Open Geospatial Consortium Sensor Web Enablement GWG Plenary

Open Geospatial Consortium Sensor Web Enablement GWG Plenary October 16, 2008 Dr. Mike Botts mike. botts@uah. edu Principal Research Scientist University of Alabama in Huntsville Mike Botts – October 2008 1

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 (Sensor. ML, 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 Helping the World to Communicate Geographically

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 Helping the World to Communicate Geographically

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 inbetween) – scalable from single, simple sensor to large sensor collections Helping the World to Communicate Geographically

Mike Botts – July 2006 5

Basic Desires • Quickly discover 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 Helping the World to Communicate Geographically

Latest SWE Application 1. Greed sensor described using Sensor. ML, enabling plug-n-play and on-demand processing 2. Real-time and archived observations accessed through SOS 3. SAS notifies FEDS when threshold is reached 4. Additional sensors/actuators tasked using SPS and necessary action initiated Helping the World to Communicate Geographically

Sensor Web Enablement Framework Heterogeneous sensor network Airborne In-Situ monitor s - sparse - disparate Decision Support Tools Satellite Surveillance Bio/Chem/Rad Detectors - mobile/in-situ - extensible Models and Simulations Sensor Web Enablement - discovery - access - tasking - alert notification web services and encodings based on Open Standards (OGC, ISO, OASIS, IEEE) - nested - national, regional, urban - adaptable - data assimilation - vendor neutral - extensive M. Botts -2004 - flexible - adaptable Helping the World to Communicate Geographically

History -1 OGC Web Services Testbed 1. 2: OGC Web Services Testbed 1. 1: Sensor. ML initiated at University of Alabama in Huntsville: NASA AIST funding 1999 - 2000 • Sponsors: EPA, NASA, NIMA • Specs: Sensor. ML, SOS, O&M • Demo: NYC Terrorism • Sensors: weather stations, water quality 2001 • Sponsors: EPA, General Dynamics, NASA, NIMA • Specs: SOS, O&M, Sensor. ML, SPS, WNS • Demo: Terrorist, Hazardous Spill and Tornado • Sensors: weather stations, wind profiler, video, UAV, stream gauges 2002 • Specs advanced through independent R&D efforts in Germany, Australia, Canada and US • SWE WG established • Specs: SOS, O&M, Sensor. ML, SPS, WNS, SAS 2003 -2004 Helping the World to Communicate Geographically

History -2 OGC Web Services Testbed 3. 0: • Sponsors: NGA, ORNL, LMCO, BAE • Specs: SOS, O&M, Sensor. ML, SPS, TML • Demo: Forest Fire in Western US • Sensors: weather stations, wind profiler, video, UAV, satellite SAS Interoperabilty Experiment 2005 OGC Web Services Testbed 4. 0: • Sponsors: NGA, NASA, ORNL, LMCO • Specs: SOS, O&M, Sensor. ML, SPS, TML, SAS • Demo: Radiation, Emergency Hospital • Sensors: weather stations, wind profiler, video, UAV, satellite 2006 OGC Web Services Testbed 5. 1 SWE Specifications approved: Sensor. ML – V 1. 0. 1 TML – V 1. 0 SOS – V 1. 0 SPS – V 1. 0 O&M – V 1. 0 SAS – V 0. 0 WNS – Best Practices • Sponsors: NGA, NASA, • Specs: SOS, Sensor. ML, WPS • Demo: Streaming JPIP of Georeferenceable Imagery; Geoprocess Workflow • Sensors: Satellite and airborne imagery EC 07: in-situ sensors, video 2007 Helping the World to Communicate Geographically

SWE Specifications • Information Models and Schema – Sensor Model Language (Sensor. ML) 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 (eb. RIM)– Discover sensors and sensor observations Helping the World to Communicate Geographically

Status • Current specs are in various stages – Sensor. ML/SWE Common – Version 1. 0. 1 (V 2. 0 underway) – TML – Version 1. 0 – Observations & Measurement – Version 1. 0 (V 2. 0 underway) – WNS – Request for Comments – SOS – Version 1. 0 (V 2. 0 about to be initiated) – SPS – Version 1. 0 (V 2. 0 underway) – SAS – Ready for final vote (may skip V 1. 0 for V 2. 0) • Approved SWE standards can be downloaded: – Specification Documents: http: //www. opengeospatial. org/standards – Specification Schema: http: //schemas. opengis. net/ Helping the World to Communicate Geographically

