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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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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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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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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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