Semantic Content Enrichment of Sensor Network Data for Environmental Monitoring

The Semantic Sensor Web (SSW) will eventually revolutionize how we perceive and query information about the physical world. Currently, there is an ongoing effort to develop a searchable web of things that sense and control the world. As this new internet of things expands, there will be an explosion of available raw data that may not always be reachable by users. Bridging this gap between what the user wants and the information collected and represented by embedded devices is a critical issue. In order to really maximize the benefit of such a web of networked sensing devices, new semantic approaches that can infer and predict additional information about the sensors and their context need to be developed. This paper proposes a content enrichment approach that uses sensor and context data as features to predict new meta-tags that can further identify relevant categorizations for the embedded devices and the physical data they collect. Specifically, machine learning classification and regression techniques are used to predict semantic tags for each embedded system context. Results of the 10-fold cross validation analysis and feature ranking are presented and discussed.