A machine-to-machine architecture to merge semantic sensor measurements

Amelie Gyrard 1 Christian Bonnet 2 Karima Boudaoud 3
2 Mobile Communication
Eurecom [Sophia Antipolis]
3 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe RAINBOW
Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : The emerging eld Machine-to-Machine (M2M) enables machines to communicate with each other without human intervention. Existing semantic sensor networks are domainspeci c and add semantics to the context. We design a Machine-to-Machine (M2M) architecture to merge heterogeneous sensor networks and we propose to add semantics to the measured data rather than to the context. This architecture enables to: (1) get sensor measurements, (2) enrich sensor measurements with semantic web technologies, domain ontologies and the Link Open Data, and (3) reason on these semantic measurements with semantic tools, machine learning algorithms and recommender systems to provide promising applications.
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Amelie Gyrard, Christian Bonnet, Karima Boudaoud. A machine-to-machine architecture to merge semantic sensor measurements. 22nd International World Wide Web Conference, Brazil, May 13-17, 2013, Companion Volume, May 2013, Rio de Janeiro, Brazil. pp.371-376. ⟨hal-00927389⟩

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