SHORE: a model-driven approach that combines goal, semantic and variability models for Smart HOme self-REconfiguration - Archive ouverte HAL Access content directly
Conference Papers Year : 2022

SHORE: a model-driven approach that combines goal, semantic and variability models for Smart HOme self-REconfiguration

Abstract

Smart Homes, and more generally the Internet of Things (IoT), are gaining more and more audience, but this kind of environment is still challenging on several aspects. Semantic representations, mainly ontologies, have been used to cope with the complexity and lack of interoperability due to the wide variety of objects and services. However, these semantic representations do not permit a fine description of variability and evolution to achieve particular objectives. Therefore, in this paper, we introduce a model-driven approach, so-called SHORE for Smart HOme self-REconfiguration, which addresses these limitations and covers the complexity and variability of Smart Homes allowing automated reconfiguration guided by their goals. Thus, SHORE includes artifacts, based on goal, semantic and feature models, that are used at design-time to conceptualise Smart Homes, their variants and goals. Whilst, at runtime, SHORE exploits these artifacts to detect situations that require reconfiguration, i.e., detect deviations from their goals, and then to calculate optimal reconfiguration plans to cope with these situations.
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hal-03610122 , version 1 (24-03-2023)

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Denisse Muñante, Bruno Traverson, Sophie Chabridon, Amel Bouzeghoub. SHORE: a model-driven approach that combines goal, semantic and variability models for Smart HOme self-REconfiguration. MODELSWARD 2022: 10th international conference on Model-Driven Engineering and Software Development, Feb 2022, Online, France. pp.328-335, ⟨10.5220/0010907300003119⟩. ⟨hal-03610122⟩
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