Indoor Positioning System for Smart Homes Based on Decision Trees and Passive RFID View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Frédéric Bergeron , Kevin Bouchard , Sébastien Gaboury , Sylvain Giroux , Bruno Bouchard

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. More... »

PAGES

42-53

References to SciGraph publications

  • 2005. On the Complexity of Probabilistic Inference in Singly Connected Bayesian Networks in ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2004-11. LANDMARC: Indoor Location Sensing Using Active RFID in WIRELESS NETWORKS
  • 2005-05. Logistic Model Trees in MACHINE LEARNING
  • Book

    TITLE

    Advances in Knowledge Discovery and Data Mining

    ISBN

    978-3-319-31749-6
    978-3-319-31750-2

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-319-31750-2_4

    DOI

    http://dx.doi.org/10.1007/978-3-319-31750-2_4

    DIMENSIONS

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