mike botts – october 2008 1 ogc sensor web enablement airborne application march 18, 2008 dr. mike...

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ike Botts – October 2008 1 OGC Sensor Web Enablement Airborne Application March 18, 2008 Dr. Mike Botts [email protected] Principal Research Scientist University of Alabama in Huntsville

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Mike Botts – October 2008 1

OGC Sensor Web Enablement

Airborne ApplicationMarch 18, 2008

Dr. Mike Botts

[email protected]

Principal Research Scientist

University of Alabama in Huntsville

Helping the World to CommunicateGeographically

What is SWE?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

Helping the World to CommunicateGeographically

Why SWE?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 CommunicateGeographically

What are the benefits of SWE?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

Helping the World to CommunicateGeographically

Basic DesiresBasic 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 CommunicateGeographically

SWE SpecificationsSWE 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

Helping the World to CommunicateGeographically

Why is SensorML Important?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

Helping the World to CommunicateGeographically

SensorML DescriptionsSensorML 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)

Helping the World to CommunicateGeographically

Executing SensorML ProcessesExecuting SensorML Processes

• Flexibility of execution engine

• Flexibility of execution location

SensorML

SensorML

– UAH Open Source Execution Engine

– Compute server (e.g. NGA IPL)

– COTS (e.g. ERMapper, Matlab, etc.)

– Web services (e.g. BPEL, Grid)

– Client

– Web Service

– Middleware

– On-board sensor or platform

Helping the World to CommunicateGeographically

SensorML On-Demand ProcessingSensorML 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

Mike Botts – January 2008 11

SWE Visualization Clients can render graphics to screen

SensorML

SOS

OpenGL

SensorML-enabled Client (e.g. STT)

Stylers

SLD

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 12

Incorporation of SWE into Space Time Toolkit

Space Time Toolkit has been retooled to be SensorML process chain executor + SLD stylers

Mike Botts – March 2008 13

SWE Portrayal Service can “render” to various graphics standards

SensorML

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 !

SOS

KML

SWE Portrayal Service

Google Earth Client

Stylers

SLD

Collada

Mike Botts – March 2008 14

SWE to Google Earth (KML – Collada)

AMSR-E SSM/I

LIS

TMI

MAS

Helping the World to CommunicateGeographically

Demo: Tigershark UAV-HD VideoDemo: Tigershark UAV-HD Video

SensorML

OpenGL

SensorML-enabled Client (e.g. STT)

Stylers

SLD

Tigershark

SOS

JP2

NAV

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)

Helping the World to CommunicateGeographically

Demo: Tigershark UAV-HD Video -2-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

Helping the World to CommunicateGeographically

Demo: Real-time Video streamingDemo: 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

Helping the World to CommunicateGeographically

Other Known Applications -1-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

Helping the World to CommunicateGeographically

Other Known Applications -2-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

Helping the World to CommunicateGeographically

ConclusionsConclusions• 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)

Helping the World to CommunicateGeographically

Relevant LinksOpen 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

Helping the World to CommunicateGeographically

Additional SlidesAdditional Slides

Mike Botts – January 2008 23

SensorML Process Editors

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)

Currently, SensorML documents are edited in XML (left), but will soon be edited using human friendly view (below)

Mike Botts – March 2008 24

Java Class Generator Tool

Programmer needs add only execution code

Takes an instance of a SensorML ProcessModel and generates the template for the Java class that can execute the ProcessModel

Mike Botts – March 2008 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

Mike Botts – March 2008 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.

Mike Botts – March 2008 27

Where and how SensorML can be used

Mike Botts – March 2008 28

Supports description of Lineage for an Observation

Observation

SensorML

Within an Observation, SensorML can describe

how that Observation came to be using the

“procedure” property

Mike Botts – March 2008 29

On-demand processing of sensor data

Observation

SensorML processes can be executed on-demand to

generate Observations from low-level sensor data

(without a priori knowledge of sensor system)

SensorML

Mike Botts – March 2008 30

On-demand processing of higher-level products

Observation

SensorML

SensorML processes can be executed on-

demand to generate higher-level Observations

from low-level Observations (e.g. discoverable

georeferencing algorithms or classification

algorithms)

Observation

Mike Botts – March 2008 31

SensorML can support generation of Observations within a Sensor Observation Service (SOS)

Observation

SensorML

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

SOS Web Service

request

Mike Botts – March 2008 32

SensorML can support tasking of sensors within a Sensor Planning Service (SPS)

SensorML

For example, SensorML will be used to support

tasking of video cam (pan, tilt, zoom) based on

location of target (lat, lon, alt)

SPS Web Service

request

Mike Botts – March 2008 33

SWE Visualization Clients can render graphics to screen

SensorML

SOS

OpenGL

SensorML-enabled Client (e.g. STT)

Stylers

SLD

Mike Botts – March 2008 34

SWE Portrayal Service can “render” to various graphics standards

SensorML

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)

SOS

KML

SWE Portrayal Service

Google Earth Client

Stylers

SLD

Collada