Semantically Enabling the Web of Things
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Presentation of the SSN XG results at eResearch Australia 2011 https://eresearchau.files.wordpress.com/2012/06/74-semantically-enabling-the-web-of-things-the-w3c-semantic-sensor-network-ontology.pdf
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Semantically Enabling the Web of Things
- 1. Semantically-Enabling the Web of Things: The W3C Semantic Sensor Network Ontology Laurent Lefort (presenter), Kerry Taylor and Michael Compton CSIRO ICT Centre Photo by Scott Kwasny OzFluz tower Tumbarumba, NSW (2003) © CSIRO (Photo: Gregory Heath, CLW)
- 2. The W3C SSN-XG • Chairs: • Amit Sheth, Kno.e.sis Lab, Wright State • Kerry Taylor, CSIRO • Amit Parashar -> Holger Neuhaus -> Laurent Lefort, CSIRO • Two main objectives: • (a) the development of ontologies for describing sensors, and • (b) the extension of the Sensor Model Language (SensorML), one of the four SWE languages, to support semantic annotations. End date 3 September 2010 Confidentiality Proceedings are public Initiating Members •CSIRO •Wright State •OGC Usual Meeting Schedule Teleconferences: Every week Face-to-face: Once Annually
- 3. The Semantic Sensor Network Incubator Group (SSN-XG) • SSN Ontology http://purl.oclc.org/NET/ssnx/ssn • Initial review of 17 Sensor and Observations ontologies • Group consensus (votes at meetings) on extensions • First, core concepts and relations (sensors, features and properties, observations, …), then measuring capabilities, operating and survival restrictions, and deployments, finally DOLCE-Ultralite alignment. • 41 concepts & 39 object properties, organised into ten conceptual modules. • Definitions and SKOS mappings to sources and similar definitions. • Navigable documentation on wiki auto derived http://www.w3.org/2005/Incubator/ssn/wiki/SSN • Members of the group also developed and documented examples using the ontology in their projects.
- 4. Core: sensor - stimulus - observation
- 5. SSN Ontology
- 6. 4 perspectives on sensing Sensors (capabilities) System (deployment) Observation (data) + Features & Things
- 8. Context-specific and model-specific performances 10% under- estimation 50% under- estimation World Meteorological Organisation intercomparison study of Rainfall Intensity (RI) Gauges (IOM-99_FI-RI) done in 2009.
- 9. System : parts of sensing infrastructure
- 10. Better instrument lifecycle management (data only partially accessible to end users) • CI Instrument Life Cycle Concept of Operations V 2.0 (2010) (OOI - oceanobservatories.org) Manufacture Deployment Operator Commissioning Recovery Capabilities Calibration Observation System Device Deployment Platform “Since the likely problem is a physical one and there is no immediate possibility of repair, Eta confirms that the secondary (backup) unit is working correctly, then swaps the primary and secondary Alpha systems on the Kappa mooring. Now instrument #2623 is merely providing auxiliary verification data, and Alpha instrument #2621 provides the primary stream of Alpha data for that mooring.”
- 11. Sensor data discovery Via semantic mappings (often based on RDB2RDF solutions)
- 12. Domain-specific extensions http://www.w3.org/2005/Incubator/ssn/wiki/Agriculture_Meteorology_Sensor_Network Better “vocabularies” reusable in other contexts http://www.w3.org/2005/Incubator/ssn/ssnx/meteo/aws#ImpactDisdrometer
- 13. What is it useful for?
- 14. Applications: Linked Sensor Data and Semantic sensing • (Live) Linked Sensor Data: to support large scale apps • Rel. Db to RDF mappings • Stream to RDF mappings • Semantic sensing: to use of sensor data in social media • Use of semantics to support complex event processing • SSN extension needed for Mobile Web applications like Augmented Reality
- 15. Phenonet – Microclimate Sensing for Plant Phenomics • Phenomics: Start with a particular observable trait or phenotype and work to discover the causal gene. • With the the High Resolution Plant Phenomics Centre of the Australian Plant Phenomics Facility • To examine the influence of microclimate on test plantings intended to compare the phenotype of grain varieties • To reproduce controlled lab results in the field • Photos Carl Davies, CSIRO Plant Industry and Peter Lamb CSIRO ICT Centre
- 17. Semantic sensing: from observations (attached to features) to events (attached to things) Complex Event Processing
- 18. The SSN community • SSN XG participants and adopters • CSIRO, Wright State U. (KNOESIS), DERI, UPM and University of Southampton, Open University, Fraunhofer Institute, Ericsson, Boeing, Telefonica, ETRI (Korea) plus invited experts • SemsorGrid4Env, Smart Products, SENSEI, OpenIoT, ENVISION, SPITFIRE, Planet-Data, IoT-A, EXALTED, EBBITS • Future Internet • Internet of Things • Sensor cloud • Environmental Monitoring • … • Publications (tagged bibliography) • BibBase (last update: 18 May 2011) • Mendeley group: ssn-xg-public (last update: 17 October 2011) • …
- 19. Follow-up work • Recommendations at the end of the SSN-XG final report http://www.w3.org/2005/Incubator/ssn/XGR-ssn/ • Provenance • Use of upper ontologies • APIs • Three options • Continuation of exploratory work: community group • Transition to standard development (inside W3C): Member submission or working group • Transition to standard development (outside W3C): business group • To support the adoption of solutions based on Semantic Web standards in a specific domain
- 20. Acknowledgements: Sensors & Sensor Networks Transformational Capability Platform (SSN TCP) Water for a Healthy Country flagship Special thanks to contributing group members: Payam Barnaghi, Michael Compton, Oscar Corcho, Raúl García Castro, Cory Henson, Arthur Herzog, Krzysztof Janowicz, Laurent Lefort, Holger Neuhaus, Andriy Nikolov, Kevin Page and Kerry Taylor. Acknowledgements to supporting group members: Luis Bermudez, Simon Cox, Manfred Hauswirth, Vincent Huang, W. David Kelsey, Dahn Le-Phuoc, Myriam Leggieri, Amit Parashar, Alexandre Passant, Victor Manuel Pelaez Martinez and Amit Sheth.
