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.
%0 Conference Paper
%1 strohbach2019smart
%A Strohbach, Martin
%A Saavedra, Luis Adan
%A Smirnov, Pavel
%A Legostaieva, Stefaniia
%B 2019 IEEE Global Internet of Things Summit (GIoTS) Proceedings
%D 2019
%E Skarmeta, Antonio
%I IEEE
%K iot iotcrawler myown
%R 10.1109/GIOTS.2019.8766394
%T Smart Home Crawler - Towards a framework for semi-automatic IoT sensor integration
%U https://ieeexplore.ieee.org/document/8766394
%X 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.
%@ 978-1-7281-2171-0
@inproceedings{strohbach2019smart,
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 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.},
added-at = {2019-07-16T09:31:48.000+0200},
author = {Strohbach, Martin and Saavedra, Luis Adan and Smirnov, Pavel and Legostaieva, Stefaniia},
biburl = {https://www.bibsonomy.org/bibtex/2fcfc0c2bb58744c6fde013ca605dfbad/martinstrohbach},
booktitle = {2019 IEEE Global Internet of Things Summit (GIoTS) Proceedings},
doi = {10.1109/GIOTS.2019.8766394},
editor = {Skarmeta, Antonio},
interhash = {ed4a3dca9f5b082a7931fdfb91d38156},
intrahash = {fcfc0c2bb58744c6fde013ca605dfbad},
isbn = {978-1-7281-2171-0},
keywords = {iot iotcrawler myown},
publisher = {IEEE},
timestamp = {2020-02-11T16:36:48.000+0100},
title = {Smart Home Crawler - Towards a framework for semi-automatic IoT sensor integration},
url = {https://ieeexplore.ieee.org/document/8766394},
year = 2019
}