1. SMART DUST Optical Data Exfiltration from Microsensor Networks (smart dust by any other name…) K. Pister EECS, BSAC UC Berkeley

2. SMART DUST DoD Workshops RAND 1992 “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 …

3. SMART DUST University Programs UCLA Bill Kaiser (LWIM, WINS) Greg Pottie (AWAIRS) U. Michigan Ken Wise USC Deborah Estrin UCB K. Pister (Smart Dust) …

4. SMART DUST Ken Wise, U. Michigan http://www.eecs.umich.edu/~wise/Research/Overview/wise_research.pdf

5. SMART DUST Smart Dust Goals Autonomous sensor node (mote) in 1 mm 3 MAV delivery Thousands of motes Many interrogators Demonstrate useful/complex integration in 1 mm 3

6. SMART DUST ’01 Goal

7. 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 control: nJ/instruction (e.g. strongARM) Analog circuitry: nJ/sample (e.g. video ADC) Communication: nJ/bit (non-trivial)

8. SMART DUST Solar Power Silicon maple seeds Silicon dandelions

9. SMART DUST Combustion Solid rocket propellant integrated igniter thermoelectric generator

10. SMART DUST COTS Dust GOAL: Get our feet wet RESULT: Cheap, easy, off-the-shelf RF systems Fantastic interest in cheap, easy, RF: Industry Berkeley Wireless Research Center Center for the Built Environment (IUCRC) PC Enabled Toys (Intel) Fantastic RF problems Optical proof of concept

11. SMART DUST COTS Dust - RF Motes Atmel Microprocessor RF Monolithics transceiver 916MHz, ~20m range, 4800 bps 1 week fully active, 2 yr @1% N S EW 2 Axis Magnetic Sensor 2 Axis Accelerometer Light Intensity Sensor Humidity Sensor Pressure Sensor Temperature Sensor

12. SMART DUST weC Mote RF programmable Light, temperature sensors 3 color LEDs Integrated antenna Endeavour buy-in Prof. David Culler and students TOS ( Tiny OS ) Center for the Built Environment interest Designed by James McLurkin and Seth Hollar

13. SMART DUST Decentralized Network Growing Number Of Motes=128 (McLurkin)

14. SMART DUST Recovering Flow from Distributed Networks Sense temperature at nodes Interpolate to grid pointsCompute flow In a dense sensor scenario, environmental data can be interpolated Over a few time steps, optical flow algorithms are applied to determine flow Accuracy of results is highly dependent on the smoothness of the flow (Doherty/ Teasdale)

15. SMART DUST Position Estimation by Convex Optimization Use known positions (red nodes) and communication distance constraints (blue lines) to locate unknown positions (blue node) Solve using Semidefinite Programming (SDP) for many constraints simultaneously More connections  smaller intersection of convex sets Minimization in SDP gives smallest bounding ellipse around feasible set (dashed blue line around yellow region) (Doherty/El Ghaoui)

16. SMART DUST Exploiting Sensor Correlation to Reduce Network Bandwidth PC All nodes sense with n bits of precision Assuming that adjacent nodes have correlated sensor readings, <n bits need be communicated for each node In example, blue nodes are correlated to the adjacent red node and less information is transmitted back to the PC (Doherty/Ramchandran)

17. SMART DUST Low Power Radio Projects LWIM (Bill Kaiser, UCLA) 902-928MHz, 1mW goal 1-1-1 SHARC (Tom Lee, Stanford) 1 GHz, 1mW, 1mm 2 goal picoRadio (Rabaey/ Brodersen, BWRC, UCB) 100uW, 0.1nJ/bit goal …(dozens more)

18. 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 drain= ~200mW  2uJ/bit TX power drain= ~4W  40 uJ/bit

19. SMART DUST RF Path Loss Isotropic radiator, /4 dipole P r =P t / (16   (d/ ) n ) Free space n=2 Ground level n=2—7, average 4

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

21. 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 – /4 @ 1GHz = 7.5cm

22. 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

23. SMART DUST Cassini Limits to RF Communication 8 GHz (3.5cm) 20 W 1.5x10 9 km 115 kbps -130dbm Rx 10 -21 J/bit kT=4x 10 -21 J @300K ~5000 3.5cm photons/bit Canberra 4m, 70m antennas

24. SMART DUST Maxell (Hitachi) RF ID Chip

25. SMART DUST 1000 bits, 100m, ground Sensitivity = k B T  f N f SNR min k B T 1kHz 10x limit SNR S = -174dBm + 30 dB + 10 dB + 10 dB = -124 dBm Path loss = 16  2 (d/ ) 4 /G ant (min=1) = 22dB + 40dB log 10 300 – G ant = 122 dB – G ant Transmit 1mW, receive –122dBm  OK 1uJ/bit fundamental Tx cost.

