Transcript Slide 1

OGC Sensor Web Enablement
Airborne Application
March 18, 2008
Dr. Mike Botts
[email protected]
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 (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
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Why SWE?
• Break down current stovepipes
• Enable interoperability not only within communities but between traditionally
disparate communities
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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
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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
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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
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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
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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
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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)
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Executing SensorML Processes
• Flexibility of execution engine
– UAH Open Source Execution Engine
SensorML
– Compute server (e.g. NGA IPL)
– COTS (e.g. ERMapper, Matlab, etc.)
– Web services (e.g. BPEL, Grid)
• Flexibility of execution location
– Client
SensorML
– Web Service
– Middleware
– On-board sensor or platform
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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
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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)
Mike Botts – January 2008
11
Incorporation of SWE into Space Time Toolkit
Space Time Toolkit has been retooled to be SensorML process chain executor + SLD stylers
Mike Botts – January 2008
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SWE Portrayal Service can “render” to various graphics standards
SWE Portrayal Service
SLD
SensorML
KML
Collada
SOS
Stylers
Google
Earth
Client
For example, a SWE portrayal service can utilize a SensorML frontend 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 !
Mike Botts – March 2008
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SWE to Google Earth (KML – Collada)
AMSR-E
SSM/I
MAS
TMI
LIS
Mike Botts – March 2008
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Demo: Tigershark UAV-HD Video
Tigershark
SensorML-enabled Client (e.g. STT)
SLD
SOS
SensorML
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 onthe-fly within the client software (enabled with SensorML process execution engine)
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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
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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
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Other Known Applications -1•
Community Sensor Models (NGA/CSM-WG)
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CBRNE Tiger Team (DIA, ORNL, JPEO, NIST, STRATCOM)
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funded project to add SWE support into SensorNet nodes for threat monitoring
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Developing SensorNet/SWE architecture for North Alabama (SMDC, DESE, UAH, ORNL)
PulseNet (Northrop Grumman TASC)
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demonstrated end-to-end application of SensorML/SWE for legacy surveillance sensors (demonstrated at EC07 and EC08)
Sensor Web (SAIC - Melbourne, FL)
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SensorML and SWE as future direction, with CCSI from JPEO and possibly IEEE1451
SensorNet (Oak Ridge National Labs)
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SensorML encoding of the CSM; CSM likely to be the ISO19130 standard
Developing end-to-end SWE components for MASINT and multi-sensor intelligence (demonstrated at EC08)
European Space Agency
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developing SensorML profiles for supporting sensor discovery and processing within the European satellite community
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establishing SPS and SOS services for satellite sensors
NASA
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funded 30 3-year projects (2006) based on RFP citing SensorML and Sensor Webs; additional RFP in 2008
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5 SBIR topics with SensorML and Sensor Web cited
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Received 2008 Business Innovative of Year Award for Sensor Web 2.0 based on SWE (new proposals under review)
Empire Challenge 2007 & 2008
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PulseNet demonstrated at EC07
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SAIC Sensor Web and OGC SWE Pilot Project participated at EC08
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Other Known Applications -2•
Sensors Anywhere (S@NY), OSIRIS, and NSPIRES
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Marine Metadata Initiative, OOSTethys, GOMOOS, Q2O (NOAA)
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Implementing and demonstrating SWE in several oceans monitoring activities
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Developing SensorML models and encodings for supporting QA/QC in ocean observations
Department of Homeland Security
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Using SensorML and SWE within several large European Union sensor projects
In 2007 SBIR, requested SensorML and SWE proposals
ASUS Wireless Home Monitoring System
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$23 billion/year company in Taiwan building commercial Zigbee Home Monitoring system using SWE
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DLR German-Indonesian Tsunami Warning System
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Others
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Landslide monitoring in Germany
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Water quality monitoring in Europe and Canada
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Mining and water management in Australia
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Building monitoring in Australia
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SWE a part of GEOSS and CEOS activities
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Hurricane monitoring at NASA
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Vaisala weather sensor vendor joined OGC and creating SensorML descriptions of their sensor systems
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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)
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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
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Additional Slides
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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)
Mike Botts – January 2008
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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
Mike Botts – March 2008
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SensorML Table Viewer
•
Will provide simple view of all data
in SensorML document
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Web-based servlet or standalone;
upload SensorML file and see view
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Ongoing effort: initial version in May
2008
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Future version will support
resolvable links to terms, as well as
plotting of curves, display of images,
etc
Mike Botts – March 2008
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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.
Mike Botts – March 2008
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Where and how SensorML can be used
Mike Botts – March 2008
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Supports description of Lineage for an Observation
SensorML
Observation
Within an Observation, SensorML can describe
how that Observation came to be using the
“procedure” property
Mike Botts – March 2008
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On-demand processing of sensor data
SensorML
Observation
SensorML 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
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On-demand processing
of higher-level products
SensorML
Observation
Observation
SensorML 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
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SensorML can support generation of Observations
within a Sensor Observation Service (SOS)
SOS Web Service
SensorML
request
Observation
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
Mike Botts – March 2008
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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)
Mike Botts – March 2008
32
SWE Visualization Clients can render graphics to screen
SensorML-enabled Client (e.g. STT)
SLD
SensorML
OpenGL
SOS
Stylers
Mike Botts – March 2008
33
SWE Portrayal Service can “render” to various graphics standards
SWE Portrayal Service
SLD
SensorML
KML
Collada
SOS
Stylers
Google
Earth
Client
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)
Mike Botts – March 2008
34