DocumentCode
245767
Title
Modeling Geospatial Sensor Knowledge under a Semantic Sensor Web Environment
Author
Xiaoliang Meng ; Yuwei Wang ; Yunhao Wu
Author_Institution
Int. Sch. of Software, Wuhan Univ., Wuhan, China
fYear
2014
fDate
19-21 Dec. 2014
Firstpage
1090
Lastpage
1095
Abstract
In recent years, developments in Internet of things and ubiquitous computing have created new opportunities to promote sensors participation in environmental, military and personalized monitoring. Up to now, sensor networks have been proposed for various applications, which are however characterized by a lack of interoperability. In this context, the paper gives a survey on semantic sensor web, and proposes a general method for modeling geospatial sensor knowledge by adding semantic annotations and rule reasoning to the Sensor ML (Sensor Model Language) described sensor information within OGC (Open Geospatial Consortium) SWE (Sensor Web Enablement) framework. We describe the air quality monitoring instance which gives a solution for automatically reporting the health implications and cautionary statements to the nearby people. This work constitutes an essential step towards the geospatial knowledge modeling of interoperable and intelligent sensor networks.
Keywords
Internet of Things; open systems; semantic Web; sensors; Internet of Things; OGC; Open Geospatial Consortium; SWE framework; geospatial knowledge modeling; geospatial sensor knowledge; intelligent sensor networks; interoperability; personalized monitoring; rule reasoning; semantic annotations; semantic sensor Web environment; sensor ML; sensor Web enablement framework; sensor information; sensor model language; ubiquitous computing; Atmospheric modeling; Cognition; Geospatial analysis; Monitoring; Ontologies; Semantics; Sensor ML; geospatial sensor knowledge; ontology; semantic; sensor web;
fLanguage
English
Publisher
ieee
Conference_Titel
Computational Science and Engineering (CSE), 2014 IEEE 17th International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4799-7980-6
Type
conf
DOI
10.1109/CSE.2014.215
Filename
7023725
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