Self-Configuring Beacon Systems for Localizing Networked Sensors

1. Self-Configuring Beacon Systems for Localizing Networked Sensors Nirupama Bulusu Laboratory for Embedded Collaborative Systems Department of Computer Science University of California at Los Angeles

2. Sensing Networking Computation Wireless Sensor Networks • New technologies have reduced the cost, size and power of micro-sensors and wireless interfaces Circulatory Net Environmental Monitoring • Systems can • Embedded into environment • Sense phenomena at close range • Systems will revolutionize • Environmental monitoring • Disaster scenarios • Structure Response

3. New Challenges • Energy constraints imposed by unattended systems • Scalingchallenges due to very large numbers of sensors • Level of dynamics: • Environmental – obstacles, weather, terrain • System – large number of nodes, failures • Nodes • Small form factor • Battery operated • System • Large #s • Ad hoc deployment • Unattended Building Blocks to enable efficient coordination among sensor nodes; bridge technology-application gap

4. What is Localization? • A mechanism for discovering spatial relationships between objects

5. Why is Localization Important? • Large scale embedded systems coupledto the physical world • Localization measures that coupling, giving raw sensor readings a physical context • Temperature readings  temperature map • Asset tagging  asset tracking • Smart spaces  context dependent behavior • Sensor time series  coherent beam-forming • Enables data-centric network design Goal: Scalable, ad hoc deployable, energy-efficient localization for small sensor devices

6. Problem Statement • Consider a collection of sensors Si, with coordinate Xi. • Given a subset of Si, are “reference points (beacons)”, with defined values for Xi, • Given a set of measurements that relate the positions of Si, • Estimate Xi. • Design of position estimation algorithm depends on nature of constraints; Nature of constraints depends on types of ranging. Ranging sensitive to environment.

7. Goal: Robust, Unattended operation Approach: Self-configuration • Thesis • Many aspects of localization are highly environment dependent and may require configuration. • In order to be ad hoc deployed and operate unattended in any environment, the localization system must self-configure. • Many dimensions to Self-configuration • System – Adapting to node density, failures etc. • Multiple sensor modalities for robust measurements • Environment - Adapting to fixed characteristics • Dynamically deriving wireless channel parameters

8. Implement best solutions on real networks Evaluate performance Collect data with real networks Identify and analyze problems Simulation and Analysis Design solutions Evaluate solutions in simulation Methodology

9. Talk Structure • Motivation • Localization Background • Networked Sensors: Localization Challenges • Self-Configuring Beacon Systems • Conclusions

10. Variety of Applications • Two applications: Passive habitat monitoring: Where is the bird? What kind of bird is it? Asset tracking: Where is the projector? Why is it leaving the room?

11. Outdoor operation Weather problems Bird is not tagged Birdcall is characteristic but not exactly known Accurate enough to photograph bird Infrastructure: Several acoustic sensors, with known relative locations; coordination with imaging systems Indoor operation Multipath problems Projector is tagged Signals from projector tag can be engineered Accurate enough to track through building Infrastructure: Room-granularity tag identification and localization; coordination with security infrastructure Variety of Application Requirements • Very different requirements!

12. Axes of Application Requirements Wireless Sensor Networks • Cost, Power, Form factor • Scaling (Number of devices) • Communications Requirements • Environmental conditions • Is the target known? Is it cooperating? • Distance scales • Accuracy scales • Relation to established coordinate system

13. Target Synchronization channel Ranging channel Variety of Mechanisms ? Definitely no “one size fits all” solution

14. What’s Wrong with What’s There? • Approaches that scale (e.g. GPS) cannot always accommodate device constraints, be ubiquitously available, responsive or accurate enough. • Approaches that accommodate device constraints (eg. Microsoft RADAR) require extensive pre-configuration and may not be suitable for ad hoc, unattended deployment. No existing localization system can self-configure to its environmental conditions.

