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

TL;DR

This paper presents a practical framework for extracting temporal and probabilistic knowledge from heterogeneous IoT sensor data in smart buildings, enabling improved automation and event detection.

Contribution

It introduces a unified inference framework using ontology and SPARQL for modeling and querying sensor data, including novel time-probability based event detection methods.

Findings

Successful detection of room occupancy and elevator trajectories

Probabilistic inference of combined events like occupancy and elevator movement

Demonstrated effectiveness over a 78-day real-world deployment

Abstract

Smart buildings incorporate various emerging Internet of Things (IoT) applications for comprehensive management of energy efficiency, human comfort, automation, and security. However, the development of a knowledge extraction framework 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. The Building Topology Ontology (BOT) was leveraged to construct spatial relations among sensors and spaces within the building. We utilized Apache Jena Fuseki's SPARQL server for storing and querying the 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. The data of light and elevator states has been collected 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.