842manobianco

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 Siegel Carnegie Mellon University Donna Manobianco ManoNano 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) •

Mobile, 3D wireless network with communication among – Probes, intermediate nodes, data collectors, remote receiving platforms

Broad scalability & applicability

~1010 probes Global coverage 1-km spacing Regional coverage 100-m spacing

Potential Applications Weather / climate analysis & prediction Basic 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 Space Environment Monitoring 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 Battlesphere surveillance Intelligence gathering Threat monitoring & assessment Homeland security

Phase I (Define Feasibility Issues) Data collection/management Data impact

Cost Networking Navigation

Communication

Dispersion Deployment

Scavenging Probe design Measurement

Environmental Power

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 Rain

Simple Collision Model

Probability of Survival

1 8 mm/hr

0.8

128 mm/hr

0.6 0.4 0.2 0 0.01

0.1

1

10

Time (minutes)

100

1000

Observing System Simulation Experiments (OSSE) Time (days) 0

1

…….. 10

11

12

13

14 …….. 29

30

Nature run (“Truth” from Model 1) Simulated observations

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 Domains Same boundary & initial conditions 30 km

30 km

10 km

10 km

2.5 km

Nature Run (Model 1) Summer / winter case Probes deployed / dispersed for 20-30 days

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

230 µJ: 25 µm2 solar cell

Sample, compute, listen, talk (RF) once per hour for 10 days

• 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 Time Increase

Probe Size & Shape

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

5 mm

Intersema pressure:

Range: 300-1100 mbar, -10-60 ºC ±1.5 mbar µW per measurement $18

9 mm

Shrinking Probes

• • • • • • •

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

• • • • • •

8 bit uP 3k RAM OS accelerators World record low power 8 bit ADC (100kS/s, 2uA) HW Encryption support 900 MHz transmitter

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 Operations Current

Operation

[A]

Time [s]

Charge [A*s]

Sleep

3µ

Sample

1m

20µ

0.020µ

Talk to neighbor

25m

5m

125µ

10m

8m

80µ

Sound an alarm

25m

1s?

25,000µ?

Listen for alarm

2m

2m

4µ

15 byte payload

Listen to neighbor 15 byte payload

QAAbattery = 2000mAh = 7,200,000,00 µA*s

Mesh Network Routing & Localization Probe network determines optimal route to gateway, and locates probes based on signal strength and GPS sensors.

Specialized GPS motes send position information to gateway.

Three motes’ routing paths Limit: Message traffic increases near gateway

Communication Limits • RF noise limit: Preceived > kTB Nf SNRmin Signal Power Received

Thermal Noise -174+53 dBm

Receiver Noise +9 dBm

Signal to Noise required by downstream processing +10 dBm

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

Link Budget ↑ Probe Spacing = ↑ Transmission Power

Transmit Power vs. Probe Spacing

Transmit Power Required (W)

0.14 0.12 0.1 0.08

Transmit Power Required for 0.1 pW at Receiver

0.06 0.04

10 GHz Antenna Gain = 3

0.02 0 0

2000

4000

6000

8000

10000

12000

Probe Spacing (m)

14000

16000

18000

20000

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 Motes near event “wake up” and report

Lots of message traffic near gateway

Optimization: Trade-offs ↓SIZE

+ + -

↓POWER

+ Smaller power supply required - Decrease transmission distance & sampling frequency - Shorter mote life

↑# PROBES

Min Environmental Impact Slow descent Decreased power storage Decrease SNR

+ 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