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Developing multi-functional “sensor” web service platform for citizen sensing
This document discusses the development of a multi-functional "sensor" web service platform for citizen sensing called CITI-SENSE. The project aims to develop citizens' observatories, empower citizens to contribute environmental data, and support decision making. Three prototypes are being developed for air quality, noise, and indoor air quality monitoring using sensor data from 9 locations in Europe. Lessons learned include understanding user requirements, choosing standards to meet them, and that INSPIRE data specifications allowed quick development.
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Developing multi-functional “sensor” web service platform for citizen sensing
- 1. www.snowflakesoftware.com Developing multi-functional “sensor” webservice platform for citizen sensing Richard Rombouts Snowflake Software Ltd. United Kingdom
- 2. www.snowflakesoftware.com CITI-SENSE • EU FP7-ENV-2012project • 4 years, started October 2012 • 27 project partners from Europe, South Korea and Australia • Budget > €12mil www.citi-sense.eu
- 3. www.snowflakesoftware.com Objectives • Develop citizens’observatories • Empower citizens to contribute and participate in environmental governance • Support and influence decision making progress
- 4. www.snowflakesoftware.com Concept Standards for GeospatialData and Services Citizens’ Observatories Participation and Empowerment Information Products and Services Data and Services Platform Sensor Infrastructure Urban Quality Public Spaces School Indoor Quality
- 5. www.snowflakesoftware.com No-SQL Database No-SQL Database Mobile Sensors & Apps TripleStoreTriple Store senMLsenML RDFRDF Sensor Application Platform Spatial Data Services Platform Linked Data Platform
- 6. www.snowflakesoftware.com Prototypes • Three end-to-endprototypes are developed: 1. Air Quality 2. Noise 3. Indoor air at schools • 9 locations in Europe
- 7. www.snowflakesoftware.com Air Quality Prototype(6 Mth) DATA ACCESS PROTOCOLS PostgreSQL Cloud Database ClusterPostgreSQL Cloud Database Cluster Model Transformation / Data Fusion / Semantic Annotation Model Transformation / Data Fusion / Semantic Annotation SENSOR Data SENSOR Data OGC WFS OGC WFS
- 8. www.snowflakesoftware.com Configuring the DataServices • Publishing from single source to multiple schemas: – OGC SWE: SensorML, O&M, KML – INSPIRE: EMF, O&M Profiles – W3C: SenML • Using off-the-shelf software • Rapid configuration/deployment of data services to meet specific user requirements
- 9. www.snowflakesoftware.com Deploying Data Services •Once schema transformation configured – project is deployed within WFS • But first need to configure WFS settings: – GetCapabilities – Encoding format (compressed/uncompressed) – Servlet pattern • Finally, generate WFS war and deploy to application server
- 10. www.snowflakesoftware.com Deploying Data Services Desktop Server Translation configuration DataRequest SchemaSchema translationtranslation SchemaSchema translationtranslation Data Request SchemaSchema translationtranslation SchemaSchema translationtranslation Database Records Database Records SQL Query SQL Query Database table information
- 11. www.snowflakesoftware.com Experiences Observations and Measurements: Howis this described/encoded? How is this described/encoded? How do you describe a collection of observable properties Where are these defined?
- 12.
- 13. www.snowflakesoftware.com Experiences Encoding PointTimeSeriesObservation – O&Mdescribes a collection of observations – Result cannot be dynamically generated – Need to have a really good understanding of request patterns to configure service: • Get latest observation • Get last 24 hrs • Get last 7 days • Get last month Issue: O&M does not support request pattern: •Get observations within a user defined time period for a subset of air quality parameters
- 14. www.snowflakesoftware.com Experiences Encoding PointTimeSeriesObservation – O&Mdescribes a collection of observations – Result cannot be dynamically generated – Need to have a really good understanding of request patterns to configure service: • Get latest observation • Get last 24 hrs • Get last 7 days • Get last month Resolution: Encoded the observations as separate features using senML which is a lightweight encoding to serve through WFS
- 15. www.snowflakesoftware.com Lessons learnt • Youneed to understand your end-user requirements before developing your services • Choose the right set of standards for meeting these requirements • INSPIRE Data Specifications allowed us to get going within weeks of project start!!
