Time-Probability Dependent Knowledge Extraction in IoT-enabled Smart Building

Smart buildings incorporate various emerging Internet of Things (loT) applications for comprehensive management of energy efficiency, human comfort, automation and security. However, the development of a knowledge extraction framework for human activities is fundamental. Currently, there is a lack of a unified and practical framework for modeling heterogeneous sensor data within buildings. In this paper, we propose a practical inference framework for extracting status-to-event knowledge within smart building. Our proposal includes IoT-based API integration, ontology model design, and time probability dependent knowledge extraction methods. We leveraged the Building Topology Ontology (BOT) to construct spatial relations among sensors and spaces within the building. Additionally, we utilized Apache Jena Fuseki's SPARQL server for storing and querying RDF triple data. Two types of knowledge could be extracted: timestamp-based probability for abnormal event detection and time interval-based probability for conjunction of multiple events. We conducted experiments over a 78-day period in a real smart building environment, collecting data on light and elevator states for evaluation. The evaluation revealed several inferred events, such as room occupancy, elevator trajectory tracking, and the conjunction of both events. The numerical values of detected event counts and probability demonstrate the potential for automatic control in the smart building.