SMART DUST
B. Boser, D. Culler, J. Kahn, K. Pister
Berkeley Sensor & Actuator Center
Electrical Engineering & Computer Sciences
UC Berkeley
SMART DUST
Outline
• History
• Technology Ramblings
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Motivation
• Exponential decrease in size, power, cost
• Digital computation
• Analog/RF communication
• Sensors
 battery
Goals
• Understand fundamental limits
• Build working systems
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Moore’s Law, take 2
• Nanochips on a dime (Prof. Steve Smith, EECS)
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DoD Workshops
• RAND 1992
• “Future Technology-Driven Revolutions in
Military Conflict”
• “Smart Chaff”, “Floating Finks”
• Bruno Augenstein, Seldon Crary, Noel
Macdonald, Randy Steeb, …
• Santa Fe, 1995
• Xan Alexander, Ken Gabriel; Roger Howe,
George Whitesides, …
• ISAT 1995, 1996, 1997, 1998, 1999, 2000
•…
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University Programs (old slide)
• UCLA
• Bill Kaiser (LWIM, WINS)
• Greg Pottie (AWAIRS)
• U. Michigan
• Ken Wise
• USC
• Deborah Estrin
• UCB
• K. Pister (Smart Dust)
•…
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•
Ken Wise, U. Michigan
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http://www.eecs.umich.edu/~wise/Research/Overview/wise_research.pdf
Bill Kaiser, UCLA
•
http://www.janet.ucla.edu/WINS
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August ’01 Goal
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COTS Dust - RF Motes
• Simple computer
• Cordless phone radio
• Up to 2 year battery life
N
W
E
S
2 Axis Magnetic
Sensor
2 Axis Accelerometer
Light Intensity
Sensor
Humidity Sensor
Pressure Sensor
Temperature Sensor
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COTS Dust
GOALS:
• Create a network of sensors
• Explore system design issues
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COTS Dust
RESULTS:
• TinyOS – David Culler, UCB
• Manufactured by Crossbow ~ $150
• 100+ users, 40+ locations
• Military and civilian applications
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800 node demo at Intel Developers Forum
4 sensors
$70,000 / 1000
Concept to demo in 30 days!
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Structural performance due to multi-directional
ground motions (Glaser & CalTech)
.
Mote
Layout
1
3
1
54
6`
1
8
1
1
1
5
29
Mote infrastructure
Comparison of Results
Wiring for traditional
structural instrumentation
+ truckload of equipment
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Cory Energy Monitoring/Mgmt
System
• 50 nodes on 4th floor
• 5 level ad hoc net
• 30 sec sampling
• 250K samples to database over 6 weeks
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29 Palms Sensorweb Experiment
•
Goals
• Deploy a sensor network onto a road from an unmanned
aerial vehicle (UAV)
• Detect and track vehicles passing through the network
• Transfer vehicle track information from the ground network
to the UAV
• Transfer vehicle track information from the UAV to an
observer at the base camp.
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Flight Data
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Dragon Wagon
HMMWV
HMMWV
From UAV
Dragon Wagon
From
UAV
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Last 2 of 6 motes are dropped
from UAV
•
8 packaged motes loaded
on plane

Last 2 of six being dropped
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Detection algorithm
Each vehicle V(v,t) has two parameters:
1) Speed (v)
2) Time at beginning of network (t)
The n-node network is described by an n-entry pattern vector p:
The jth entry is the time we expect that node j will see V(1,0)
Times when nodes detect V are collected in the t vector
 p1
p
 2
 p3
p
 4
1
 t1 
1 1 / v  t 2 

 


1  t  t3 
t 
1
 4

 Ax  b
Linear least-squares guess at v and t
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Room to spare!
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RF Sensitivity
• Pn = kBT Df Nf
• Sensitivity = Pn + SNRmin
• e.g. GSM (European cell phone standard),
115kbps
k BT
200kHz ~8x
SNR
S = -174dBm + 53 dB + 9 dB + 10 dB
= -102 dBm
RX power = ~200mW
TX power = ~4W  50 uJ/bit
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RF Path Loss
• Isotropic radiator, l/4 dipole
• Pr=Pt / (4p (d/l)n)
• Free space n=2
• Ground level n=2—7, average 4
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N=4
From Mobile Cellular
Telecommunications,
W.C.Y. Lee
Pt = 10-50W
-102dBm
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Path Loss
• Like to choose longer wavelength
• Loss ~(l/d)n
• 916MHz, 30m,  92dB power loss
•  need –92dBm receiver for 1mW xmitter
•  power!
