Adapting Semantic Sensor Networks for Smart Building Diagnosis

The Internet of Things is one of the next big changes in which devices, objects, and sensors are getting linked to the semantic web. However, the increasing availability of generated data leads to new integration problems. In this paper we present an architecture and approach that illustrates how semantic sensor networks, semantic web technologies, and reasoning can help in real-world applications to automatically derive complex models for analytics tasks such as prediction and diagnostics. We demonstrate our approach for buildings and their numerous connected sensors and show how our semantic framework allows us to detect and diagnose abnormal building behavior. This can lead to not only an increase of occupant well-being but also to a reduction of energy use. Given that buildings consume 40% of the world's energy use we therefore also make a contribution towards global sustainability. The experimental evaluation shows the benefits of our approach for buildings at IBM's Technology Campus in Dublin.