Towards Adaptive Sensor Data Quality Improvement based on Context Models
Aboubakr Benabbas, Simon Steuer, Daniela Nicklas · 2020
Pervasive applications use context information for decision making and to adapt to new circumstances at run time. We can derive this context from sensors (dynamic) or from the deployment information (static). Some applications rely on a combination of context information sources to evaluate the quality of the data received. Besides, different context sources enable the applications to adjust to changes in the configuration as soon as they happen. The possible adaptation can include a change in the data processing to incorporate data quality improvement approach to provide a better context to the application. In this paper, we offer an adaptive data quality improvement based on a combination of context sources that we model using a domain-specific ontology.