- 16. www.snowflakesoftware.com Richard Rombouts richard.rombouts@snowflakesoftware.com @RichRombouts ;@Sflakesoftware Learn more? Talk to us
Editor's Notes
- #4 CITI-SENSE will develop citizens’ observatories to empower citizens to contribute and participate in environmental governance, to enable them to support and influence community and societal priorities and associated decision making. CITI-SENSE will develop, test, demonstrate and validate a community-based environmental monitoring and information system using innovative and novel earth observation applications. The project aims to learn from citizen experience and perception and enable citizenship co-participation in community decision making and co-operative planning.
- #5 The concept of the project is to realise the chain from sensors->platforms->products->users. Elements Sensors: Technologies for distributed monitoring Platform: Information and communication technologies Products: Information products and services Users: Citizen involvement in monitoring and societal decisions
- #8 We are polling the AirBase database every 15 mins and load the sensor data directly into our PostGIS database cluster (managed by EnterpriseDB). On top of this database we used GO Publisher to develop and deploy various data services such as INSPIRE Environmental Monitoring Facilities (EMF), Observations & Measurements (O&M) and SensorML. We discovered that we ran into a blocker in configuring the translations because the end-user requirements were not specified. Turned out that the end-user requirements involved something that the O&M and EMF models do not support. Users are interested in specific results for specific time intervals (eg. last hour, or between 2 dates and times). The current O&M and WFS standards on which the INSPIRE specifications are based do not allow for this specific query. In the O&M standard an observation is a feature and the results are its properties. When using a WFS to query the data the service will always return a full feature (i.e. Observation) with all its results including the ones you are not interested in. To mitigate this, we changed the models slightly so that the service return a result as a feature. We also look at the W3C senML specification which provides better support for meeting the end-user requirements.
- #14 Query Translation - GO Publisher WFS Being able to query data through a translation process is significantly more complex. We can illustrate this by looking at GO Publisher WFS the software architecture of GO Publisher WFS. Configuration takes of the translation takes place in exactly the same way as for GO Publisher Desktop. Once the translation is configured the user adds additional configuration to the GO Publisher project file control the WFS behaviour. GO Publisher Desktop is then used to create a Web Archive (war) file which contains the project file and the WFS software. This war file contains everything needed by an application server to deploy the WFS. The war file is uploaded to an application server which unpacks and deploys the WFS from the war file. When a client submits a query to the WFS GO Publisher translates the WFS request into a SQL query. Because the WFS request is specified in terms of the GML application schema GO Publisher must use the data translation in reverse to translate the query. For example, if we set up a translation that translates the column “NAM” to the XML element “gml:name”, when a WFS request querying against “gml:name” arrives this must be turned into a SQL query against the column “NAM”. The data returned by the SQL query is then translated into GML (using the translation in its forward direction i.e. “NAM” becomes “gml:name”) and returned to the client. Multiple translations can be set up and deployed for a single database, thus allowing the data held in the database to be accessed by different communities of users using different GML application schemas.
- #15 Observations and Measurements: This is a brilliantly simple, generic model for encoding the observations collected by a sensor......but its too generic: How should the result (which is the container for the actual observations) be described/encoded? This needs to be defined by someone...... How should the procedure be described/encoded? What is the observedProperty? Is this a single observable parameter (e.g. Ozone) or can it be a collections
- #16 Observations and Measurements: For the 6 month prototype we used the INSPIRE PointTimeSeriesObservation as the result, SensorML to describe how the observation was measured Created a set of definitions of observableProperties within the project