Name
Abstract | ||
|---|---|---|
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 time-consuming 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 domain-specific crawling component for IoTCrawler, a secure and privacy-preserving search engine for the Internet of Things. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GIOTS.2019.8766394 | 2019 Global IoT Summit (GIoTS) |
Keywords | DocType | ISSN |
Internet of Things,Search Engines,Smart Home,Data Integration,Machine Learning | Conference | In 2019 Global IoT Summit (GIoTS) (pp. 1-6). IEEE 2019 |
ISBN | Citations | PageRank |
978-1-7281-2172-7 | 0 | 0.34 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Strohbach | 1 | 224 | 31.60 |
| Luis Adan Saavedra | 2 | 0 | 0.34 |
| Pavel Smirnov | 3 | 0 | 0.34 |
| Stefaniia Legostaieva | 4 | 0 | 0.34 |