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

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.