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An Architecture for Query, Insertion, and Discovery For 1 1 SemSOS : an Architeure for Query, Insertion, and Discovery for Semantic Sensor Networks A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science By Joshua Kenneth Pschorr B.S., Eastern Kentucky University, 2004 2013 Wright State University 1 1 2 2 COPYRIGHT Joshua Kenneth Pschorr 2013 2 2 3 3 WRIGHT STATE UNIVERSITY SCHOOL OF GRADUATE STUDIES May 14, 2013 I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPERVISION BY Joshua Kenneth Pschorr ENTITLED SemSOS : an Architeure for Query, Insertion, and Discovery for Semantic Sensor Networks BE ACCEPTED IN PARTIAL FULFILL- MENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Science. Krishnaprasad Thirunarayan, Ph.D. Thesis Direor Mateen Rizki, Ph.D., Chair Department of Computer Science College of Engineering and Computer Science Committee on Final Examination Krishnaprasad Thirunarayan, Ph.D. Amit P. Sheth, Ph.D. Bin Wang, Ph.D. R. William Ayres, Ph.D. Interim Dean, Graduate School 3 3 4 4 AbstraAbstra Pschorr, Joshua Kenneth M.S., Department of Computer Science and Engineering, Wright State University, 2013. SemSOS : an Architeure for Query, Insertion, and Discovery for Semantic Sensor Networks. With sensors, storage, and bandwidth becoming ever cheaper, there has been a drive recently to make sensor data accessible on the Web. However, because of the vast num- ber of sensors collecting data about our environment, finding relevant sensors on the Web and then interpreting their observations is a non-trivial challenge. The Open Geospa- tial Consortium (OGC) defines a web service specification known as the Sensor Obser- vation Service (SOS) that is designed to standardize the way sensors and sensor data are discovered and accessed on the Web. Though this standard goes a long way in providing interoperability between sensor data producers and consumers, it is predicated on the idea that the consuming application is equipped to handle raw sensor data. Sensor data consuming end-points are generally interested in not just the raw data itself, but rather actionable information regarding their environment. The approaches for dealing with this are either to make each individual consuming application smarter or to make the data served to them smarter. This thesis presents an application of the latter approach, which is accomplished by providing a more meaningful representation of sensor data by leveraging semantic web technologies. Specifically, this thesis describes an approach to sensor data modeling, reasoning, discovery, and query over richer semantic data derived from raw sensor descriptions and observations. iv 4 4 5 5 The artifacts resulting from this research include: an implementation of an SOS service which hews to both Sensor Web and Se- mantic Web standards in order to bridge the gap between syntactic and semantic • sensor data consumers and that has been proven by use in a number of research applications storing large amounts of data, which serves as an example of an approach for designing applications which integrate syntactic services over semantic models and allow for interactions with external reasoning • systems. As more sensors and observations move online and as the Internet of Things becomes a reality, issues of integration of sensor data into our everyday lives will become impor- tant for all of us. The research represented by this thesis explores this problem space and presents an approach to dealing with many of these issues. Going forward, this research may prove a useful elucidation of the design considerations and affordances which can allow low-level sensor and observation data to become the basis for machine processable knowledge of our environment. v 5 5 6 6 TableTable of of Contents Contents Abstract . iv Table of Contents . vi List of Figures . x List of Tables . xi List of Listings . xii Acknowledgement . xiv 1 Introduction . 1 2 Motivation . 6 3 Related Work . 8 4 Background . 11 4.1 Sensor Web . 11 4.1.1 Sensor Web Enablement . 12 4.1.1.1 O&M . 13 vi 6 6 7 7 4.1.1.2 SensorML . 14 4.1.1.3 Sensor Observation Service . 15 4.2 Semantic Web . 18 4.2.1 Resource Description Framework . 19 4.2.2 RDF Schema . 22 4.2.3 Web Ontology Language . 22 4.2.4 SPARQL Protocol and RDF Query Language . 23 4.2.5 Linked Data . 24 4.3 Ontologies . 25 4.3.1 Upper . 25 4.3.1.1 DUL . 26 4.3.2 Semantic Sensor Network Ontology . 27 4.3.3 Spatio-temporal . 28 4.3.3.1 OWL-Time . 29 4.3.3.2 WGS84 . 29 4.3.4 Thematic . 29 4.3.4.1 MUO UCUM . 30 4.3.4.2 GeoNames . 30 vii 7 7 8 8 5 Architecture . 32 5.1 Overview . 32 5.2 Knowledge Base . 34 5.2.1 Organization . 35 5.2.2 Data Model . 37 5.3 Document Annotation . 38 5.4 SemSOS Extensions . 39 5.4.1 Operations . 42 5.4.1.1 RegisterSensor . 44 5.4.1.2 DescribeSensor . 50 5.4.1.3 InsertObservation . 53 5.4.1.4 GetCapabilities . 57 5.4.1.5 GetObservation . 61 5.4.1.6 RefreshMetadata . 69 5.5 Clients . 69 5.6 Reasoner Interaction . 71 viii 8 8 9 9 6 Applications & Practical Impact . 75 6.1 Reasoning about Weather Data . 75 6.2 Linked Sensor Data . 77 6.3 Discovery on Linked Sensor Data . 78 6.4 SemMOB . 80 6.5 Practical Impact . 80 7 Future Work . 82 8 Conclusion . 84 References . 85 A SPARQL Templates . 89 B Example Reasoner . ..
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