1. Embedded Computing Seminar Noam Sapiens

2. Outline What is smart dust? Characteristics Applications Military Commercial Requirements and restrictions Analysis of smart dust communication General architecture and design What we have today Would like to have References

3. What is Smart Dust? Large scale networks of wireless sensors for various applications The three key capabilities of smart dust are: Sensory capabilities Processing capabilities Communication capabilities

4. Smart dust characteristics A system is made of one or a few base stations (interrogators) and as many smart dust motes as possible or required Ubiquitous – sensors of different types Very task/application oriented design and performance Wireless communication Self-organizing, self-optimizing, self-configuring, self-sustaining. Very small (should be under 1mm 3 ) Low power consumption Easy to deploy Based on current or very near future components

5. Military and Space applications Internal and external spacecraft monitoring Meteorological and seismological monitoring in difficult terrain and environments Land/space communication Chemical/biological environment sensing Meteorological sensing – for better aiming of guns and artillery Autonomous vehicles external aid

6. Surveillance Sensors minefield e.g. smart clear tracks on borders Urban engagement (cont. DARPA funding in 2005) Motion detection and enemy numbers Bunker/building mapping Peace time/treaty monitoring Intelligence in hostile areas/behind enemy lines Transportation monitoring and traffic mapping Missile hunting Monitoring soldier vitals and injury Pursuit aid

7. Unmanned pursuit Aerial smart dust deployment in the area of interest – ground and air Sensors: Each mote has motion detectors and a small CMOS camera Some motes has GPS Computation: Image processing for target distinction Communication: Ad-hoc networking Relative localization Local coordinate system Energy tradeoff UC Berkeley and MLB Co. Northwestern university Integration of several smart dust experiments

8. UC Berkley PEG (pursuit-evasion game) experiment 200 sensors network One aerial and three ground unmanned vehicles – pursuers One ground unmanned – evader Pursuers are interrogators of the sensor network deployed Sensor networks roles: Provide complete monitoring of the environment, overcoming the limited sensing range of on board sensors Relay secure information to the pursuers to design and implement an optimal pursue strategy Provide guidance to pursuers, when GPS or other navigation sensors may fail UC Berkeley

9. Evader Dynamics Sensor Network Pursuer Dynamics GPS Pursuer Tracking control Evader motion estimator Pursuit Strategy Experiment block diagram

10. Methodologies Autonomous sensor nodes deployed Target vehicles traverse sensor field Clumps of sensors exchange information Data association from local information Clump estimates target heading, speed, position Computations use robust Closest Point of Approach statistics Target parameters used to match existing tracks Euclidean metric finds track with best-fit New parameters merged with existing ones Track information reported to user workstation Track information propagated in advance of target Diffusion routing limits information propagation Difficult global problem decomposed into tractable local problems

11. Target detectedNodes exchange readingsClump head selected Track initiated and users told Track info propagated Target moves and detected Readings exchanged Clump head chosen Track updated and user toldTrack info propagated Recourse Tracking process demonstration Penn State University and DARPA

12. Experimental results Unsuccessful tracking Successful tracking Penn State University and DARPA

13. Commercial applications Games and sports Traffic monitoring Inventory control Security Identification and tagging Predictive maintenance Product quality control Industrial facilities Vehicles and systems Appliances Agriculture

14. Building management Energy management Temperature control Lighting control Fire systems Smart office spaces Computer interface Virtual keyboard 3D virtual sculpturing Health, medicine and wellness Handicap aid

16. Requirements Perform a specific task according to the application Sense as defined by the task profile (different types of detectors – will not be discussed in this talk) Perform basic computations – digitization, noise filtering, DSP, FFT, image processing, decision making, localization, etc… Establish ad-hoc communication in a physical environment Base station communication and peer to peer Ranges between a few meters (between motes) and over a km (motes to base station) Multi-hop routing (if required) Self configuration and optimization

17. Restrictions Mote volume will not exceed 1mm 3 A single mote is probably restricted to few sensory capabilities Energy restrictions Battery ≈ 1J/mm 3 (about 10  W for a day) Capacitors ≈ 1mJ/mm 3 Solar cells ≈ 1J/day (sun) or ≈1mJ/day (room light) Vibrations ≈ 0.4-30  W (depends on amplitude and frequency) Thermopile ≈ 0.4-2  W @ 25-37  C Very low cost motes (enable large scale distribution) No science fiction technologies

