Minimalist Architectures for Large-Scale Sensor Networks

1. Minimalist Architectures for Large-Scale Sensor Networks Upamanyu Madhow ECE Department University of California, Santa Barbara Funding Sources

2. Research Overview Today’s focus • Sensor Networks • Scalability: size and energy • Camera Networks • Fundamentals of Tracking • Nextgen Wireless • Millimeter wave communication • Indoor WPAN: Gigabit speeds • Outdoor LOS: Multigigabit speeds • Scalability and QoS in multihop wireless networks • Cognitive radio architectures and signal processing • Multimedia security • Data hiding • Steganalysis

3. Collaboration is key to progress Electronics & Photonics (Prof. Mark Rodwell--ECE) Imaging Sensor Nets Distributed Beamforming Computer Vision (Prof. B. S. Manjunath--ECE) Multimedia Security Wireless QoS Ad hoc networks (Prof. Elizabeth Belding-Royer--CS) Tracking with Binary Sensors Controls (Prof. Joao Hespanha--ECE) Distributed Compression Computational Geometry (Prof. Subhash Suri--CS) Camera networks Source Coding (Prof. Ken Rose--ECE) Ultra high-speed wireless comm Signal Processing (Prof. Kannan Ramchandran--Berkeley) Cognitive radio

4. Who is doing the work? • Bharath Ananthasubramaniam: Signal Processing for Imaging Sensor Nets • Ibrahim El-Khalil: Data hiding and steganalysis • Raghu Mudumbai: Distributed beamforming, camera nets, tracking, cross-layer design • Mike Quinn: Camera networks • Anindya Sarkar: Data hiding and steganalysis • Munkyo Seo: IC design for Imaging Sensor Nets • Jaspreet Singh: Distributed compression, high-speed comm • Sumit Singh: Wireless QoS, protocols for mm wave radio • Ben Wild (UC, Berkeley): Distributed beamforming prototype

5. Sensor Nets: the CENS view Slide courtesy of Dr. Deborah Estrin (CENS-UCLA): http://cens.ucla.edu • Micro-sensors, on-board processing, wireless interfaces feasible at very small scale--can monitor phenomena “up close” • Enables spatially and temporally dense environmental monitoring Embedded Networked Sensing will reveal previously unobservable phenomena Ecosystems, Biocomplexity Contaminant Transport Marine Microorganisms Seismic Structure Response

6. Sensor Nets Today • Berkeley motes continue to prove their worth • Impact on science • Promising for DoD and security applications • Scalability of flat architectures limited to 100s of nodes • Enough for many applications • Hierarchical architectures can help • But we are far from the sci-fi vision of Smart Dust • Hundreds of thousands of randomly deployed sensors • Dumb sensors that get smarter by working together

7. Scale requires minimalistic design • Scaling in Space • Sensors have small coverage area (e.g., bio/chem) • Large areas must be covered • Large deployments must be automated • Need minimalistic network protocols • Energy Scaling • Need long battery life or batteryless operation • Minimalistic mechanisms for cooperative communication can significantly enhance performance • Scaling in functionality • Small, inexpensive, noisy, failure-prone microsensors • Need minimalistic sensing models

8. Today’s talk Students Involved Bharath Ananthasubramaniam, Munkyo Seo • Spatial scaling: Imaging sensor nets • Have been talking about concept since 2004 • Today: ongoing prototyping effort • Energy scaling: Distributed transmit beamforming • Gains from non-ideal beamforming explored in 2004 • Today: a method that works and prototyping results • Cost/functionality scaling: Tracking with binary sensors • What can we do if a sensor can only say yes or no? • Today: Fundamental limits, minimal descriptions, algorithms Raghu Mudumbai, Ben Wild Raghu Mudumbai, Nisheeth Srivastava

9. Imaging Sensor Nets

10. Imaging Sensor Nets: the Concept collector: satellite base station on UAV vast numbers of low-complexity "dumb" pixelssensor + RF transducer + antenna. Sensor field Sensor field Field of simple, low-power sensors dispersed across field of viewCast on ground from truck, plane, or satellite Sensor as pixels (“dumb dust”) Electronically reflect, with data modulation, beacon from collector (“virtual radar”) Minimal functionality: no GPS, no inter-sensor networking Lifetime of year on watch cell battery Sophisticated collector Radar and image processing, multiuser data demodulation Joint localization and data collection Range varies from 100m to 100 km Active versus “passive” sensors, collector characteristics

