Sensor Discovery on Linked Data
Sensor Discovery on Linked Data
This example shows how the SSN Ontology can be applied to extend the work done at the Ohio Center of Excellence in Knowledge Enabled Computing (Kno.e.sis) on sensor discovery, linked sensor data and sensor data provenance.
There has been a drive recently to make sensor data accessible on the Web. However, because of the vast number of environmental sensors, finding relevant sensors on the Web is a non-trivial challenge.
Figure 5.46 presents an application developed by Kno.e.sis ([Pschorr et al. 2010]) where users can type in a location of interest in the search box provided at the top of the page. The application provides an auto suggest list of locations that have sensors nearby. Once the user clicks on a location, all the sensors located nearby are displayed on a map. Users can click to get more information about the sensor, such as the sensor capabilities, coordinates and also current weather observations.
This approach for discovering sensors uses a standard service interface to query Linked Sensor Data, a semantic sensor network middleware that includes a sensor registry and a sensor discovery service that extends the OGC Sensor Web Enablement.
The application gives access to the following datasets sourced from MesoWest, a project within the Department of Meterology at the University of Utah that has been aggregating weather data since 2002:
- MesoWest/Kno.e.sis Sensor Observation Dataset - This RDF dataset contains expressive descriptions of hurricane and blizzard observations in the United States. observations collected include measurements of phenomena such as temperature, visibility, precipitation, pressure, wind speed, humidity, etc. The dataset includes observations within the entire United States during the time periods that several major storms were active -- including Hurricane Katrina, Ike, Bill, Bertha, Wilma, Charley, Gustav, and a major blizzard in Nevada in 2002. These observations are generated by weather stations described in the MesoWest/Kno.e.sis Sensor Description Dataset introduced below. Currently, this dataset contains around one-billion triples.
- MesoWest/Kno.e.sis Sensor Description Dataset - This RDF dataset, originated at MesoWest contains expressive descriptions of ~20,000 weather stations in the United States. On average, there are about five sensors per weather station measuring phenomena such as temperature, visibility, precipitation, pressure, wind speed, humidity, etc. In addition to location attributes such as latitude, longitude, and elevation, there are also links to locations in Geonames that are near each weather station. This sensors description dataset is now part of the LOD.
Linking Sensor Data to other resources of the LOD cloud
This intuitive discovery is possible by linking concepts from Linked Sensor Data knowledge base to concepts in GeoNames knowledge base. This linking of concepts is a fundamental principle of Linked Data. A number of government, corporate, and academic organizations are collecting enormous amounts of data provided by environmental sensors. However, this data is too often locked within organizations and underutilized by the greater community. This is accomplished by converting raw sensor observations to RDF and linking with other datasets on LOD. With such a framework, organizations can make large amounts of sensor data openly accessible, thus allowing greater opportunity for utilization and analysis.
The Linked Sensor Data resource produced by Kno.e.sis enables sensor data to be openly accessible on the Linked Open Data (LOD) Cloud. Now available through the Comprehensive Knowledge Archive Network, this resource contains expressive descriptions of ~20,000 weather stations in the United States with more than 18000 links to GeoNames places as shown in Figure 5.47.
The DUL:hasLocation relationship should be used to define the links between sensor sites and geographical places. Figure 5.48 shows how the link between the AGTC1_HMP50 weather station and its Geonames location can be implemented.
Figure 5.49 shows the rest of this example of weather station defined with the SSN ontology (OWL File).
Provenance tracking in the Linked Sensor Data cloud
The development of the SSN Ontology, its alignment with DUL and its use in conjunction with external ontologies will foster new opportunities to link sensor data to other resources of the LOD cloud like DbPedia. It will also support the ongoing efforts to capture the provenance of sensor data.
Provenance, from the French word 'provenir', describes the lineage or history of a data entity. Provenance is critical information in the sensors domain to identify a sensor and analyze the observation data over time and geographical space. In this paper, we present a framework to model and query the provenance information associated with the sensor data exposed as part of the Web of Data using the Linked Open Data conventions. This is accomplished by developing an ontology-driven provenance management infrastructure that includes a representation model and query infrastructure. This provenance infrastructure ([Patni et al. 2010b]), called Sensor Provenance Management System (PMS), is underpinned by a domain specific provenance ontology called Sensor Provenance (SP) ontology.