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

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.
Type de document :
Communication dans un congrès
22nd International World Wide Web Conference, Brazil, May 13-17, 2013, Companion Volume, May 2013, Rio de Janeiro, Brazil. pp.371-376, 2013
Liste complète des métadonnées


https://hal.archives-ouvertes.fr/hal-00927389
Contributeur : Amelie Gyrard <>
Soumis le : mardi 14 janvier 2014 - 13:31:36
Dernière modification le : mercredi 31 août 2016 - 11:41:10
Document(s) archivé(s) le : mardi 15 avril 2014 - 16:19:25

Fichier

doc03a-gyrard.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00927389, version 1

Collections

Citation

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, 2013. <hal-00927389>

Partager

Métriques

Consultations de
la notice

278

Téléchargements du document

352