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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
SMART DUST
Motivation
• Exponential decrease in size, power, cost
• Digital computation
• Analog/RF communication
• Sensors
 battery
Goals
• Understand fundamental limits
• Build working systems
SMART DUST
Moore’s Law, take 2
• Nanochips on a dime (Prof. Steve Smith, EECS)
SMART DUST
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
•…
SMART DUST
University Programs (old slide)
• UCLA
• Bill Kaiser (LWIM, WINS)
• Greg Pottie (AWAIRS)
• U. Michigan
• Ken Wise
• USC
• Deborah Estrin
• UCB
• K. Pister (Smart Dust)
•…
SMART DUST
•
Ken Wise, U. Michigan
SMART DUST
http://www.eecs.umich.edu/~wise/Research/Overview/wise_research.pdf
Bill Kaiser, UCLA
•
http://www.janet.ucla.edu/WINS
SMART DUST
August ’01 Goal
SMART DUST
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
SMART DUST
COTS Dust
GOALS:
• Create a network of sensors
• Explore system design issues
SMART DUST
COTS Dust
RESULTS:
• TinyOS – David Culler, UCB
• Manufactured by Crossbow ~ $150
• 100+ users, 40+ locations
• Military and civilian applications
SMART DUST
800 node demo at Intel Developers Forum
4 sensors
$70,000 / 1000
Concept to demo in 30 days!
SMART DUST
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
SMART DUST
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
SMART DUST
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.
SMART DUST
Flight Data
SMART DUST
Dragon Wagon
HMMWV
HMMWV
From UAV
Dragon Wagon
From
UAV
SMART DUST
Last 2 of 6 motes are dropped
from UAV
•
8 packaged motes loaded
on plane

Last 2 of six being dropped
SMART DUST
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  t2 

 


1  t  t3 
t 
1
 4

 Ax  b
Linear least-squares guess at v and t
SMART DUST
Room to spare!
SMART DUST
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
SMART DUST
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
SMART DUST
N=4
From Mobile Cellular
Telecommunications,
W.C.Y. Lee
Pt = 10-50W
-102dBm
SMART DUST
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
SMART DUST
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
SMART DUST
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
SMART DUST
Video Semaphore Decoding
Diverged beam @ 5.2 km
In shadow in evening sun
SMART DUST
~8mm3 laser scanner
Two 4-bit mechanical DACs
control mirror scan angles.
~6 degrees azimuth, 3 elevation
1Mbps
SMART DUST
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
SMART DUST
Mirrors in Solution
Courtesy: Roger Howe, UCB
SMART DUST
Mirrors on Microactuators
assembled mirror
Courtesy: Roger Howe, UCB
SMART DUST
CMOS Imaging Detector
Photosensor
CRC Check
SIPO Shift
Local Bus Driver
Register
Signal Processing
A/D Conversion
Off Chip
Bus Driver
Pixel Array
SMART DUST
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
SMART DUST
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)
SMART DUST
Power, sensor, motor fab
Isolation trenches are etched through
an SOI wafer and backfilled with nitride
and undoped polysilicon.
SMART DUST
Power, sensor, motor fab
Solar cells and circuits are created
by ion implantation, drive-in, oxidation,
contact etching, aluminum sputtering
and etching.
SMART DUST
Power, sensor, motor fab
Actuators are deep reactive ion etched
through device layer.
SMART DUST
Power, sensor, motor fab
Optional backside etch (would actually precede front side etch)
SMART DUST
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)
SMART DUST
Power from MEMS Combustion
Nozzle
(w/ igniter)
Thermopiles
SMART DUST
Closing in on 1mm3
2.8mm
2.1mm
Solar Cells
CCR
Accelerometer
CMOS IC
SMART DUST
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
SMART DUST
175 bps from 10 mm3
CCR Drive Voltage
Sample from XL pad
(connected to Vdd)
Echo of
Downlink data
Sample from
photosensor
Detected Transmission
SMART DUST
Mote with Micro-battery from Lee & Lin, UCB
SMART DUST
Optical Communication
Path loss
0-25%
25%
Loss = (Antenna Gain) Areceiver / (4p d2)
Antenna Gain = 4p / q½2
SMART DUST
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
SMART DUST
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!
SMART DUST
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
SMART DUST
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
SMART DUST
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
SMART DUST
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
SMART DUST
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?
SMART DUST
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
SMART DUST
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
SMART DUST
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,
• …
SMART DUST
Abstracting the Hardware
• Need a representation(s) of
• Energy cost
• Latency
• Probabilistic?
SMART DUST
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
SMART DUST
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
SMART DUST
Example: minimal coverage
• Workstation?
SMART DUST
Example: minimal coverage
• Smart dust?
SMART DUST
Example: minimal coverage
• Some of both?
SMART DUST
Mobility
SMART DUST
Other topics
• Simulation of big networks
• Data fusion/compression
• Information theory
• Shannon for sensor networks
• What is “capacity”?
• Collaborative signal processing
• Definition
• Existence?
SMART DUST
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
SMART DUST
Conclusion
1
3
mm
or bust!!!
SMART DUST