Spatio-Temporal normalization of data from heterogeneous sensors

The growing use of sensors in smart environments applications like smart homes, hospitals, public transportation, emergency services, education, and workplaces not only generates constantly increasing of sensor data, but also rises the complexity of integration of heterogeneous data and hardware devices. In order to get more accurate and consistent information on real world events, heterogeneous sensor data should be normalized. The paper proposes a set of architectural abstractions aimed at representing sensors' measurements that are independent from the sensors' technology. Such a set can reduce the effort for data fusion and interpretation. The abstractions allow to represent raw sensor readings by means of spatio-temporal contextualized events.