Title :
A Linked-Data Model for Semantic Sensor Streams
Author :
Barnaghi, Payam ; Wei Wang ; Lijun Dong ; Chonggang Wang
Author_Institution :
Centre for Commun. Syst. Res., Univ. of Surrey, Guildford, UK
Abstract :
This paper describes a semantic modelling scheme, a naming convention and a data distribution mechanism for sensor streams. The proposed solutions address important challenges to deal with large-scale sensor data emerging from the Internet of Things resources. While there are significant numbers of recent work on semantic sensor networks, semantic annotation and representation frameworks, there has been less focus on creating efficient and flexible schemes to describe the sensor streams and the observation and measurement data provided via these streams and to name and resolve the requests to these data. We present our semantic model to describe the sensor streams, demonstrate an annotation and data distribution framework and evaluate our solutions with a set of sample datasets. The results show that our proposed solutions can scale for large number of sensor streams with different types of data and various attributes.
Keywords :
Internet of Things; data models; semantic Web; wireless sensor networks; Internet of Things resources; data distribution framework; data distribution mechanism; large-scale sensor data; linked-data model; naming convention; semantic annotation; semantic modelling scheme; semantic representation frameworks; semantic sensor networks; semantic sensor streams; Adaptation models; Data models; Distributed databases; Internet; Ontologies; Semantics; Temperature measurement; Internet of Things; Linked-data; Semantic Streams; Sensor Data Modelling; Stream Processing;
Conference_Titel :
Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing
Conference_Location :
Beijing
DOI :
10.1109/GreenCom-iThings-CPSCom.2013.95