OGC Sensor Web Enablement Airborne Application March 18, 2008

1. OGC Sensor Web Enablement Airborne ApplicationMarch 18, 2008 Dr. Mike Botts mike.botts@uah.edu Principal Research Scientist University of Alabama in Huntsville

2. What is SWE? • SWE is technology to enable the realization of Sensor Webs • much like TCP/IP, HTML, and HTTPD enabled the WWW • SWE is a suite of standards from OGC (Open Geospatial Consortium) • 3 standard XML encodings (SensorML, O&M, TML) • 4 standard web service interfaces (SOS, SAS, SPS, WNS) • SWE is a Service Oriented Architecture (SOA) approach • SWE is an open, consensus-based set of standards

3. Why SWE? • Break down current stovepipes • Enable interoperability not only within communities but between traditionally disparate communities • different sensor types: in-situ vs remote sensors, video, models, CBRNE • different disciplines: science, defense, intelligence, emergency management, utilities, etc. • different sciences: ocean, atmosphere, land, bio, target recognition, signal processing, etc. • different agencies: government, commercial, private, Joe Public • Leverage benefits of open standards • competitive tool development • more abundant data sources • utilize efforts funded by others • Backed by the Open Geospatial Consortium process • 350+ members cooperating in consensus process • Interoperability Process testing • CITE compliance testing

4. What are the benefits of SWE? • Sensor system agnostic - Virtually any sensor or model system can be supported • Net-Centric, SOA-based • Distributed architecture allows independent development of services but enables on-the-fly connectivity between resources • Semantically tied • Relies on online dictionaries and ontologies for semantics • Key to interoperability • Traceability • observation lineage • quality of measurement support • Implementation flexibility • wrap existing capabilities and sensors • implement services and processing where it makes sense (e.g. near sensors, closer to user, or in-between) • scalable from single, simple sensor to large sensor collections

5. Basic Desires • Quicklydiscover sensors and sensor data (secure or public) that can meet my needs – based on location, observables, quality, ability to task, etc. • Obtain sensor information in a standard encoding that is understandable by my software and enables assessment and processing without a-priori knowledge • Readily access sensor observations in a common manner, and in a form specific to my needs • Task sensors, when possible, to meet my specific needs • Subscribe to and receive alerts when a sensor measures a particular phenomenon

6. SWE Specifications • Information Models and Schema • Sensor Model Language (SensorML) for In-situ and Remote Sensors - Core models and schema for observation processes: support for sensor components and systems, geolocation, response models, post measurement processing • Observations and Measurements (O&M) – Core models and schema for observations; archived and streaming • Transducer Markup Language (TML) – system integration and multiplex streaming clusters of observations • Web Services • Sensor Observation Service - Access Observations for a sensor or sensor constellation, and optionally, the associated sensor and platform data • Sensor Alert Service – Subscribe to alerts based upon sensor observations • Sensor Planning Service – Request collection feasibility and task sensor system for desired observations • Web Notification Service –Manage message dialogue between client and Web service(s) for long duration (asynchronous) processes • Sensor Registries (ebRIM)– Discover sensors and sensor observations

7. Why is SensorML Important? • 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

8. SensorML Descriptions • UAV Sensor System Description • Provides as detailed description of the system as you desire • Example: SensorML XML • Example: Pretty View version mined from SensorML • Community Sensor Models (CSM) • Tigershark System – KCM-HD camera (SensorML encoding)

9. Executing SensorML Processes • Flexibility of execution engine • Flexibility of execution location • UAH Open Source Execution Engine • Compute server (e.g. NGA IPL) SensorML SensorML • COTS (e.g. ERMapper, Matlab, etc.) • Web services (e.g. BPEL, Grid) • Client • Web Service • Middleware • On-board sensor or platform

10. SensorML On-Demand Processing • Streaming AIRDAS observations from an SOS • navigation observations • ASCII encoded • latitude, longitude, altitude, pitch, roll, true heading • scan lines observations • base64 encoded (could also be pure binary or ASCII) • video • On-demand geolocation of streaming data using SensorML • video • Space Time Toolkit knows nothing about doing geolocation • SensorML provides the required expertise • Could be any algorithm or process • Discoverable processes, as well as sensors • Space Time Toolkit only knows how to execute SensorML