• Penetration of structures, foliage, …
• But…
• Antenna efficiency
• Size – l/4 @ 1GHz = 7.5cm
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Output Power Efficiency
• RF
• Slope Efficiency
• Linear mod. ~10%
• GMSK ~50%
• Poverhead = 1-100mW
Pout
True
Efficiency
Slope
Efficiency
• Optical
• Slope Efficiency
• lasers ~25%
• LEDs ~80%
• Poverhead = 1uW-100mW
Poverhead
Pin
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Limits to RF Communication
Cassini
• 8 GHz (3.5cm)
• 20 W
• 1.5x109 km
• 115 kbps
• -130dbm Rx
• 10-21 J/bit
• kT=4x 10-21 J @300K
• ~5000 3.5cm photons/bit
Canberra
• 4m, 70m antennas
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Video Semaphore Decoding
Diverged beam @ 5.2 km
In shadow in evening sun
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~8mm3 laser scanner
Two 4-bit mechanical DACs
control mirror scan angles.
~6 degrees azimuth, 3 elevation
1Mbps
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Application to Microassembly
•
•
Pattern complementary
hydrophobic shapes onto parts and
substrates using SAMs.
• no shape constraints on parts
• no bulk micromachining of
substrate
• submicron, orientational
alignment
Uthara Srinivasan, Ph.D. thesis,
UC Berkeley Chem.Eng., May 2001
Courtesy: Roger Howe, UCB
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Mirrors in Solution
Courtesy: Roger Howe, UCB
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Mirrors on Microactuators
assembled mirror
Courtesy: Roger Howe, UCB
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CMOS Imaging Detector
Photosensor
CRC Check
SIPO Shift
Local Bus Driver
Register
Signal Processing
A/D Conversion
Off Chip
Bus Driver
Pixel Array
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Power and Energy
• Sources
• Solar cells ~0.1mW/mm2, ~1J/day/mm2
• Combustion/Thermopiles
• Storage
• Batteries ~1 J/mm3
• Capacitors ~0.01 J/mm3
• Usage
• Digital computation: nJ/instruction 10 pJ
• Analog circuitry: nJ/sample 27 pJ/sample
• Communication: nJ/bit 11 pJ RX, 2pJ TX
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Smart Dust - Processes (CMOS)
330µm
TX Drivers
Power input
ambient light
sensor
Photodiode
Sensor input
ADC
70kS/s,
1.8uW
0-100kbps
CCR or diode
Power
Oscillator
13 state
FSM
controller
Optical Receiver
1 Mbps, 11uW
1mm
What’s working – Oscillator, FSM, ADC, photosensor, TX drivers
What’s kind of working – Optical receiver (stability problems lead
to occasional false packets)
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Power, sensor, motor fab
Isolation trenches are etched through
an SOI wafer and backfilled with nitride
and undoped polysilicon.
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Power, sensor, motor fab
Solar cells and circuits are created
by ion implantation, drive-in, oxidation,
contact etching, aluminum sputtering
and etching.
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Power, sensor, motor fab
Actuators are deep reactive ion etched
through device layer.
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Power, sensor, motor fab
Optional backside etch (would actually precede front side etch)
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Solar Cell Results
0.5 to 100 V demonstrated
10-14% efficiency
Solar Array Performance
0.2
Current (uA)
0.1
0.0
0
5
10
15
20
25
30
-0.1
-0.2
-0.3
-0.4
Voltage (V)
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Power from MEMS Combustion
Nozzle
(w/ igniter)
Thermopiles
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Closing in on 1mm3
2.8mm
2.1mm
Solar Cells
CCR
Accelerometer
CMOS IC
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Smart Dust - Integration
Solar Cell Array
CCR
XL
CMOS
IC
SENSORS
ADC
PHOTO
8-bits
1V
1-2V
FSM
RECEIVER
375 kbps
16 mm3 total circumscribed volume
TRANSMITTER
175 bps
~4.8 mm3 total displaced volume
1V
1V
SOLAR POWER
3-8V
2V
OPTICAL IN
OPTICAL OUT
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175 bps from 10 mm3
CCR Drive Voltage
Sample from XL pad
(connected to Vdd)
Echo of
Downlink data
Sample from
photosensor
Detected Transmission
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Mote with Micro-battery from Lee & Lin, UCB
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Optical Communication
Path loss
0-25%
25%
Loss = (Antenna Gain) Areceiver / (4p d2)
Antenna Gain = 4p / q½2
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Theoretical Performance
5km
Photosensor
CRC Check
SIPO Shift
Local Bus Driver
Register
Signal Processing
A/D Conversion
Off Chip
Bus Driver
Ptotal = 50mW
Pt = 5mW
q½ = 1mrad
BR = 5 Mbps
10nJ/bit
Pixel Array
Areceiver = 1cm2
Pr = 10nW (-50dBm)
Ptotal = 50uW
SNR = 15 dB
~10,000 photons/bit
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Theoretical Performance
5m
Ptotal = 100uW
Pt = 10uW
q½ = 1mrad
BR = 5 Mbps
Areceiver = 0.1mm2
Pr = 10nW (-50dBm)
Ptotal = 50uW
SNR = 15 dB
20pJ/bit!
