Abstract
The paper presents a modification of Random Forest approach to the indoor localization problem. The localization solution is based on RSS (Received Signal Strength) from multiple sources of Wi–Fi signal. We analyze two localization models. The first one is built using a straightforward application of a random forest method. The second model is a combination of localization models built for each Access Point from the building’s network using similar technique (Random Forests) as for the first model. The modification proposed in the second model gives us a substantial accuracy improvement when compared to the first model. We test also the solution against a network malfunction when some Access Points are turned off as the malfunction immunity is another important feature of the presented localization solution.
The research is supported by the National Centre for Research and Development, grant No PBS2/B3/24/2014, application No 208921.
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Górak, R., Luckner, M. (2016). Modified Random Forest Algorithm for Wi–Fi Indoor Localization System. In: Nguyen, N., Iliadis, L., Manolopoulos, Y., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2016. Lecture Notes in Computer Science(), vol 9876. Springer, Cham. https://doi.org/10.1007/978-3-319-45246-3_14
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DOI: https://doi.org/10.1007/978-3-319-45246-3_14
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