26. SMART DUST 1000 bits, 100m, ideal Sensitivity = k B T  f N f SNR min k B T 1kHz at limit coding wizards S = -174dBm + 30 dB + 0 dB + 0 dB = -134 dBm Path loss = 16  2 (d/ ) 2 /G ant (UAV) = 22dB + 20dB log 10 300 – 6 (dipole) = 66 dB Transmit 1nW, receive –126dBm  OK 1pJ/bit fundamental Tx cost.

27. SMART DUST 1000 bits, 100m, Bluetooth Sensitivity = -75 dBm (standard) k B T 1MHz lousy radios OK! S = -174dBm + 60 dB + 39 dB Path loss = 10 dB/desk? /wall? Transmit 1mW for 1ms 1nJ/bit fundamental Tx cost. actual Tx, Rx power drain ~100mW 100nJ/bit, 10s of meters?

28. SMART DUST RF Sensor Future RF tags + Sensors Ultra Wide Band 10ps? digital pulse trains LLNL 60 GHz Major path loss problems But oh, the bandwidth! MEMS RF components Mechanical filters already dominate RF Never ever bet against Pisano and Howe

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

30. SMART DUST COTS Dust - Optical Motes Laser mote Trans-bay comm (26km) 2 day life full duty 4bps, huge SNR CCR mote Trans-lab comm (5m) 4 corner cubes 40% hemisphere

31. SMART DUST Video Semaphore Decoding Diverged beam @ 300m Shadow or full sunlight Diverged beam @ 5.2 km In shadow in evening sun

32. SMART DUST CCR Interogator

33. SMART DUST Micro Mote - First Attempt

34. SMART DUST Micro Mote - Second Attempt

35. SMART DUST 1 Mbps CMOS imaging receiver

36. SMART DUST 6-bit DAC Driving Scanning Mirror Open loop control Insensitive to disturbance Potentially low power

37. SMART DUST ~8mm 3 laser scanner Two 4-bit mechanical DACs control mirror scan angles. ~6 degrees azimuth, 3 elevation

38. SMART DUST Theoretical Performance P total = 50mW P t = 5mW  ½ = 1mrad  G ant = 71dB BR = 5 Mbps A receiver = 1cm 2 P r = 10nW (-50dBm) P total = 50uW /pixel SNR = 15 dB ~10,000 photons/bit 5km 10nJ/bit

39. 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!

40. SMART DUST Satellite Imagery

41. SMART DUST Theoretical Performance P total = 50mW P t = 5mW  ½ = 1mrad BR = 2 Mbps A receiver = 1m 2 P r = 10nW (-50dBm) P total = 50uW /pixel SNR = 17 dB 500km 25nJ/bit!

42. SMART DUST Conclusion Grit your teeth and use the radio 50uJ/bit 1-10km 100nJ/bit 0-50m Unless you’re lucky enough to have line of sight: Use optical comm when possible 10nJ/bit 1-10km 20pJ/bit 0-50m

43. SMART DUST Teaming Endeavour CBE BWRC

44. SMART DUST Battery Energy AA Hearing Aid Rechargeable Lead Acid

45. SMART DUST Optical Receiver Noise Thermal noise from amplifier I nt 2 = 4kTB/R Shot noise from Background light photocurrent Signal light photocurrent Diode leakage I ns 2 = 2 q I d B

46. SMART DUST Video Semaphore Decoding Diverged beam @ 300m Shadow or full sunlight Diverged beam @ 5.2 km In shadow in evening sun

47. SMART DUST Optical Communication Hardware Laser Imager

48. SMART DUST 2D beam scanning laser lens CMOS ASIC Steering Mirror AR coated dome

49. SMART DUST Distributed Algorithms Centroid Location Find edges Diffuse pheromone from the edges inward Find the lowest concentration using Min/Max sharing If you have the lowest concentration, turn yellow (James McLurkin) Number Of Motes=500 Communications Range=.8

50. SMART DUST Mote Position Estimation Give GPS receivers to some motes and call them “Basis Motes”. Ask them to turn gray. Each Basis Mote diffuses it’s own pheromone throughout the group The position of any other mote can be estimated from the levels of basis pheromones present.

51. SMART DUST Lots of exponentials Digital circuits Speed, memory Size, power, cost Communication circuits Range, data rate Size, power, cost MEMS Sensors Measurands, sensitivity Size, power, cost

52. SMART DUST Sensor Networks as a Vision Problem Randomly arranged sensors are just “pixels” Borrow/steal/apply many vision tricks directly. (Doherty/ Teasdale)