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.
翻译：智能之家的传感器部署对于诸如家庭自动化、家庭安全或能源消耗意识和减少等应用已长期具有商业意义,然而,由于传感器装置和网关的多样性,数据整合仍是一个耗时费钱的过程,在本文件中,我们提议“智能家捕捉者框架”(1) 从基本传感器和网关技术中提供一个共同的语义抽象,(2) 通过应用机器学习技术将已发现装置与语义数据模型联系起来,加速新装置的整合。我们提出了第一个原型,在2018年信通技术上展示了这一原型。原型是IoTCrawler的地域特定爬行部件,它是物联网的安全和隐私保护搜索引擎。
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