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J. Sens. Actuator Netw. 2013, 2(2), 172-195; doi:10.3390/jsan2020172

Semantic Models for Scalable Search in the Internet of Things

Institute of Computer Engineering, University of Lübeck, 23562 Lübeck, Germany
Institute of Information Systems, University of Lübeck, 23562 Lübeck, Germany
Institute of Telematics, University of Lübeck, 23562 Lübeck, Germany
Author to whom correspondence should be addressed.
Received: 31 January 2013 / Revised: 5 March 2013 / Accepted: 14 March 2013 / Published: 27 March 2013
(This article belongs to the Special Issue Feature Papers)


The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and services in the Internet to realize novel applications such as Smart Cities, Smart Grids, or Smart Healthcare. This requires an open representation of sensor data and scalable search over data from diverse sources including sensors. In this paper we show how the Semantic Web technologies RDF (an open semantic data format) and SPARQL (a query language for RDF-encoded data) can be used to address those challenges. In particular, we describe how prediction models can be employed for scalable sensor search, how these prediction models can be encoded as RDF, and how the models can be queried by means of SPARQL.
Keywords: Internet of Things; searching; sensors; probability models Internet of Things; searching; sensors; probability models

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Mietz, R.; Groppe, S.; Römer, K.; Pfisterer, D. Semantic Models for Scalable Search in the Internet of Things. J. Sens. Actuator Netw. 2013, 2, 172-195.

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