18. Some basic energy data Digital calculations (e.g. writing/reading to/from memory, magnetic (memory) or electronic (transistors and gates) manipulations, Boolean, arithmetic etc.) ~1pJ/bit Analog circuitry (e.g. amplification) ~1nJ/amp DAQ ~1nJ/sample (or passive in some sensors) A/D and D/A ~1nJ/instruction MEMs control ~ 1pJ/bit @ 1kb/sec

19. Analysis of smart dust communication RF vs. Optical RF – radio frequency MHz – hundreds of GHz  1mm – 100s meters wavelength Technologies: Bluetooth Cell phones (GSM, CDMA, etc.) RFID Optical 100THz – 1PHz  0.3  - 1.6  wavelength Lasers and LEDs

20. RF Pros Well developed technologies Multiplexing techniques: TDMA, FDMA, CDMA. Does not require line of sight Not much affected by the environment Cons Antenna size (has to be at least ¼ of the wavelength) Complex circuitry (modulation/demodulation, bandpass filters, etc.) Energy consumption (approx. 100nJ/bit)

21. Optical Pros Low energy consumption (<1nJ/bit) High data rates Small aperture, very directional (localization) Spatial division multiplexing Cons Very directional Line of sight Atmospheric turbulence, weather and environmental conditions dependent

22. General smart dust mote architecture - optical

23. MEMs controlled corner cube retro-reflector Perfectly aligned corner cube reflects light at the exact same direction of incidence MEMs control of one of the corner cube side’s alignment enables modulation Energy consumption of about 1pJ/bit @ 1kb/sec Range up to 1km UC Berkeley

24. Smart dust active transmitter Incorporates a laser, lens and a MEM steering mirror 1mrad transmission Data rate of approx. 5Mb/sec Energy consumption depends on distance and detector size Distance Detector area Energy consumption 5m0.1mm 2 ~20pJ/bit 5km1cm 2 ~10nJ/bit 500km1m 2 ~25nJ/bit 1mW at 1mrad laser is 40 times brighter than 100W light bulb

25. SEM view Laser diode Lens MEM mirror Optical view UC Berkeley

26. The different parts The laser diode The micro-lens The MEM mirror

27. Experimental results Beam steering at kHz rates Steering in approx 1str ≈ 60  X 60  300m Link test 5.2 km Berkeley Marina 15.3 km Coit Tower 14  W laser 8mW laser

28. The base station Hand held Binoculars Palm Cell phone Laptop computer Command center Unmanned vehicle (land, sea, air) Autonomous systems

29. Base station architecture Optical interrogation – principles of operation Camera Lens Beam Mirror Polarizing Beam Splitter Quarter-wave Plate Filter Laser Expander Smart dust For example: FOV=17mX17m CMOS is 256X256, 43  2 pixels Range = 2km f Lens =20cm Spatial resolution = 6.6cm 2 range Space division multiplexing

30. Airborne base station example UC Berkeley and MLB Co.

31. Challenges for mobile networking for smart dust Line of sight requirement Link directionality Parallel readout and cross talk Trade-offs Revisit rates

32. Line of sight requirement Optical communication requires photons from the transmitter reach the receiver – photons travel in straight lines Line of sight is not the only way of making the photons arrive at a desired location: Diffuse reflections – low energy, wide spread (the entire FOV) and low contrast with the environment (especially with interrogating beam) Non fixed smart dust systems - line of sight could be achieved intermittently Ad hoc multi-hop routing Cannot work with passive communication, very small SNR Latency Algorithms Latency Reliability

33. Link directionality Passive links A corner cube retro-reflector angle of acceptance is 10-20  Placing multiple corner cubes Placing the corner cube and the receiver on a MEM mount – signal maximization Increase mote density – high probability for communication with at least some motes in the area of interest General Motes are unaware of neighbors location Base station can disseminate location information to motes

34. Active links Mote receiver is omnidirectional within a hemisphere Enables mote attention without aiming No source identification Making the receiver directional (by adding a lens) and connecting its directionality to the transmitter will enable communication automatically to the source Requires aiming Solved by increasing the density of motes In a static system, identification could be saved in mote memory Difference between receiver and transmitter angular spreads leads to non-reciprocal linking

35. Formation of smart dust self-organizing networks Dynamic Sensor Network Configurable Distributed Services Self-Organizing Sensor Application Systems Distributed Lookup Service Distributed Composition Service Distributed Adaptation Service Collaborative Signal Processing Sensor Data Repository Manager Distributed Sensor Query Processing Database Server User GUI Remote service execution Event notifications Mote service discovery Change detection scheme Trigger management User-defined adaptation handler Event-based Diffusion Network Group management Dataflow and group structure Group communication Group reconfiguration DARPA/ITO

