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Global Environmental MEMS Sensors (GEMS):
A Revolutionary Observing System
for the 21st Century
NIAC Phase II CP_02-01
John Manobianco, Randolph J. Evans, David A. Short
ENSCO, Inc.
Dana Teasdale, Kristofer S.J. Pister
Dust, Inc.
Mel SiegelCarnegie Mellon University
Donna Manobianco
ManoNano Technologies, Research, & Consulting
November 2003
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Outline
Description
Potential applications
Phase I (define major feasibility issues) Phase II
Methods / Approach
Plan
Summary
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Description
Integrated system of airborne probes Mass produced at very low per-unit cost
Disposable
Suspended in the atmosphere Carried by wind currents
MicroElectroMechanical System (MEMS)-based sensors Meteorological parameters (temperature, pressure, moisture, velocity)
Particulates
Pollutants
O3, CO2, etc.
Acoustic, seismic, imaging
Chemical, biological, nuclear contaminants
Self-contained with power source for Sensing
Navigation
Communication
Computation
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Description (cont)
Broad scalability & applicability
~1010 probes
Global coverage
1-km spacing
Regional coverage
100-m spacing
Mobile, 3D wireless network with communication among Probes, intermediate nodes, data collectors, remote receiving platforms
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Potential Applications
Weather / climate analysis & predictionBasic environmental science
Field experiments
Ground truth for remote sensing
Research & operational modeling
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Potential Applications
Planetary science missions
Manobianco et al.: GEMS: A Revolutionary Concept for Planetary and Space Exploration,Space Technology and Applications International Forum, Symposium on Space Colonization,Space Exploration Session, Albuquerque, NM, February 2004.
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Potential Applications
Planetary science missions
Manobianco et al.: GEMS: A Revolutionary Concept for Planetary and Space Exploration,Space Technology and Applications International Forum, Symposium on Space Colonization,Space Exploration Session, Albuquerque, NM, February 2004.
Space Environment Monitoring
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Potential Applications
Battlesphere surveillance
Intelligence gathering
Threat monitoring & assessmentHomeland security
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Phase I (Define Feasibility Issues)
Communication
Networking
DeploymentScavenging
Environmental
Data collection/management
Data impact Cost
Navigation
Dispersion
Probe designPower
Measurement
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Phase II Methods / Approach
Optimization of trade-offs
(cost, practicality, feasibility)
Multi-Dimensional Parameter Space
(Power, Deployment, Cost,)
Physical limitations
(measurement &
signal detection)
Scaling
(probe & network size)
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Phase II Plan
Study major feasibility issues
Extensive use of simulation
Deployment, dispersion, data impact, scavenging, power,
System model
Experimentation as appropriate / practical
Cost-benefit analysis
Projected per unit & infrastructure cost Compare w/ future observing systems
Quantify benefits
Develop technology roadmap & identify enablingtechnologies
Pathways for development & integration w/ NASA missions
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Meteorological Issues
Deployment strategies
Dispersion
Scavenging
Impact of probe data on analyses & forecasts
Dynamic simulation models
Virtual weather scenarios
Dispersion patterns
Simulated probe data & statistics
OSSE (Observing System Simulation Experiments)
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Deployment / Dispersion
Release (N. Hemisphere) High-altitude balloons 10o x 10o lat-lon
Deployment 4-day release 18-km altitude 1 probe every 6 min
Terminal velocity 0.01 m s-1
Duration
24 days 15 Jun 9 Jul 2001
Total # of probes ~200,000
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Scavenging
Light Rain Heavy RainSimple Collision Model
0
0.2
0.4
0.6
0.8
1
0.01 0.1 1 10 100 1000Time (minutes)
Probabilit
y
ofSurvival 8 mm/hr
128 mm/hr
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Observing System Simulation Experiments (OSSE)
0 1 .. 10 11 12 13 14 .. 29 30
Nature run (Truth from Model 1)Simulated observations
Time (days)
Benchmark (Model 2)
Data insertion window (assimilate simulated observations)
Experiment 1 (Model 2)
Compare with nature & control run to assess data impact
Experiments 2, 3, (Variations on Exp. 1)
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OSSE Domains
Same boundary & initial conditions
30 km
10 km
2.5 km
Nature Run (Model 1)Summer / winter case
Probes deployed / dispersed for 20-30 days
10 km
30 km
OSSE (Model 2)
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Engineering Issues
Components Size & shape
Sensors Fundamental limits
Whats next?
