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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. ShortENSCO, Inc.
Dana Teasdale, Kristofer S.J. PisterDust, Inc.
Mel SiegelCarnegie Mellon University
Donna ManobiancoManoNano Technologies, Research, & Consulting
November 2003
Outline• Description• Potential applications• Phase I (define major feasibility issues)• Phase II
– Methods / Approach– Plan
• Summary
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
Description (con’t)
Broad scalability & applicability
~1010 probesGlobal coverage1-km spacing
Regional coverage100-m spacing
• Mobile, 3D wireless network with communication among– Probes, intermediate nodes, data collectors, remote receiving platforms
Potential Applications
Weather / climate analysis & predictionBasic environmental science
Field experiments
Ground truth for remote sensing
Research & operational modeling
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.
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
Potential Applications
Battlesphere surveillanceIntelligence gathering
Threat monitoring & assessment
Homeland security
Phase I (Define Feasibility Issues)
Communication
Networking
DeploymentScavenging
Environmental
Data collection/management
Data impact Cost
Navigation
Dispersion
Probe designPower
Measurement
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)
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 enabling technologies
• Pathways for development & integration w/ NASA missions
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)
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
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)
Prob
abili
ty o
f Sur
viva
l 8 mm/hr
128 mm/hr
Observing System Simulation Experiments (OSSE)
0 1 …….. 10 11 12 13 14 …….. 29 30Nature 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)
OSSE DomainsSame 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)
Engineering Issues
• Components– Size & shape– Sensors– Fundamental limits– What’s next?
• Network– Cost of basic operations– Mesh network implementation– Limitations & scaling challenges
• Optimization
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 sensors• Analog Front-end• Digital Back-end
Communication:• RF antenna (shown)• Optical receiver
Sample, compute, listen, talk (RF)
once per hour for 10 days
230 µJ:25 µm2 solar cell
Probe Size & Shape
• Goal: Probe dropped at 20 km remains airborne for hours to days
• Strategies:– Dust sized particles– Materials– Buoyancy control: positively
buoyant probes– Probe shape:
dandelion/maple seed
Fall
Tim
e In
crea
se
Particle Size Decrease
Sensors• MEMS temperature, pressure & RH sensors well-established• Need to optimize range for atmospheric measurements
Sensirion humidity & temperature:Range: 0-100% RH, -40-124 ºC±0.2% RH±0.4 ºC$18
Intersema pressure:Range: 300-1100 mbar, -10-60 ºC±1.5 mbarµW per measurement$18
5 mm9 mm
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
Limiting Factors: µ-Fabricated Components
• Moore’s Law
• Thermal Noise: kT/2 (10-21 J)
• Sensors:– Fabrication limitations (aspect ratio)– Sensitivity (lower limit: molecules in Brownian motion?)– Inherent structural motion/vibration
The Next Generation: Nano Dust?
• Nanotube sensors• Nanotube computation• Nanotube hydrogen storage• Nanomechanical filters for communication!
Cost of Basic OperationsOperation Current
[A]Time[s]
Charge[A*s]
Sleep 3µSample 1m 20µ 0.020µTalk to neighbor15 byte payload
25m 5m 125µ
Listen to neighbor15 byte payload
10m 8m 80µ
Sound an alarm 25m 1s? 25,000µ?
Listen for alarm 2m 2m 4µ
QAAbattery = 2000mAh = 7,200,000,00 µA*s
Mesh Network Routing & LocalizationProbe network determines optimal route to gateway, and locates probes based on signal strength and GPS sensors.
Three motes’routing paths
Specialized GPS motes
send position information to
gateway.
Limit: Message traffic increases near gateway
Communication Limits• RF noise limit:
Preceived > kTB Nf SNRmin
Sensitivity ≈ -102 dBm (<0.1 pW)But, transmit power must be greater due to path loss
• Network communication must be rapid enough to avoid errors or loss of path due to probe motion
Signal Power
ReceivedThermal
Noise -174+53 dBm
Receiver Noise
+9 dBm
Signal to Noise required by
downstream processing+10 dBm
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)
Tran
smit
Pow
er R
equi
red
(W)
Transmit Power Required for 0.1 pW at Receiver
10 GHzAntenna Gain = 3
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 event “wake up” and
report
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
Demonstration
Pressure
Humidity/Temperature
X,Y-Acceleration
Light
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 / climate
sensitivity – How much can we reduce sensitivity with improved 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 affecting
populated areas
Summary• Advanced concept description
– Mobile network of wireless, airborne probes for environmental 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 for
development / integration with NASA missions
Acknowledgments
• Universities Space Research Association NASA Institute for Advanced Concepts– Phase I funding– Phase II funding
• Charles Stark Draper Laboratory– James Bickford– Sean George