36. Diffusion networks Assume self awareness Scan for neighbors according to criteria and scanning algorithms Notify neighbors of your existence Notify previously know neighbors about new neighbors found Resembles “click a link” internet browsing

37. Lookup services Use specific motes as lookup servers for mapping the network Disseminate lookup information to relevant motes Use region filters to reduce network traffic and avoid irrelevant connections Average Response DelayAverage Network Traffic Client Average Throughput Experimental results DARPA/ITO Resembles for example ‘Google’ crawlers

38. Composition Services Use specific motes as composition servers Create groups dynamically for local collaboration Maintain group communication Connection between tasks Data flow monitoring and control (split, merge, filter, buffer) Composition Server Failed Reconfigure DARPA/ITO

39. Adaptation Services Application renders a condition trigger, and adaptation handler changes network algorithms Dynamic steering: Distributed sensor applications steer around changes in the sensor network, such as mobility, failure, density, certainty, and reconfiguration Dynamic clustering: Active re-clustering of sensors based on density and level of activities to reduce collaborative processing and communication costs Dynamic tasking: Implement changes in task requirements of fielded sensors by dynamically downloading and executing codes to targeted sensors Distributed Lookup Service Distributed Adaptation Service Available Algorithms Trigger Maintenance Monitoring Facilities Adaptation Handler Application DARPA/ITO

40. Parallel readout and crosstalk The network architecture of smart dust enables space division multiplexing in the base station There are as many channels as there are pixels in the CMOS camera of the base station If the interrogating beam is divergent enough several motes could be ready simultaneously A base station will not distinguish between motes in the same space equivalent pixel TDMA could be incorporated in the architecture – modulation of the interrogating beam could establish a clock for synchronization Demand access method (as in cellular and satellite networks) could be implemented as well – a mote sends an active short pulse to the base station will receive attention by the interrogation beam of the base station

41. Trade-offs SNR – signal to noise ratio, governs the probability for bit error P t – average transmitter power A – receiver area N 0 – receiver inherent noise B – bit rate r – the distance between the transmitter and receiver  - beam divergence

42. Revisit rate Revisit rate should be application specific Use of AI – learning system Frequent revisits to areas in which changes happen most rapidly Could be based on human judgment or automatic Could be based on the demand access method

43. What we have today www.xbow.com www.dust-inc.com Different markets Airborne systems – monitoring, camera stability, unmanned… Marine Land vehicles Environment Mote price ~100$ Kit price (8-12 motes) ~ 2000$ Building management Industrial monitoring Security

44. Smart dust assembly – no science fiction technologies Full clip UC Berkeley

45. A different type of smart dust – is it really? Chemical sensor active smart dust – based on material engineering Chemical sensorsChemical containment Water drops manipulation UC San Diego

46. Would like to have capabilities (a partial list) Miniaturization of available smart dust and extreme price reduction Possibility of optical pre-processing and optical circuits Incorporate the concept of smart dust societies – integration of different types of smart dust Requires more robust network protocols Requires better definition of mote task Enables complex systems easy distribution Enables smaller and cheaper motes

47. Multi wavelength VCSEL arrays will enable smart dust WDM capabilities Beam quality control (divergence) – for easier scanning Electro-optic instead of MEMs Higher bit rate (will be required for very large networks) Lower energy (about 20pJ/bit @ 10Mb/sec) Active smart dust – interfaces, robotic capabilities and motion Rocket chip UCSD

48. References JM Kahn, RH Katz & KSJ Pister, “Emerging challenges: mobile networking for smart dust”, J. of Comm. and Net. 2 pp.188-196 (2000) Y Song, “Optical Communication Systems for Smart Dust”, M.Sc. Thesis, Virginia polytechnic institute and state university, 2002 The following urls: http://www.darpa.mil/ http://robotics.eecs.berkeley.edu/~pister/SmartDust/ http://www-bsac.eecs.berkeley.edu/archive/users/warneke- brett/SmartDust/index.htmlhttp://www-bsac.eecs.berkeley.edu/archive/users/warneke- brett/SmartDust/index.html http://www.xbow.com/ http://www.dust-inc.com/ http://chem-faculty.ucsd.edu/sailor/research/highlights.html http://www.nanotech-now.com/smartdust.htm