Reasoning for Improved Sensor Data Interpretation in a Smart Home
This work addresses the challenge of sensor data interpretation for smart home systems, but it appears incremental as it builds on existing SSN ontology and ASP methods.
The paper tackled the problem of interpreting complex gas sensor data in smart homes by proposing an ontological representation and reasoning paradigm, resulting in inferred explanations for ambient air changes using an incremental ASP solver.
In this paper an ontological representation and reasoning paradigm has been proposed for interpretation of time-series signals. The signals come from sensors observing a smart environment. The signal chosen for the annotation process is a set of unintuitive and complex gas sensor data. The ontology of this paradigm is inspired form the SSN ontology (Semantic Sensor Network) and used for representation of both the sensor data and the contextual information. The interpretation process is mainly done by an incremental ASP solver which as input receives a logic program that is generated from the contents of the ontology. The contextual information together with high level domain knowledge given in the ontology are used to infer explanations (answer sets) for changes in the ambient air detected by the gas sensors.