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Belief Inference with Timed Evidence

Methodology and Application Using Sensors in a Smart Home
  • Bastien Pietropaoli
  • Michele Dominici
  • Fréedéric Weis
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 164)

Abstract

Smart Homes need to sense their environment. Augmented appliances can help doing this but sensors are also required. Then, data fusion is used to combine the gathered information. The belief functions theory is adapted for the computation of small pieces of context such as the presence of people or their posture. In our application, we can assume that a lot of sensors are immobile. Also, physical properties of Smart Homes and people can induce belief for more time than the exact moment of measures.

Thus, in this paper, we present a simple way to apply the belief functions theory to sensors and a methodology to take into account the timed evidence using the specificity of mass functions and the discounting operation. An application to presence detection in smart homes is presented as an example.

Keywords

Mass Function Data Fusion Smart Home Motion Sensor Belief Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bastien Pietropaoli
    • 1
  • Michele Dominici
    • 1
  • Fréedéric Weis
    • 2
  1. 1.INRIA, Rennes-Bretagne AtlantiqueRennes CedexFrance
  2. 2.IRISA, Université de Rennes 1Rennes CedexFrance

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