Abstract
This paper presents a novel Indoor Positioning System (IPS) for objects of daily life equipped with passive RFID tags. The goal is to provide a simple to use, yet accurate, qualitative IPS for housing enhanced with technology (sensors, effectors, etc.). With such a service, the housing, namely called smart home, could enable a wide range of services by being able to better understand the context and the current progression of activities of daily living. The paper shows that classical data mining techniques can be applied to raw data from RFID readers and passive tags. In particular, it explains how we built several datasets using a tagged object in a real smart home infrastructure. Our method was proven very effective as most algorithms result in high accuracy for the majority of the smart home.
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Bergeron, F., Bouchard, K., Gaboury, S., Giroux, S., Bouchard, B. (2016). Indoor Positioning System for Smart Homes Based on Decision Trees and Passive RFID. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9652. Springer, Cham. https://doi.org/10.1007/978-3-319-31750-2_4
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DOI: https://doi.org/10.1007/978-3-319-31750-2_4
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