11. Prototype with stationary collector Millimeter wave carrier frequencies Narrow beam with moderate size collector antenna Small sensor form factor Key challenges Low-power, low-cost sensor ICs: mm-wave in CMOS Collector signal processing

12. Inducing a radar geometry Beacon with location code Active sensor reflects beacon Collector Sensor field with active sensors and inactive sensors

13. DOWN-LINK Freq. shift to filter out ground return PRBS BPSK modulation DATA UP-LINK Jointly detect DATA and DELAY Sensor Collector Basic Link Diagram

14. Hierarchy of Challenges Application interface Imaging Algorithm Nonideal beam patterns Integrating soft info Signal/Image Processing Detection/Estimation Estimation for asynchronous modulation Limited precision samples Sensor architecture Link budget calculation Collector phased array design Link-level Mm-wave Design Circuit-level Low-power, mm-wave circuit design Efficient, low-loss antenna Interconnect Technology-level IC technology Substrate/Package/Integration

15. Sensor IC functionality Semi-Passive Implementation Amplify-Reflect Implementation IC implementation in MOSIS-accessible CMOS and SiGe processes Very small antennas (~5 mm dimension) Inexpensive packaging

16. Down-link Up-link Zeroth order Link Budget 75 GHz carrier Collector with 1 meter diameter antenna, 100 mW transmit antenna 100 Kbps using QPSK/BPSK at BER of 10-9 300 m range for semi-passive sensor 100 km range for active sensor with 5 mW transmit power Downlink Uplink (bottleneck)

17. Collector Baseband Processing Correlate signal with location code to estimate delay, accounting for Residual frequency modulation Low rate data Multiple sensors in each scan Demodulation algorithm for asynchronous data modulation Software implementation Limited sample precision

18. : location code : beam pattern Collector Imaging Algorithm

19. Normalized resolution Root Mean Squared Error in X & Y coordinates versus SNR SAR-like processing gives resolution far better than chip duration Multiple collectors can be used to equalize x & y resolution

20. Localization for large sensor density • Single Sensor Algorithm + SIC – works well 25 sensors 100 sensors

21. Brassboard concept validation

22. Collector block diagram • Note: Sensor frequency shifts reflection by 50 MHz

23. Brassboarded collector and sensor Sensor Collector

24. Collector Brassboard RX-Antenna (~2 degree beamwidth) TX Antenna (~20 degree beamwidth) 2-dimensional Antenna scanner Waveguide-based Up-down converter

25. Semi-Passive Sensor Brassboard FRONT (ANT + modulator) 60GHz carrier 50MHz +data Modulated 60 GHz carrier BACK (ANT + modulator) Baseband electronics (1~100Kbps local data plus 50MHz shifting-LO)

26. Indoor Radio Experiment Passive sensor on a cart Collector system • Up to ~10m of range achievable. • 2nd-Gen setup capable up to >100m. • Using active sensors (w/ gain) and higher-gain collector antenna.

27. Preliminary results • Data for 3 ranges – 4 ft.,6 ft. and 8 ft. • Data transmitted – 16-bits 1110101001101100 • Range Resolution (chip length) = 7.5 m. Sub-chip precision achievable using averaging Collector circuit delays are predictable and easy to calibrate out BER ~ 10-2

28. Future Work • Collector Hardware/Processing • Azimuth data collection – from computer by controlling antenna pointing direction • Azimuth processing to improve effective SNR/ Range • Upgrade PA to 200mW (full power) to perform outdoor ranging experiments. (with FCC permission) • Integrate collector components into IC • Sensor ICs • Complete Design of “Passive” Sensor CMOS IC • Work towards the Active ICs

29. Imaging Sensor Nets: Current Status • Lab-scale mm wave experiment to verify link budgets • Basic concept has been verified • Baseband software (version 1) has been developed • Brassboard to-dos • Azimuth data collection • Higher power transmission to increase range (awaiting FCC OK) • IC design for semi-passive sensor • IC design for active sensor • Need creative solution for isolation problems • Many open system level issues • Data representation/compression, redundancy • Exploiting multiple collectors • Sensor-driven imaging sensor nets

30. Distributed Beamforming

31. Receiver SNR feedback f j e 1 f j e 2 Energy Efficiency via Distributed Beamforming • Distributed beamforming can increase range or cut power • Rec’d power = (A + A + …+A)2 = N2 A2 if phases line up • Rec’d power = N A2 if phases don’t line up (+ fading) • Can use low frequencies for better propagation • Large “antenna” using natural spatial distribution of nodes • Diversity • BUT: RF-level sync is hard! Today: Sync using RX feedback (analysis & prototype)

