods together to figure out the optimal solution for the visible LED light positioning system. The obtained results show that all the proposed WkNN methods can significantly improve the positioning accuracy. However, the level of improvement is not exactly the same. In the best case, our proposed solution achieves an improvement by 100-fold compared to traditional kNN method.
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This work was supported by Korea Hydro & Nuclear Power company through the project “Nuclear Innovation Center for Haeoleum Alliance”.
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Tran, H.Q., Ha, C. (2019). Comparison of Various Weighted K-Nearest Neighbor Methods for Positioning Systems Using Visible LED Lights. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11643. Springer, Cham. https://doi.org/10.1007/978-3-030-26763-6_57
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DOI: https://doi.org/10.1007/978-3-030-26763-6_57
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