Building Heterogeneous Models at Runtime to Detect Faults in Ambient-Intelligent Environments

Christophe Jacquet (1) , Ahmed Mohamed (1) , Frédéric Boulanger (1) , Cécile Hardebolle (1) , Yacine Bellik (2)

1 E3S - Supélec Sciences des Systèmes
2 LIMSI - Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur

This paper introduces an approach for fault detection in ambient-intelligent environments. It proposes to compute predictions for sensor values, to be compared with actual values. As ambient environments are highly dynamic, one cannot pre-determine a prediction method. Therefore, our approach relies on (a) the modeling of sensors, actuators and physical effects that link them, and (b) the automatic construction at run-time of a heterogeneous prediction model. The prediction model can then be executed on a heterogeneous modeling platform such as ModHel'X, which yields predicted sensor values.

Keywords: ambient intelligence; Heterogeneous modeling; models of computation; [e-mail]

Domains: Software Engineering [cs.SE]

Christophe Jacquet, Ahmed Mohamed, Frédéric Boulanger, Cécile Hardebolle, Yacine Bellik. Building Heterogeneous Models at Runtime to Detect Faults in Ambient-Intelligent Environments. MRT 2013, Sep 2013, Miami, United States. pp.52-63. ⟨hal-00905275⟩