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A Linked-data Model for Semantic Sensor Streams

Barnaghi, P, Wang, W, Dong, L and Wang, C (2013) A Linked-data Model for Semantic Sensor Streams In: IEEE International Conference on Internet of Things (iThings 2013), 2013-08-20 - 2013-08-23, Beijing, China.

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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.

Item Type: Conference or Workshop Item (Conference Paper)
Authors :
AuthorsEmailORCID
Barnaghi, PUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Dong, LUNSPECIFIEDUNSPECIFIED
Wang, CUNSPECIFIEDUNSPECIFIED
Date : 23 August 2013
Identification Number : 10.1109/GreenCom-iThings-CPSCom.2013.95
Additional Information : © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Depositing User : Symplectic Elements
Date Deposited : 25 Aug 2015 15:44
Last Modified : 25 Aug 2015 15:44
URI: http://epubs.surrey.ac.uk/id/eprint/787066

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