Supporting QA/QC in Sensor Web Enablement (SWE) and SensorML February 2008

1. Supporting QA/QC inSensor Web Enablement (SWE)and SensorMLFebruary 2008 Mike Botts mike.botts@uah.edu Principal Research Scientist University of Alabama in Huntsville

2. Why is SensorML Important? • Importance: • Discovery of sensors and processes / plug-n-play sensors – SensorML is the means by which sensors and processes make themselves and their capabilities known; describes inputs, outputs and taskable parameters • Observation lineage – SensorML provides history of measurement and processing of observations; supports quality knowledge of observations • On-demand processing – SensorML supports on-demand derivation of higher-level information (e.g. geolocation or products) without a priori knowledge of the sensor system • Intelligent, autonomous sensor network – SensorML enables the development of taskable, adaptable sensor networks, and enables higher-level problem solving anticipated from the Semantic Web

3. Where can QA/QC be supported? • Sensor Descriptions (SensorML-SWE Common) • Discovery • Capabilities • Detector Parameters • Error curves, latency • Accuracy of parameters and output values • Observations (O&M-Swe Common) • QA/QC expressions for the values • e.g. accuracy, confidence levels, etc • either constants or themselves as output values • Lineage of the Observation (procedure property) • SensorML System and ProcessChains

4. SensorML Descriptions for Discoverybased on QA/QC capabilities and characteristics

5. QA/QC metadata suitable for discoveryof sensors and processes Find all remote sensor systems measuring in the visible spectral range with ground resolution less than 20m. Key is to define terms for QA/QC characteristics

6. QA/QC in Detectors, Actuators, and Sensor Systems • Detector and Actuator parameters support some QA/QC • Sensitivity, latency, error curves, etc • Need to make certain that any additional QA/QC parameters are supported in models • Any SWE Common values can have QA/QC properties • Accuracy, Confidence, etc. • Useful for defining quality and confidence of • Outputs of sensors • Observation values in O&M • quality values can be constant or defined as output of sensors and processes

7. SensorML Observation SensorML Supports description of Lineage for an Observation Within an Observation, SensorML can describe how that Observation came to be using the “procedure” property Key is to make sure each process component provides necessary QA/QC measurement

8. SensorML Observation On-demand processing of sensor data SensorML processes can be executed on-demand to generate Observations from low-level sensor data (without a priori knowledge of sensor system) Need to understand how to support error propagation within each Process and to enable combining these errors into composite indicators