15. Talk Structure • Motivation • Localization Background • Networked Sensors: Localization Challenges • Self-Configuring Beacon Systems • Conclusions

16. Networked Sensors: Localization Challenges • #1: Scale • #2: Device Constraints • #3: Deployment and Dynamics

17. #1: Scale • Problem • Need to localize large numbers of devices • Communications and computation cost of centralized localization approach based on global system state prohibitively expensive • Our Solution • Localized location computation

18. #2: Device Constraints • Problem • Small devices have limited hardware and energy • Low-energy localization approaches leverage inherent communications capabilities (eg. RF amplitude) • But RF amplitude not fine-grained enough to converge to consistent global coordinate system….. • Our Solution • Tiered architectures • Exploit heterogeneity; use beacons

19. Lower Tier What are Beacons? Sensor Mote UCB, 2000 RFM radio, PIC • Reference Nodes that know their position • How? Less-constrained devices based on the principle of tiered architectures; can form accurate coordinate system independently • GPS-enabled (outdoors) • Special ranging hardware; multiple sensor modalities etc. (recent work at UCLA) • More memory to run sophisticated position estimation algorithms WINS NG 2.0 Sensoria, 2001 Node development platform; multi- sensor, dual radio, Linux on SH4, Preprocessor, GPS Upper Tier Tiered Architecture (trade form factor vs. functionality)

20. Example: An RF-based Localization System • Si - Set of Beacons • Beacons broadcast advertisements • Randomly with periodic offsets with • (X, Y, Z) coordinates • Beacon Identifier • Sequence number of the advertisement • Each client node computes its position based on the beacons it is connected to.

21. Single Beacon • Idealized RF-propagation model • Connectivity implies client within some maximum communication radius R R Beacon Client Node Possible position for client node

22. Multiple Beacons • More connections result in smaller regions of overlap • Smaller area  feasible position is close to real position

23. n n å å = = w Wi Xi i 1 1 W Xe W Position Estimation • Weighted centroid approach • Reference: The centroid of points with approximate weights [ M. Bern, D. Eppstein, L. Guibas, J. Hershberger, S. Suri, J. Wolter et.al.] • Set of i beacons, position Xi , Range Ri • Xe – estimated position • Wi= 1/(Ri )2

24. Inferring RF Connectivity and Range • Nrecv(i, t1, t2)- # Packets received from Bi in time [t1, t2] • Nsent(i, t1, t2)– # Packets sent by Biin time [t1, t2] • Connectivity Metric for Beacon Bi • Range Ri of Beacon Bi • median range over all gradients for which CM >CMthresh Nrecv(i, t1, t2) CM(i,t1,t2) = Nsent(i, t1,t2) Connectivity if CM > CMthresh

25. Characterizing Localization Quality • X - real position • Xe - estimated position • Localization Error Metric • LE(X) = ||X – Xe || • Localization Quality • Cumulative Error Distribution Function

26. Sources of Localization Error • Beacon Placement • Environment • Signal Propagation vagaries • Miscalibration

27. Impact of Beacon Placement Beacons randomly placed: LARGER mean granularity Beacons uniformly placed: SMALLER mean granularity

28. Received Signal Strength (RSSI) Radio Propagation Basics • Why do RF propagation vagaries occur? • Path loss characteristics depend on environment (1/rn) • Shadowing depends on environment • Short-scale fading due to multipath adds random high frequency component with huge amplitude (30-60dB) – very bad indoors • Mobile nodes might average out fading.. But static nodes can be stuck in a deep fade forever Path loss Shadowing Fading Distance Ref. Rappaport, T, Wireless Communications Principle and Practice, Prentice Hall, 1996.