Why is Sensor. ML Important? – Discovery of sensors and processes / plug-n-play sensors – Sensor. ML is the means by which sensors and processes make themselves and their capabilities known; describes inputs, outputs and taskable parameters – Observation lineage – Sensor. ML provides history of measurement and processing of observations; supports quality knowledge of observations – On-demand processing – Sensor. ML 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 – Sensor. ML enables the development of taskable, adaptable sensor networks, and enables higher-level problem solving anticipated from the Semantic Web Helping the World to Communicate Geographically

Executing Sensor. ML Processes • Flexibility of execution engine – UAH Open Source Execution Engine Sensor. ML – Compute server (e. g. NGA IPL) – COTS (e. g. ERMapper, Matlab, etc. ) – Web services (e. g. BPEL, Grid) • Flexibility of execution location – Client Sensor. ML – Web Service – Middleware – On-board sensor or platform Helping the World to Communicate Geographically

SWE Visualization Clients can render graphics to screen Sensor. ML-enabled Client (e. g. STT) SLD Sensor. ML Open. GL SOS Stylers For example, Space Time Toolkit executes Sensor. ML process chain on the front-end, and renders graphics on the screen based on stylers (uses OGC Style Layer Description standard) Mike Botts – January 2008 15

Incorporation of SWE into Space Time Toolkit has been retooled to be Sensor. ML process chain executor + SLD stylers Mike Botts – January 2008 16

SWE Portrayal Service can “render” to various graphics standards SWE Portrayal Service SLD Sensor. ML KML Collada SOS Stylers Google Earth Client For example, a SWE portrayal service can utilize a Sensor. ML frontend 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 ! Mike Botts – March 2008 17

SWE to Google Earth (KML – Collada) AMSR-E SSM/I MAS TMI LIS Mike Botts – March 2008 18

Demo: Radiation Attack on NY • OWS 4 Demonstration Project (Fall 2006) – Purpose of Demo: illustrate discovery, access to and fusing of disparate sensors – Client: UAH Space Time Toolkit – Services: • • • SOS – in-situ radiation sensors SOS – Doppler Radar SOS – Lagrangian plume model WCS – GOES weather satellite Sensor. ML – discovery and on-demand processing • WMS – Ortho Imagery • Google Earth – base maps – See all OWS 4 demos (interactive) – Download this demo (AVI: 93 MB): Helping the World to Communicate Geographically

Demo: Northrop Grumman Pulse. Net • Pulse. Net Demonstration – Purpose of Demo: Pulse. Net was an endto-end demonstration and test of SWE capabilities for legacy sensor systems – Client: Pulse. Net client (NGC) – Services: • SOS – weather stations • SOS – MASINT sensors (seismic, magnetic, radiation, acoustic, etc. ) • SOS – web cam • SPS – web cam • Sensor. ML – sensor descriptions – download this demo (wmv: 30 MB) Helping the World to Communicate Geographically

Demo: Satellite Data • NASA – Purpose of Demo: illustrate access to satellite observations and on-demand geolocation – Client: UAH Space Time Toolkit – Services: • • SOS – satellite footprints (UAH) SOS – aircraft observations (NASA) SOS – satellite observations (UAH) Sensor. ML – on-demand processing (UAH) • Virtual Earth – base maps – Download this demo Helping the World to Communicate Geographically

Demo: NASA/NWS Forecast Model Helping the World to Communicate Geographically

Demo: NASA/NWS Forecast Model -2 • NASA assimilation of AIRS satellite data into weather forecast model – Purpose of Demo: illustrate the refinement of regional forecast models based on Sensor. ML and SWE services – Client: Web-based client (NASA) – Services: • • • SOS – NAM forecast model SOS – phenomenon miner(NASA) SAS – phenomenon miner (NASA) SOS – AIRS satellite observations (UAH) SOS – footprint intersections (UAH) Sensor. ML – On-demand processing (UAH) – Download this demo Helping the World to Communicate Geographically

Demo: Robot Control • University of Muenster SPS Robot Demo – Purpose of Demo: demonstrate streaming of commands to SPS controlling a robot and retrieval of streaming video from the robot camera using SOS – Client: • IFGI Video Test Client • IFGI SPS Test Client – Services: • SOS – video from robot (52 North) • SPS – video camera control (52 North) – Download this demo: Helping the World to Communicate Geographically

