Smart Home Crawler: Towards a framework for semi-automatic IoT sensor integration
This addresses the data integration challenge for smart home applications like automation and energy management, but it appears incremental as it builds on existing IoTCrawler technology.
The paper tackles the costly and time-consuming data integration problem in smart homes due to heterogeneous sensor devices and gateways by proposing the Smart Home Crawler Framework, which provides a common semantic abstraction and uses machine learning to accelerate device integration, with a prototype demonstrated at ICT 2018.
Sensor deployments in Smart Homes have long reached commercial relevance for applications such as home automation, home safety or energy consumption awareness and reduction. Nevertheless, due to the heterogeneity of sensor devices and gateways, data integration is still a costly and timeconsuming process. In this paper we propose the Smart Home Crawler Framework that (1) provides a common semantic abstraction from the underlying sensor and gateway technologies, and (2) accelerates the integration of new devices by applying machine learning techniques for linking discovered devices to a semantic data model. We present a first prototype that was demonstrated at ICT 2018. The prototype was built as a domainspecific crawling component for IoTCrawler, a secure and privacy-preserving search engine for the Internet of Things.