Enriching sensor data processing with quality semantics

Sensors and their observations are used in almost all pervasive applications to configure and adjust the behavior of applications to the user´s need. However, the quality of those sensor observations are influenced by different factors including the physical or chemical principle of measurement, the internal processing of the sensing device, and the prevailing environmental conditions at the time of measurement. Thus, it is of great interest to not just measure and process the sensor observation but to also handle the quality of the sensor observation correctly and propagate the quality of the observation along the processing path to the user. To do so, we use the Semantic Sensor Network Ontology (SSN) to combine necessary sensor observations from multiple sources in a Probabilistic Data Stream Management System (PDSMS) to estimate prevailing conditions and propagate current quality information.