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RF mote
• CMOS ASIC
• 8 bit microcontroller
• Custom interface circuits
• External components
Temp
~$1
uP
SRAM
Amp ADC Radio
~2 mm^2 ASIC
battery
antenna
inductor
crystal
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Radio basics
• Tuneable frequency, 900MHz +/-100 MHz
• Programmable power output
• -10 – 0 dBm out, 1 – 10 mW in
• 100 kbps?
13 bit freq. reg.
uP
Tuneable cap.
Oscillator
core
8 bit power reg.
Tuneable power
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Radio basics
• Tuneable frequency, 900MHz +/-100 MHz
• Programmable sensitivity
• -100 – -90 dBm, 0.1 – 10 mW in
• 100 kbps?
• Many interface options
• Direct memory
• Low power vigilance?
DMA pointer
uP
Oscillator
core
SRAM
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Crystal-free radio?
• ~20% variation in frequency reference in
•
•
•
CMOS
I measure your frequency output in my
coordinate system, and vice versa
Theory of coupled oscillators
Digital feedback between nodes
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Wakeup synchronization
• Watch crystals
• 32kHz, 30nW
• 10-100 ppm drift
• 1-10 ms/min
• 1-10 sec/day
• 5-50 min/year
• Temperature is primary source of drift
• Compensate to sub-ppm – 100ppb?
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RF Mote Summary
• Available 2003
• Radio
• 900 MHz
• 10+ m range
• 10 nJ/bit (0.3mA, 100kbps)
• 8 bit Atmel-ish uP
• 10pJ/inst (0.03mA)
• 10 bit ADC
• 100kS/s, 30nJ/sample (0.01mA)
• Batteries
• Lithium coin cell ~ 220mAh
• AA batteries 1000mAh
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Abstracting the Hardware
• Goal:
• Provide realistic energy (and time) metrics to
drive algorithm development
• Allow software/algorithms to drive hardware
design.
Distributed
localization
Centralized
localization
Diffusion routing
…
Routing tables
Abstract representation of hardware
Rene mote
Mica mote
Laptops &
Wavelan
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Abstracting the Hardware
• Too simple:
• “computation” = x pJ
• Comm = y nJ/bit*m^4
• Sensing = z pJ/sample
• Too complex:
• 16 bit add register to non-cached main
memory = x pJ,
• …
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Abstracting the Hardware
• Need a representation(s) of
• Energy cost
• Latency
• Probabilistic?
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Example: maximize sensor net
lifetime
• Given:
• Costs of sensing, computation, communication
• Fixed sensor locations
• Connectivity matrix
• One or more base stations
• Find:
• Energy-optimal routing to get data back from each
node (define it first!)
• Everyone on all the time
• Duty cycling
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Example: minimal coverage
• Given:
• Costs of sensing, computation, communication
• Sensor range, communication range
• Mote weight dominated by battery
• Find:
• Minimal dispersion of motes (in kg/km2 !) st.
events x,y,z can be sensed for time t
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Example: minimal coverage
• Workstation?
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Example: minimal coverage
• Smart dust?
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Example: minimal coverage
• Some of both?
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Mobility
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Other topics
• Simulation of big networks
• Data fusion/compression
• Information theory
• Shannon for sensor networks
• What is “capacity”?
• Collaborative signal processing
• Definition
• Existence?
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Summary
• Cubic-inch RF motes working in applications
• 10 mm3 optical motes demonstrated
• 10 mm3 RF motes coming
• Peer-to-peer networking
• Most communication is relay
• Energy cost to communicate 1 bit is at least
1000x greater than an 8 bit instruction
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Conclusion
1
3
mm
or bust!!!
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