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 7

Use Case Scenarios

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