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 Bill Kaiser, UCLA

SMART DUST August ’01 Goal

SMART DUST COTS Dust - RF Motes Simple computer Cordless phone radio Up to 2 year battery life N S EW 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 ~ $ 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). Wiring for traditional structural instrumentation + truckload of equipment Mote infrastructure ` 5` Mote Layout Comparison of Results

SMART DUST Cory Energy Monitoring/Mgmt System 50 nodes on 4 th 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 From UAV Dragon Wagon HMMWV From UAV HMMWV

SMART DUST Last 2 of 6 motes are dropped from UAV 8 packaged motes loaded on plane n 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 j th 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 Linear least-squares guess at v and  t

SMART DUST Room to spare!

SMART DUST RF Sensitivity P n = k B T  f N f Sensitivity = P n + SNR min e.g. GSM (European cell phone standard), 115kbps k B T 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, /4 dipole P r =P t / (4  (d/ ) n ) Free space n=2 Ground level n=2—7, average 4

SMART DUST N=4 From Mobile Cellular Telecommunications, W.C.Y. Lee P t = 10-50W -102dBm

SMART DUST Path Loss Like to choose longer wavelength Loss ~(  d) n 916MHz, 30m,  92dB power loss  need –92dBm receiver for 1mW xmitter  power! Penetration of structures, foliage, … But… Antenna efficiency Size – 1GHz = 7.5cm

SMART DUST Output Power Efficiency RF Slope Efficiency Linear mod. ~10% GMSK ~50% P overhead = 1-100mW Optical Slope Efficiency lasers ~25% LEDs ~80% P overhead = 1uW-100mW Slope Efficiency True Efficiency P in P out P overhead

SMART DUST Cassini Limits to RF Communication 8 GHz (3.5cm) 20 W 1.5x10 9 km 115 kbps -130dbm Rx J/bit kT=4x ~ cm photons/bit Canberra 4m, 70m antennas

SMART DUST Video Semaphore Decoding Diverged 5.2 km In shadow in evening sun

SMART DUST ~8mm 3 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

SMART DUST Power and Energy Sources Solar cells ~0.1mW/mm 2, ~1J/day/mm 2 Combustion/Thermopiles Storage Batteries ~1 J/mm 3 Capacitors ~0.01 J/mm 3 Usage Digital computation: nJ/instruction Analog circuitry: nJ/sample Communication: nJ/bit 10 pJ 27 pJ/sample 11 pJ RX, 2pJ TX

SMART DUST Smart Dust - Processes (CMOS) 13 state FSM controller ADC 70kS/s, 1.8uW ambient light sensor Photodiode Sensor input Oscillator Power input Power TX Drivers 0-100kbps CCR or diode Optical Receiver 1 Mbps, 11uW 1mm 330µm 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 Actuators are deep reactive ion etched through device layer. Power, sensor, motor fab

SMART DUST Optional backside etch (would actually precede front side etch) Power, sensor, motor fab

SMART DUST Solar Cell Results 0.5 to 100 V demonstrated 10-14% efficiency

SMART DUST Power from MEMS Combustion Thermopiles Nozzle (w/ igniter)

SMART DUST Closing in on 1mm 3 2.8mm 2.1mm CCR Accelerometer Solar Cells CMOS IC

SMART DUST Smart Dust - Integration Solar Cell ArrayCCR XL CMOS IC

SMART DUST 175 bps from 10 mm 3 CCR Drive Voltage Detected Transmission Sample from XL pad (connected to Vdd) Echo of Downlink data Sample from photosensor

SMART DUST Mote with Micro-battery from Lee & Lin, UCB

SMART DUST Optical Communication 0-25%25% Path loss Loss = (Antenna Gain) A receiver / (4  d 2 ) Antenna Gain = 4  /  ½ 2

SMART DUST Theoretical Performance P total = 50mW P t = 5mW  ½ = 1mrad BR = 5 Mbps A receiver = 1cm 2 P r = 10nW (-50dBm) P total = 50uW SNR = 15 dB ~10,000 photons/bit 5km 10nJ/bit

SMART DUST Theoretical Performance P total = 100uW P t = 10uW  ½ = 1mrad BR = 5 Mbps A receiver = 0.1mm 2 P r = 10nW (-50dBm) P total = 50uW SNR = 15 dB 5m 20pJ/bit!

SMART DUST ~2 mm^2 ASIC RF mote CMOS ASIC 8 bit microcontroller Custom interface circuits External components uP SRAM Radio ADC Temp Amp inductor crystal battery antenna ~$1

SMART DUST Radio basics Tuneable frequency, 900MHz +/-100 MHz Programmable power output -10 – 0 dBm out, 1 – 10 mW in 100 kbps? Tuneable cap. Oscillator core Tuneable power 13 bit freq. reg. 8 bit power reg. uP

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? Oscillator core DMA pointer uP 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 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. Rene mote Mica mote Laptops & Wavelan Abstract representation of hardware Diffusion routing Routing tables … Centralized localization Distributed localization

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/km 2 !) 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 mm 3 optical motes demonstrated 10 mm 3 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 mm 3 or bust!!!