Localization in Sensor Networks

1. Localization in Sensor Networks

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

3. Why is Localization Important? • Large scale embedded systems introduce many fascinating and difficult problems… • coupling them to 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 beamforming

4. 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?

5. 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!

6. Granularity & Scale Accuracy & Precision Relative vs. Absolute Positioning Dynamic vs. Static (Mobile vs. Fixed) Cost & Form Factor Infrastructure & Installation Cost Communications Requirements Environmental Sensitivity Cooperative or Passive Target Multidimensional Requirement Space

7. Axes of Application Requirements • Granularity and scale of measurements: • What is the smallest and largest measurable distance? • e.g. cm/50m (acoustics) vs. m/25000km (GPS) • Accuracy and precision: • How close is the answer to “ground truth” (accuracy)? • How consistent are the answers (precision)? • Relation to established coordinate system: • GPS? Campus map? Building map? • Dynamics: • Refresh rate? Motion estimation?

8. Axes of Application Requirements • Cost: • Node cost: Power? $? Time? • Infrastructure cost? Installation cost? • Form factor: • Baseline of sensor array • Communications Requirements: • Network topology: cluster head vs. local determination • What kind of coordination among nodes? • Environment: • Indoor? Outdoor? On Mars? • Is the target known? Is it cooperating?

9. Variety of Mechanisms • We’ve seen a broad spectrum of application requirements • There are also a broad spectrum of localization mechanisms appropriate for different applications

10. Returning to our two Applications… • Choice of mechanisms differs: Passive habitat monitoring: Minimize environ. interference No two birds are alike Asset tracking: Controlled environment We know exactly what tag is like

11. Bird is not tagged Passive detection of bird presence Birdcall is characteristic but not exactly known Bird does not have radio; TDOA measurement Passive target localization Requires Sophisticated detection Coherent beamforming Large data transfers Projector is tagged Projector might know it had moved Signals from projector tag can be engineered Tag can use radio signal to enable TOF measurement Cooperative Localization Requires Basic correlator Simple triangulation Minimal data transfers Variety of Localization Mechanisms • Very different mechanisms indicated!

12. Taxonomy of Localization Mechanisms • Active Localization • System sends signals to localize target • Cooperative Localization • The target cooperates with the system • Passive Localization • System deduces location from observation of signals that are “already present” • Blind Localization • System deduces location of target without a priori knowledge of its characteristics

13. Target Synchronization channel Ranging channel Active Mechanisms • Non-cooperative • System emits signal, deduces target location from distortions in signal returns • e.g. radar and reflective sonar systems • Cooperative Target • Target emits a signal with known characteristics; system deduces location by detecting signal • e.g. ORL Active Bat, GALORE Panel, AHLoS • Cooperative Infrastructure • Elements of infrastructure emit signals; target deduces location from detection of signals • e.g. GPS, MIT Cricket

14. Target Synchronization channel Ranging channel ? Passive Mechanisms • Passive Target Localization • Signals normally emitted by the target are detected (e.g. birdcall) • Several nodes detect candidate events and cooperate to localize it by cross-correlation • Passive Self-Localization • A single node estimates distance to a set of beacons (e.g. 802.11 bases in RADAR [Bahl et al.], Ricochet in Bulusu et al.) • Blind Localization • Passive localization without a priori knowledge of target characteristics • Acoustic “blind beamforming” (Yao et al.)

15. Active vs. Passive • Active techniques tend to work best • Signal is well characterized, can be engineered for noise and interference rejection • Cooperative systems can synchronize with the target to enable accurate time-of-flight estimation • Passive techniques • Detection quality depends on characterization of signal • Time difference of arrivals only; must surround target with sensors or sensor clusters • TDOA requires precise knowledge of sensor positions • Blind techniques • Cross-correlation only; may increase communication cost • Tends to detect “loudest” event.. May not be noise immune

16. Building Localization Systems • Given a set of application requirements, how do we build a system that meets them? • Outline: • