nasa aist-05 sensor web pi meeting - feedback
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NASA AIST-05 Sensor Web
PI Meeting - Feedback
Concept Diagrams - C, B, A
Scenario Diagrams - C, B, A
Project Mapping - Group C
Capabilities Roadmap - Group A
February 14, 2007
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 2
Architectural Concept - Group C1
Output
• Prediction Models
• Decision Support Systems
• Science Users
Communications
Data Acquisition Data Processing
Command & Control
Autonomous External
Input
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 3
Architectural Concept - Group C
Command & Control
Autonomous External
Data Acquisition Data Processing
Modeling Storage
Data/
Information
Prediction models
Decision support systems
Data processing
Science users
Data/
Information
Commands/
Requests
Commands/
Requests
Platform
Sensor
Sensor Web
Storage
Comm Stds &
Protocols
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 4
Sensorweb Architecture - Group B
Visualization/User Interface
Applications
Data Services Sensor Services
Command/Control/Comm/Coordination
Sensors, Platforms
Scientists, disaster mitigators, general public
Service Models
Sensorweb 1
Sensorweb 2
Sensorweb 3Science Models
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 5
Architectural Concept - Group A
Outside Network
Compute
Nodes
Storage
Nodes
Composite
NodesSensor
Nodes
Communications
Fabric
EnvironmentEvents or state
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 6
Architectural Concept (cont.) - Group A
EnvironmentEvents or state
Sensor Layer
Models:- Sensor
- Environment
- …
Processing: - algorithms
- agents
- data fusion
Service Layer
Se
rvic
e 1
Se
rvic
e 2
Se
rvic
e n
Control:- Workflow
- Resource Mgmt
- …
raw data state data change of state command
observation
request
service requestData / information
Data / information
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 8
• Goal: Improve weather forecasting
on a regional basis.
• Problem: Not collecting data where
we need to collect data. Need to
collect and assimilate complex
heterogeneous data more intelligently.
• Opportunity afforded by the sensor web:
- Targeted observations
- Dynamic allocation of data collection assets
- Intelligent spatial (3-D), temporal and spectral coverage
- Collecting data from multiple viewing angles simultaneously
- Prediction based measurements drive targeted observations
Architecture Concept / Weather Forecasting - C
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 9
Sensorweb Use-Case Architecture - B
Visualization/User Interface
Applications
Data Services Sensor Services
Command/Control/Comm/Coordination
Sensors, Platforms
Scientists, disaster mitigators, general public
Service Models
Science Models
Seismometers
GPS Sensors
Satellite or airborne
imagery
Interferometric Radar
Deformation Model
gives strain over the
area
Map showing pipeline stresses
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 10
Biomass Measurement Scenario - A
Sensors Weather Soil Moisture Vegetation
Biomass (x,y,z,t)
Estimate of Uncertainty
Map of Biomass +
Uncertainty
Assimilate with Model to Predict
Future Ecosystem parameters (e.g.
CO2)
Utility of Data
Decision Making
Retask Sensors Model Calibration
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 11
Architecture Concept / Project Mapping - C
Command & Control
Autonomous External
Data Acquisition Data Processing
Modeling Storage
Data/
Information
Prediction models
Decision support systems
Data processing
Science users
Data/
Information
Commands/
Requests
Commands/
Requests
Platform
Sensor
Sensor Web
Storage
13
1
1
4
4
5
6
6
22
2
4
4 4
7
7
8
8
9
9
10
10
10
10
7
Comm
Stds &
Protocols
2
5
February 13-14, 2007 AIST-05 Sensor Web PI Meeting 12
Evolutionary Sensor Web Capabilities(potential to drive NASA road map) - Group A
Simple Complex
Data collection and
reporting (e.g., river waterlevels, incident solar radiation,
lightning detection network,
GOES DCS for datadissemination)
Data sharing (e.g., GPS-
based time and location
dissemination)
Event detection and
notification (e.g., Swift GRB and
the Gamma Ray BurstCoordinates Network - GCN)
Coordinated data collection
and reporting, science datafusion, information synthesis,
and decision making.
Collaborative system levelreconfiguration (e.g., run new algorithm
based on node data collected; deploy
additional sensors/interrogate other
sensors to refine forecast and alerts)
Intelligent sensor webpredicts phenomenon, controls
resources to perform
collaborative observations,
possibly even takes actions tomodify effects of phenomena
(e.g., predicts flash flood;
monitors rainfall conditions;
accesses GIS data; opens floodgates when flash flood detected)
Class 1
Class 2
Class 3
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