Enrich machine-to-machine data with semantic web technologies for cross-domain applications

TLDR

The Internet of Things’ Machine‑to‑Machine devices generate domain‑specific data, but combining heterogeneous data across domains is difficult because each application uses its own device and measurement descriptions, making it hard to understand the data’s meaning. The authors propose a semantic‑based approach to automatically combine, enrich, and reason about M2M data to enable cross‑domain applications. Their method employs semantic web technologies to integrate and enrich M2M data, allowing automated reasoning across domains. A proof‑of‑concept demonstrating this approach is available online at http://sensormeasurement.appspot.com/.

Abstract

The Internet of Things, more specifically, the Machine-to-Machine (M2M) standard enables machines and devices such as sensors to communicate with each other without human intervention. The M2M devices provide a great deal of M2M data, mainly used for specific M2M applications such as weather forecasting, healthcare or building automation. Existing applications are domain-specific and use their own descriptions of devices and measurements. A major challenge is to combine M2M data provided by these heterogeneous domains and by different projects. It is really a difficult task to understand the meaning of the M2M data to later reason about them. We propose a semantic-based approach to automatically combine, enrich and reason about M2M data to provide promising cross-domain M2M applications. A proof-of-concept to validate our approach is published online (http://sensormeasurement.appspot.com/).