32. Feedback Control Mechanism • Initially the carrier phases are unknown • Each timeslot, the transmitters try a random phase correction • Keep the corrections that increase SNR, discard the others • Carrier phases become more and more aligned • Phase coherence achieved in time linear in number of nodes Typical phase evolution (10 nodes)

33. Transmitters Receiver 1KHz Feedback channel Experimental Prototype

34. sin(2(904e6)t) FPGA A R*cos(2(904e6)t+) 12 Bit DAC 64 point Sine Table 1024 point Random number {±1} Table Phase Counter B 64 point Cosine Table 12 Bit DAC Best Phase Cos(2(904e6)t) 200Hz, 1 bit feedback from Receiver R = (A2+B2),  = arctan(B/A) Transmitter block diagram

35. 904 MHz Signal 20kHz IF to avoid problems with Direct Conversion to DC sin(2(904e6+fIF)t) 1MSPS, 16 Bit ADC 1MSPS, 16 Bit ADC 904 MHz Bandpass Filter, 20MHz BW cos(2(904e6+fIF)t) 200Hz 1 bit feedback to beamformers FPGA Power (i) >? Power(i-1,..,i-M-1) {M=4 for results shown} ( · )2 Oscilloscope 5000 Sample average Power (i) 12 Bit DAC Power ( · )2 time Receiver block diagram

36. Need to get data for this. Received Power Time Received Power

37. Conclusions from prototype • Distributed beamforming works! • Key technical challenges • Phase jitter: Highly sensitive to PLL loop-filter • Power measurement for modulated signals • Limitations • Small-scale experiment (only 3 transmitters) • Static channel conditions

38. y[n+1] x2 α.y[n] x1 Towards an analytical model • Empirical observation: convergence is highly predictable Net effect of phase perturbations What can we say about the distributions of x1 and x2? (without knowing all the individual transmitter phases)

39. Key idea: statistical approach • Received signal proportional to • Infinitely many possible i[n] for any given y[n] • Analogy with statistical physics • Given total energy i.e. temperature • What is the energy of each atom? • More interesting: how many atoms have a energy, E • Concept of Macrostates • Distribution of energy is fixed • Maxwell-Boltzmann distribution • Density ~ exp(-E/kT)

40. The “exp-cosine” distribution • Initially i[0] is uniform in (-π, π] • The phases i[n]get more and more clustered • Given , what is the distribution of i[n]? • “Typical” distribution closest in KL distance to uniform • The Conditional Limit Theorem The “exp-cosine” distribution

41. Exp-cosine matches simulations N = 500 transmitters

42. Implications of analytical framework • Accurate analytical predictions for moderately large N • Evolution of phase distribution • Convergence rate • Optimal choice of distribution of phase perturbations • To maximize convergence rate • Proof of scalability • Convergence time linear in N

43. Accurate prediction of trajectory

44. Effect of optimization • Optimize pdf for δi at each iteration • restrict to uniform pdf: δi~uniform[-δ0,+δ0] • Choose δ0 to maximize E(Δy[n]), given y[n] 200 transmitters Fixed uniform distribution vs. uniform distribution optimized at each slot

45. Scalability and Convergence Phase perturbation not optimized Uniform over (-2o,2o) Phase perturbation optimized Scalable: Convergence is linear in the number of nodes N (provably so for optimized phase perturbations)

46. Tracking time-variations • Must adapt fast enough to track channel • Too fast an adaptation causes loss in phase coherence Should we maximize the mean SNR? Should we minimize fluctuations? How can we trade them off?

47. Analytical framework for time variations Statistical distribution still applies • “exp-cosine” phase distribution for large N • Received SNR is a Markov random process • Analytically derive steady-state distribution Excellent match with simulations!

48. Distributed Beamforming: Current Status • We now know it works • 3 node prototype gives 90% of maximum possible gains • Analytical framework accurately predicts performance • Need simple rules of thumb for time-varying channels • Better justification of exp-cosine derivation • Applications go beyond sensor nets • Wireless link protocols building on distributed beamforming? • Generalization to other distributed control tasks?

49. Tracking with binary proximity sensors

50. Tracking with binary sensors • Minimalistic model appropriate for microsensors • Sensor says target present or absent • Appropriate for large-scale deployments • How well can we track with a network of binary sensors? • Fundamental limits • Minimal path descriptions • Efficient geometric algorithms