Demo: SPOT Image • SPOT SPS and JPIP server – Purpose of Demo: illustrate dynamic query of SPS; show on-demand geolocation of JPIP stream using Sensor. ML – Client: • UAH Space Time Toolkit – Services: • SPS – satellite imagery feasibility [archived or future] (SPOT) • WCS/JPIP server – streaming J 2 K image with CSM parameters encoded in Sensor. ML (SPOT) • Sensor. ML – On-demand geolocation (UAH) • Virtual Earth – base maps – Download this demo (AVI-divx: 16 MB) Helping the World to Communicate Geographically

Demo: Tigershark UAV-HD Video Tigershark Sensor. ML-enabled Client (e. g. STT) SLD SOS Sensor. ML JP 2 Open. GL NAV Stylers The Tigershark SOS has two offerings: (1) time-tagged video frames (in JP 2) and (2) aircraft navigation (lat, lon, alt, pitch, roll, true heading) both served in O&M. A Sensor. ML process chain (using CSM frame sensor model) geolocates streaming imagery onthe-fly within the client software (enabled with Sensor. ML process execution engine) Helping the World to Communicate Geographically

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) • Sensor. ML – On-demand processing (Botts Innovative Research, Inc. ) • Virtual Earth – base maps – Download this demo Helping the World to Communicate Geographically

Tigershark Geolocation Helping the World to Communicate Geographically

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 Sensor. ML – 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) • Sensor. ML – On-demand processing (UAH) • Virtual Earth – base maps – Download this demo Helping the World to Communicate Geographically

Application: DLR Tsunami Warning System Helping the World to Communicate Geographically

Application: NASA Sensor Web Helping the World to Communicate Geographically

Application: NASA Sensor Web -2 - Helping the World to Communicate Geographically

Application: Sensors Anywhere (S@NY) Helping the World to Communicate Geographically

Other Known Applications -1 • Community Sensor Models (NGA/CSM-WG) – Sensor. ML encoding of the CSM; CSM likely to be the ISO 19130 standard • CBRNE Tiger Team (DIA, ORNL, JPEO, NIST, STRATCOM) – Sensor. ML and SWE as future direction, with CCSI from JPEO and possibly IEEE 1451 • Sensor. Net (Oak Ridge National Labs) – funded project to add SWE support into Sensor. Net nodes for threat monitoring – Developing Sensor. Net/SWE architecture for North Alabama (SMDC, DESE, UAH, ORNL) • Pulse. Net (Northrop Grumman TASC) – demonstrated end-to-end application of Sensor. ML/SWE for legacy surveillance sensors (demonstrated at EC 07 and EC 08) • Sensor Web (SAIC - Melbourne, FL) – Developing end-to-end SWE components for MASINT and multi-sensor intelligence (demonstrated at EC 08) • European Space Agency – developing Sensor. ML 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 Sensor. ML and Sensor Webs; additional RFP in 2008 – 5 SBIR topics with Sensor. ML 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 – Pulse. Net demonstrated at EC 07 – SAIC Sensor Web and OGC SWE Pilot Project participated at EC 08 Helping the World to Communicate Geographically

Other Known Applications -2 • Sensors Anywhere (S@NY), OSIRIS, and NSPIRES – Using Sensor. ML and SWE within several large European Union sensor projects • Marine Metadata Initiative, OOSTethys, GOMOOS, Q 2 O (NOAA) – Implementing and demonstrating SWE in several oceans monitoring activities – Developing Sensor. ML models and encodings for supporting QA/QC in ocean observations • Israeli Ministry of Defense – Testing/implementing TML for sensor data • Department of Homeland Security – In 2007 SBIR, requested Sensor. ML 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 Sensor. ML descriptions of their sensor systems Helping the World to Communicate Geographically

Directions Needed • Real, permanent implementations – Operational services and encodings, not just demos • Online semantic dictionaries and ontologies – Term definitions in dictionaries – Ontologies connecting terms – Share dictionary with public where possible • Discovery – Sensor discovery, as well as service discovery – Consider discovery closer to sensor fine grained temporal-spatial search (e. g. for UAV video) • Process Definition Library – Define common process models in Sensor. ML – IPL and others can then support these models • Sensor component and observation profile – Not necessary but useful for improved interoperability and ease of implementation • Continued tool development • Commitment to Use – Can go long way toward getting sensor and software vendor commitment • Flexibility of execution location Helping the World to Communicate Geographically