11. SWE Visualization Clients can render graphics to screen SensorML-enabled Client (e.g. STT) SLD SensorML OpenGL SOS Stylers For example, Space Time Toolkit executes SensorML process chain on the front-end, and renders graphics on the screen based on stylers (uses OGC Style Layer Description standard)

12. Incorporation of SWE into Space Time Toolkit Space Time Toolkit has been retooled to be SensorML process chain executor + SLD stylers

13. SWE Portrayal Service can “render” to various graphics standards SWE Portrayal Service SLD SensorML KML Collada SOS Google Earth Client Stylers For example, a SWE portrayal service can utilize a SensorML front-end and a Styler back-end to generate graphics content (e.g. KML or Collada) However, it’s important that the data content standards (e.g. SWE) exist to support the graphical exploration and exploitation !

14. SWE to Google Earth (KML – Collada) AMSR-E SSM/I MAS TMI LIS

15. SensorML Demo: Tigershark UAV-HD Video TigersharkSOS SensorML-enabled Client (e.g. STT) SLD JP2 OpenGL NAV Stylers The Tigershark SOS has two offerings: (1) time-tagged video frames (in JP2) and (2) aircraft navigation (lat, lon, alt, pitch, roll, true heading) both served in O&M. A SensorML process chain (using CSM frame sensor model) geolocates streaming imagery on-the-fly within the client software (enabled with SensorML process execution engine)

16. Demo: Tigershark UAV-HD Video -2- • Empire Challenge 2008 • Purpose of Demo: illustrate on-demand geolocation and display of HD video from Tigershark UAV • Client: UAH Space Time Toolkit • Services: • SOS – Tigershark video and navigation (ERDAS) • SOS – Troop Movement (Northrop Grumman) • SensorML – On-demand processing (Botts Innovative Research, Inc.) • Virtual Earth – base maps • Download this demo

17. Demo: Real-time Video streaming • UAH Dual Web-based Sky Cameras • Purpose of Demo: demonstrate streaming of binary video with navigation data; on-demand geolocation using SensorML • Client: • 52 North Video Test Client • UAH Space Time Toolkit • Services: • SOS – video and gimbal settings (UAH, 52 North) • SPS – Video camera control (52 North, UAH) • SensorML – On-demand processing (UAH) • Virtual Earth – base maps • Download this demo

18. Other Known Applications -1- • Community Sensor Models (NGA/CSM-WG) • SensorML encoding of the CSM; CSM likely to be the ISO19130 standard • CBRNE Tiger Team (DIA, ORNL, JPEO, NIST, STRATCOM) • SensorML and SWE as future direction, with CCSI from JPEO and possibly IEEE1451 • SensorNet (Oak Ridge National Labs) • funded project to add SWE support into SensorNet nodes for threat monitoring • Developing SensorNet/SWE architecture for North Alabama (SMDC, DESE, UAH, ORNL) • PulseNet (Northrop Grumman TASC) • demonstrated end-to-end application of SensorML/SWE for legacy surveillance sensors (demonstrated at EC07 and EC08) • Sensor Web (SAIC - Melbourne, FL) • Developing end-to-end SWE components for MASINT and multi-sensor intelligence (demonstrated at EC08) • European Space Agency • developing SensorML profiles for supporting sensor discovery and processing within the European satellite community • establishing SPS and SOS services for satellite sensors • NASA • funded 30 3-year projects (2006) based on RFP citing SensorML and Sensor Webs; additional RFP in 2008 • 5 SBIR topics with SensorML and Sensor Web cited • Received 2008 Business Innovative of Year Award for Sensor Web 2.0 based on SWE (new proposals under review) • Empire Challenge 2007 & 2008 • PulseNet demonstrated at EC07 • SAIC Sensor Web and OGC SWE Pilot Project participated at EC08

19. Other Known Applications -2- • Sensors Anywhere (S@NY), OSIRIS, and NSPIRES • Using SensorML and SWE within several large European Union sensor projects • Marine Metadata Initiative, OOSTethys, GOMOOS, Q2O (NOAA) • Implementing and demonstrating SWE in several oceans monitoring activities • Developing SensorML models and encodings for supporting QA/QC in ocean observations • Department of Homeland Security • In 2007 SBIR, requested SensorML and SWE proposals • ASUS Wireless Home Monitoring System • $23 billion/year company in Taiwan building commercial Zigbee Home Monitoring system using SWE • DLR German-Indonesian Tsunami Warning System • Others • Landslide monitoring in Germany • Water quality monitoring in Europe and Canada • Mining and water management in Australia • Building monitoring in Australia • SWE a part of GEOSS and CEOS activities • Hurricane monitoring at NASA • Vaisala weather sensor vendor joined OGC and creating SensorML descriptions of their sensor systems

20. Conclusions • SWE has been tested and has proven itself • Useful, flexible, efficient, extensible • Simple to add to both new and existing legacy systems • Enables paradigm shifts in access to and processing of observations • SWE is getting buy-in from scattered sensor communities • Large agencies like NGA, DIA, NASA, ESA, DLR, NOAA • Smaller communities as well • SWE open to improvements by the user communities • Tools are being developed to support SWE (Open Source and Commercial) • Tools will ease buy-in • Tools will assist in realizing the full benefits of SWE • SWE would be useful to airborne sensor community • Standard sensor system descriptions • Efficient observation streaming • On-demand georectification and processing • Flexibility for service (on-board or on-ground)

21. Relevant Links Open Geospatial Consortium http://www.opengeospatial.org Sensor Web Enablement Working Group http://www.ogcnetwork.net/SWE SWE Public Forum http://mail.opengeospatial.org/mailman/listinfo/swe.users SensorML information http://vast.uah.edu/SensorML SensorML Public Forum http://mail.opengeospatial.org/mailman/listinfo/sensorml

22. Additional Slides

23. SensorML Process Editors Currently, SensorML documents are edited in XML (left), but will soon be edited using human friendly view (below) Currently, we diagram the process (right top) and then type the XML version; soon the XML will be generated from the diagram itself (right bottom)

24. Java Class Generator Tool Takes an instance of a SensorML ProcessModel and generates the template for the Java class that can execute the ProcessModel Programmer needs add only execution code

25. SensorML Table Viewer • Will provide simple view of all data in SensorML document • Web-based servlet or standalone; upload SensorML file and see view • Ongoing effort: initial version in May 2008 • Future version will support resolvable links to terms, as well as plotting of curves, display of images, etc

26. Simple SensorML Forms for the Mass Market User fills out simple form with manufacturer name and model number, as well as other info. Then detailed SensorML generated.

27. Where and how SensorML can be used

28. SensorML Observation Supports description of Lineage for an Observation Within an Observation, SensorML can describe how that Observation came to be using the “procedure” property

29. 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)

30. SensorML Observation Observation On-demand processing of higher-level products SensorML processes can be executed on-demand to generate higher-level Observations from low-level Observations (e.g. discoverable georeferencing algorithms or classification algorithms)

31. Observation SensorML can support generation of Observations within a Sensor Observation Service (SOS) SOS Web Service SensorML request For example, SensorML has been used to support on-demand generation of nadir tracks and footprints for satellite and airborne sensors within SOS web services

32. SensorML can support tasking of sensors within a Sensor Planning Service (SPS) SPS Web Service SensorML request For example, SensorML will be used to support tasking of video cam (pan, tilt, zoom) based on location of target (lat, lon, alt)

33. SWE Visualization Clients can render graphics to screen SensorML-enabled Client (e.g. STT) SLD SensorML OpenGL SOS Stylers

34. SWE Portrayal Service can “render” to various graphics standards SWE Portrayal Service SLD SensorML KML Collada SOS Google Earth Client Stylers For example, a SWE portrayal service can utilize a SensorML front-end and a Styler back-end to generate graphics content (e.g. KML or Collada)