On Minimalism and Scale in Sensor Networks

1. On Minimalism and Scale in Sensor Networks Upamanyu Madhow ECE Department University of California, Santa Barbara Funding Sources

2. Why sensor networks? • Sensors have been around for a long time • Why are we so interested in sensor networks? • Role of networking • Can we usefully exploit data from lots of sensors? • A whole greater than the sum of its parts? • Why lots of sensors? • Large areas to be covered, small sensing range • Many big picture issues for theorists to ponder • How does scale improve information? • How do we communicate with a million sensors? • How can inter-sensor collaboration help? • How can we engineer trust in wireless sensor networks?

3. Big picture questions lead to new ideas • How to gather information from 100,000 sensors? • Imaging sensor nets • How do we scale surveillance and tracking? • Tracking with binary proximity sensors • Large-scale camera networks • Can sensors act like a distributed antenna array? • Practical algorithms for distributed transmit beamforming

4. Today’s focus: minimalism and scale • Scaling to a very large number of sensors • Minimalism in sensor capability and deployment requirements • Imaging sensor nets • Scaling in energy • Collaboration with minimal inter-sensor communication • Distributed transmit beamforming with feedback control • Scaling in sensor cost/functionality • Minimalism in sensor output: yes or no • Tracking with binary proximity sensors Bharath Ananthasubramaniam, Munkyo Seo, Prof. Mark Rodwell Raghu Mudumbai, Ben Wild, Prof. Joao Hespanha, Prof. Kannan Ramchandran Nisheeth Srivastava, Raghu Mudumbai, Jaspreet Singh, Rajesh Kumar, Prof. Subhash Suri

5. Imaging Sensor Nets 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”) Collector-driven data collection:Sensors electronically reflect, with data modulation, beacon from collector (“virtual radar”) Sensor-driven data collection: Sensors transmit at will, sensor location and data estimated by collaboration among a network of collectors Minimal functionality: no GPS, no inter-sensor networking Lifetime of year on watch cell battery Sophisticated collectors 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

6. 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

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

8. 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

9. 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)

10. : location code : beam pattern Collector Imaging Algorithm

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

12. Brassboard concept validation

13. Ongoing research and open issues • Hardware • Sensor IC design • Collector system integration • Algorithms • Alleviating inter-sensor interference • Collaboration among collectors • Data compression and representation • Collector-driven data collection • Sensor-driven data collection • Integration of transceiver with sensing • Well-defined interfaces for mm wave motes

14. Receiver SNR feedback f j e 1 f j e 2 Distributed Transmit 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! Sync can be achieved using RX feedback!

15. 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) Concept experimentally verified by Ben Wild (UC Berkeley)

16. 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)

17. 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)

18. 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

19. Exp-cosine matches simulations N = 500 transmitters Can now predict trajectory, optimize convergence rate, and prove scalability

20. Accurate prediction of trajectory

21. Optimize phase perturbation • 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

22. 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)

23. Ongoing research and open issues • Detailed understanding of time variations • Trade off tracking versus misadjustment • Protocols leveraging distributed beamforming • Generalization to other distributed control tasks?

24. 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

25. The Geometry of Binary Sensing Target Path Localization patches Sensor Outputs Localization arcs

26. Results • Attainable resolution ~ 1/(sensor density*sensing range) • Spatial low pass filtering • Can track only “lowpass” version of the path • OccamTrack algorithm • Minimal piecewise linear representations • Velocity estimates for “lowpass” version • Robustness to sensing range variation • Particle filter + Geometric clean-up • Multiple targets • Many possible explanations for snapshot of sensor readings • Temporal evolution can be exploited by particle filter

27. How a trajectory is localized Max patch size at least 1/(density*range) for any deployment Patch size of the order of 1/(density*range) for Poisson deployment Resolution theorems

28. Minimal representation: OccamTrack Greedy algorithm for piecewise linear representation: Draw lines stabbing localization arcs that are as long as possible

29. Spatial Lowpass Filtering Cannot capture rapid variations Can only reconstruct “lowpass” version of path Justifies simple piecewise linear representation

30. Velocity estimation and minimal representation Which path should we use to estimate (lowpass version of) velocity? We can be off by a factor of two! A simple formula: dv/v = dL/L Proposition: If piecewise linear approx works well, then velocity estimate is accurate.

31. Simulation Results Weighted Centroid Output (Kim et al, IPSN 2005) OccamTrack Output OccamTrack Velocity Estimate

32. Fundamental Resolution Limits Theoretical resolution attained by both regular and random deployment

33. Handling non-ideal sensing Experiment with acoustic sensor Not detected ? Detected Low pass filter observations A simple model Acoustic sensors are unreliable & unpredictable Localization patch = intersection of outer circles with complements of inner circles Particle filtering algorithm provides robust performance Geometric clean-up provides minimal description

34. Experiments with acoustic sensors Non-ideal sensor response Particle Filter OccamTrack OccamTrack with ideal sensing Particle Filter + Geometric

35. Multiple Targets: what does a snapshot tell us? Significant ambiguity regarding how many targets and where they are Can get a minimal explanation for sensor readings: greedy algorithm But how do we piece together snapshots?

36. Particle filters work!

37. Simulation results with non-ideal sensing

38. Experiments with passive IR sensors

39. Ongoing research and open issues • Signal processing framework • Spatial lowpass filtering • “Multiuser interference” due to multiple targets • Multimodal sensing • Enhancement of binary model • Bayesian frameworks for combining sensor observations • Distributed realizations

40. A Big Picture • What is needed for sensor networks to be a success? • Two or three successful big applications • What can theorists do? • Thought experiments based on application scenarios • Ask the big picture questions • Follow up with logical answers • Collaborate with builders and experimentalists • Example big picture questions • How best to use sensor correlations? • How to provide wire-like guarantees? • What are good architectures for nextgen surveillance and tracking? • What are good models for distributed sensing & actuation?