The emerging field Machine-to-Machine (M2M) enables machines to communicate with each other without human intervention. Existing semantic sensor networks are domain specific 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.
A machine-to-machine architecture to merge semantic sensor measurements
WWW 2013, 22nd International World Wide Web Conference, Doctoral Consortium, May 13-17, 2013, Rio de Janeiro, Brazil
Rio de Janeiro
Systèmes de Communication
© ACM, 2013. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in WWW 2013, 22nd International World Wide Web Conference, Doctoral Consortium, May 13-17, 2013, Rio de Janeiro, Brazil http://dx.doi.org/10.1145/2487788.2487945
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