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 and processing of observations • SWE is getting buy-in from scattered sensor communities – A commitment from the IC and Do. D communities could provide the inertia to realize the full benefits (i. e. abundance of data, available of tools) – The IC and Do. D communities will benefit from contributions in the public sectors – Sensor vendors will contribute directly to Sensor Web only after user community commitment – SWE open to improvements by the user communities • Tools are being developed to support SWE – Tools will ease buy-in – Tools will assist in realizing the full benefits of SWE • SWE is ready to meet the challenges of the IC and Do. D communities Helping the World to Communicate Geographically

Relevant Links Open Geospatial Consortium http: //www. opengeospatial. org Sensor Web Enablement Working Group http: //www. ogcnetwork. net/SWE Public Forum http: //mail. opengeospatial. org/mailman/listinfo/swe. users Sensor. ML information http: //www. ogcnetwork. net/SWE/Sensor. ML Public Forum http: //mail. opengeospatial. org/mailman/listinfo/sensorml Helping the World to Communicate Geographically

Additional Slides Helping the World to Communicate Geographically

Sensor Web Vision -1 • Sensors will be web accessible • Sensors and sensor data will be discoverable • Sensors will be self-describing to humans and software (using a standard encoding) • Most sensor observations will be easily accessible in real time over the web Helping the World to Communicate Geographically

Sensor Web Vision -2 • Standardized web services will exist for accessing sensor information and sensor observations • Sensor systems will be capable of real-time mining of observations to find phenomena of immediate interest • Sensor systems will be capable of issuing alerts based on observations, as well as be able to respond to alerts issued by other sensors Helping the World to Communicate Geographically

Sensor Web Vision -3 • Software will be capable of on-demand geolocation and processing of observations from a newly-discovered sensor without a priori knowledge of that sensor system • Sensors, simulations, and models will be capable of being configured and tasked through standard, common web interfaces • Sensors and sensor nets will be able to act on their own (i. e. be autonomous) Helping the World to Communicate Geographically

Sensor. ML Process Editors Currently, Sensor. ML 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) Mike Botts – January 2008 43

Java Class Generator Tool Takes an instance of a Sensor. ML Process. Model and generates the template for the Java class that can execute the Process. Model Programmer needs add only execution code Mike Botts – March 2008 44

Sensor. ML Table Viewer • Will provide simple view of all data in Sensor. ML document • Web-based servlet or standalone; upload Sensor. ML 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 Mike Botts – March 2008 45

Simple Sensor. ML Forms for the Mass Market User fills out simple form with manufacturer name and model number, as well as other info. Then detailed Sensor. ML generated. Mike Botts – March 2008 46

Where and how Sensor. ML can be used Mike Botts – March 2008 47

Supports description of Lineage for an Observation Sensor. ML Observation Within an Observation, Sensor. ML can describe how that Observation came to be using the “procedure” property Mike Botts – March 2008 48

On-demand processing of sensor data Sensor. ML Observation Sensor. ML processes can be executed on-demand to generate Observations from low-level sensor data (without a priori knowledge of sensor system) Mike Botts – March 2008 49

On-demand processing of higher-level products Sensor. ML Observation Sensor. ML processes can be executed ondemand to generate higher-level Observations from low-level Observations (e. g. discoverable georeferencing algorithms or classification algorithms) Mike Botts – March 2008 50

Sensor. ML can support generation of Observations within a Sensor Observation Service (SOS) SOS Web Service Sensor. ML request Observation For example, Sensor. ML has been used to support on-demand generation of nadir tracks and footprints for satellite and airborne sensors within SOS web services Mike Botts – March 2008 51

Sensor. ML can support tasking of sensors within a Sensor Planning Service (SPS) SPS Web Service Sensor. ML request For example, Sensor. ML will be used to support tasking of video cam (pan, tilt, zoom) based on location of target (lat, lon, alt) Mike Botts – March 2008 52

SWE Visualization Clients can render graphics to screen Sensor. ML-enabled Client (e. g. STT) SLD Sensor. ML Open. GL SOS Stylers Mike Botts – March 2008 53

SWE Portrayal Service can “render” to various graphics standards SWE Portrayal Service SLD Sensor. ML KML Collada SOS Stylers Google Earth Client For example, a SWE portrayal service can utilize a Sensor. ML front-end a Styler back-end to generate graphics content (e. g. KML or Collada) Mike Botts – March 2008 54