Network Cost of basic operations Mesh network implementation
Limitations & scaling challenges
Optimization
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Probe Components
Power: Solar cell (~1 J/day/mm2) Batteries ~1 J/mm3
Capacitors ~0.01 J/mm3 Fuel Cell ~30 J/mm3
Sensing & Processing: Temperature, pressure, RH sensorsAnalog Front-end Digital Back-end
Communication: RF antenna (shown) Optical receiver
Sample, compute,
listen, talk (RF)once per hour for 10
days
230 J:25 m2 solar cell
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Probe Size & Shape
Goal: Probe dropped at 20 kmremains airborne for hours to
days
Strategies:
Dust sized particles Materials
Buoyancy control: positivelybuoyant probes
Probe shape:dandelion/maple seed
FallT
imeIncrease
Particle Size Decrease
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Sensors
MEMS temperature, pressure & RH sensors well-established
Need to optimize range for atmospheric measurements
Sensirion humidity & temperature: Range: 0-100% RH, -40-124 C
0.2% RH
0.4 C
$18
Intersema pressure: Range: 300-1100 mbar, -10-60 C
1.5 mbar
W per measurement
$18
5 mm9 mm
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Shrinking Probes
8 bit uP
3k RAM
OS accelerators
World record low power 8 bit ADC(100kS/s, 2uA)
HW Encryption support
900 MHz transmitter
Circuit Board Layout
TI MSP430f149 16-bit processor
60kB flash, 2 kB RAM
Temp, battery, RF signal sensors
7 12-bit analog inputs
16 digital IO pins
902-928 kHz operation
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Limiting Factors: -Fabricated Components
Moores Law
Thermal Noise: kT/2(10-21 J)
Sensors: Fabrication limitations (aspect ratio)
Sensitivity (lower limit: molecules in Brownian motion?)
Inherent structural motion/vibration
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The Next Generation: Nano Dust?
Nanotube sensors
Nanotube computation
Nanotube hydrogen storage
Nanomechanical filters for communication!
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Cost of Basic Operations
Operation Current
[A]
Time
[s]
Charge
[A*s]
Sleep 3
Sample 1m 20 0.020
Talk to neighbor
15 byte payload
25m 5m 125
Listen to neighbor
15 byte payload
10m 8m 80
Sound an alarm 25m 1s? 25,000?
Listen for alarm 2m 2m 4
QAAbattery = 2000mAh = 7,200,000,00 A*s
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Mesh Network Routing & Localization
Probe network determines optimal route to gateway, andlocates probes based on signal strength and GPS sensors.
Three motesrouting paths
SpecializedGPS motes
send positioninformation to
gateway.
Limit: Message traffic increases near gateway
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Communication Limits
RF noise limit:Preceived > kTB NfSNRmin
Sensitivity -102 dBm (
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Link Budget
Probe Spacing = Transmission Power
Transmit Power vs. Probe Spacing
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000
Probe Spacing (m)
TransmitP
owerRequired(W
)
Transmit Power Required for
0.1 pW at Receiver
10 GHz
Antenna Gain = 3
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Network Scaling
Message traffic limited near gateway Next step: event-based reporting (1-way communication)
Beyond: local event-based subnet formation & reporting any mote
becomes a gateway
Lots of message
traffic near gateway
Motes near eventwake up and
report
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Optimization: Trade-offs
SIZE
+ Min Environmental Impact
+ Slow descent
- Decreased power storage
- Decrease SNR
POWER
+ Smaller power supply required
- Decrease transmission distance &
sampling frequency- Shorter mote life
# PROBES
+ Improved network localization
+ Improved forecast- Increased message traffic
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Demonstration
Pressure
Humidity/Temperature
X,Y-Acceleration
Light
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Cost / Benefit Analysis
Cost issues Per unit cost
Deployment / O&M cost
Global versus regional (targeted) observations
Estimates for future observing systems (in situ v. remote)
Benefit issues
$3 trillion dollars of U.S. economy has weather / climatesensitivity How much can we reduce sensitivity withimproved observations / forecasts?
Example (hurricane track forecasts) 72-h track forecast error 200 mi
Evacuation cost = $0.5M per linear mile
Potential savings with 10% error reduction = $10M for storms affectingpopulated areas
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Summary
Advanced concept description
Mobile network of wireless, airborne probes forenvironmental monitoring
Phase I results
Define major feasibility issues
Validate viability of the concept
Phase II plans
Study feasibility issues
Cost-benefit
Generate technology roadmap including pathways fordevelopment / integration with NASA missions
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Acknowledgments
Universities Space Research Association
NASA Institute for Advanced Concepts
Phase I funding
Phase II funding
Charles Stark Draper Laboratory James Bickford
Sean George
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