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

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0.2

0.4

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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

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0.02

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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