Hinweis der Redaktion
- March 2009 – September 2010 41 people from 16 organisations joined the group 20 attended 10 or more meetings (24 credits in report) Weekly meetings; one face-to-face (at ISWC/SSN 2009) Universities in US, Germany, Finland, Spain, Britain, Ireland Multinationals (Boeing, Ericsson) and small companies Research institutes: DERI (Ireland), Fraunhofer(Germany), ETRI (Korea), MBARI (US), SRI International (US), MITRE (US), US Defense, CTIC (Spain), CSIRO (Australia), CESI (China) http://www.w3.org/2005/Incubator/ssn/wiki/Main_Page Two main items in the charter http://www.w3.org/2005/Incubator/ssn/charter An ontology to describe sensors (the ‘SSN ontology’) Semantic markup of SWE documents
- Roughly half of the reviewed earlier work by XG participants
- A sensor can do (implements) sensing: that is, a sensor is any entity that can follow a sensing method and thus observe some Property of a FeatureOfInterest. Sensors may be physical devices, computational methods, a laboratory setup with a person following a method, or any other thing that can follow a Sensing Method to observe a Property Same as ‘sensor’ in OGC’s Sensor ML, Similar to 'observation procedure' in OGC’s O&M An Observation is a Situation in which a Sensing method has been used to estimate or calculate a value of a Property of a FeatureOfInterest. Links to Sensing and Sensor describe what made the Observation and how; links to Property and Feature detail what was sensed; the result is the output of a Sensor; other metadata gives the time(s) and the quality. Different to OGC’s O&M, in which an ‘observation’ is an act or event, although it also provides the record of the event.
- OWL2 ontology, SRIQ(D) 41 concepts & 39 object properties, organised into ten conceptual modules 117 concepts and 142 object properties in total, including DUL Aligned to DOLCE UltraLite
- Four perspectives A sensor perspective, with a focus on what senses, how it senses, and what is sensed; A data or observation perspective, with a focus on observations and related metadata; A system perspective, with a focus on systems of sensors and deployments; and, A feature and property perspective, focusing on what senses a particular property or what observations have been made about a property. World Meteorological Organisation (2009) Intercomparison study of Rainfall Intensity (RI) Gauges (IOM-99_FI-RI)
- Collects together measurement properties (accuracy, range, precision, etc) and the environmental conditions in which those properties hold, representing a specification of a sensor's capability in those conditions. The conditions specified here are those that affect the measurement properties, while those in OperatingRange (of a System) represent the sensor's standard operating conditions, including conditions that don't affect the observations. MeasurementCapabilities are properties – they are observable aspects of a sensor. So we have an observable aspect of a sensors environment (the conditions) being used together with an observable aspect of a sensor to specify these.
- Why a Sensor Ontology? Use data from two precipitation sensors same function, different principles important or not important? The answer is in the World Meteorological Organisation intercomparison study of Rainfall Intensity (RI) Gauges (IOM-99_FI-RI) done in 2009. They have different underestimation thresholds for high rainfall events Vaisala: 801 to 2002 mm/hr RIMCO: 3001 to 5002 mm/hr 1 WMO IOM-99_FI-RI 2 Manufacturer sheet Issue: Basic information about the type or model of sensors is often missing. Knowing the sensor which has been used and its underestimation threshold is a critical input for the analysis of the frequency or severity of extreme weather events.
- 1. Manufacture .....................................................................................................................1-1 1.1 Build .............................................................................................................................1.1-1 1.2 Calibration/Test ............................................................................................................1.2-1 2. Operator Commissioning ...............................................................................................2-2 2.1 Acquisition and Logistics ..............................................................................................2.1-2 2.2 Configuration, Calibration, and Test.............................................................................2.2-4 3. Deployment ......................................................................................................................3-5 3.1 Installation, Network/System Connection and Registration..........................................3.1-6 3.2 Command and Control .................................................................................................3.2-7 3.3 Data Generation ...........................................................................................................3.3-8 3.4 Failure Detection, Diagnosis and Repair ......................................................................3.4-9 4. Recovery ........................................................................................................................4-11 4.1 Turn-Off and Removal................................................................................................4.1-11 4.2 Decommissioning .......................................................................................................4.2-11 4.3 Disposal............
- Possibly not at the same level of details
- (e.g. http://lsm.deri.ie/ )
- Important for the transfer of information between users with different needs Sensir manufacturer to Instrumentation specialist to Data users
- Maintenance and tooling: W3C Community Group (open source, non for profit, tied to research projecvts) Transition to W3C standard: W3C Member submission or working group Transition/Linkage with other standard development efforts or for a particular domain: W3C Business Group