29. Impact of Propagation Vagaries Gap in Beacon Coverage Proximity inferred to Distant Beacon

30. Summary: RF-based Localization • Problem • Localization of many small devices • Solution • Self-Localization from RF-proximity beacons • General Lessons • Localized algorithms • Tiered architectures that leverage heterogeneity • Status • Implementations: Radiometrix RPC radios, UCB motes • Experiments both indoors and outdoors • Used for proximity-based tracking, geo-routing, localization for energy harvesting etc. • Papers: IEEE Personal Communications

31. #3: Deployment and Dynamics • Problem • Localization quality governed by beacon placement and environmental conditions….. • …..But • careful manual pre-configuration of beacon systems impedes ad hoc deployment • manual re-configuration to dynamics impedes unattended operation • Our solution • Self-configuring beacon systems

32. Talk Structure • Motivation • Localization Background • Networked Sensors: Localization Challenges • Self-Configuring Beacon Systems • Conclusions

33. Self-configuring Beacon Systems • Idea: • Measure and adapt to unpredictable environment • Exploit spatial diversity and density of sensor/actuator nodes • Assuming large solution space, not seeking global optimal • Questions: • What to measure? • How to adapt?

34. R Characterizing Beacon Density • N - Number of Beacons • A - Area • R - Transmission Range of each beacon • Beacon Deployment Density, r = N/A • Beacons per nominal radio coverage area, m • m = r . p .R2

35. Impact of Beacon Density saturation density ~6 bpnrca Mean Localization Error(fraction of R) Beacons per nominal radio coverage area Density should influence approach to self-configuration

36. 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 5 10 15 20 Impact of Beacon Density saturation density ~6 bpnrca Mean Localization Error(fraction of R) High Density: STROBE Low Density: HEAP Beacons per nominal radio coverage area

37. Talk Structure • Motivation • Localization Background • Networked Sensors: Localization Challenges • Self-Configuring Beacon Systems • Beacon Density • Low densities: HEAP • High densities: STROBE • Conclusions

38. HEAP Introduction • Problem • Beacons deployed may not ensure localization quality due to environment vagaries • Traditional Approaches • Eg. Facility location, gap-finding • Offline, centralized optimization based on beacon positions only; ignore environmental effects • Our Solution: HEAP • Adaptive, incremental beacon placement

39. HEAP - Incremental Beacon Placement • Goal • Add new beacons to an already deployed beacon field where most needed • Design Goals • Measurement-based adaptation to environmental conditions • Localized algorithms to minimize communications • Caveats • Completely self-configuring only if new beacons can be added without manual intervention

40. HEAP Illustration Placer Beacon Node candidate point, utility

41. Local Candidate Point Selection • Given • S – set of all beacons reachable in grid • E - An error estimation model • Determine C - (x , y) • Such that cumulative localization error in the grid is minimized by adding beacon at C • Analytically intractable • Estimate by sampling the grid. 2R candidate point

42. HEAP Evaluations • Goals • Impact of density • How does it compare to a centralized scheme, or a purely random one? • Metrics • Improvement in mean localization error • Methodology • Simulations for repeatable experiments • Experiments to validate with real data

43. Performance: Mean Error Improvement Mean Error Improvement (fraction of R ) Beacons Per Nominal Radio Coverage Area Localized algorithms gains comparable to centralized algorithms

44. Experimental Validation • Limited Computation • 4 MHz, 8-bit CPU • Limited memory • 512 bytes • Limited code size • 8 KB • 3.5 K Base code (TinyOS + radio encoder) • Only 4.5K for apps • Limited communication • 30 byte packets Platform: Berkeley RENE Motes

45. 35 ft 24 ft 42 ft Indoor Beacon Deployment

46. Software Infrastructure Operational Testbed • Beacon • Client • Placer • Transceiver • Send control packets to beacons • Receive reports from client • BeaconRemoteController • User control of beacons • BeaconInterpretor • User input of actual client coordinates • Localization error report • Visualization – AirPacketAnalyzer • Displays all transmitting devices in the lab • Useful for checking RF interference • Uses lab snoopers Experimental Testbed

47. Control Message - Activate/Stop - Transmit Power Setting - Beaconing Interval

48. Beacon Connectivity Failed beacon

49. Beacon Connectivity Missing Link Long Asymmetric links Failed beacon

50. Candidate Point Selected by HEAP Candidate point Missing Link Long Asymmetric links Failed beacon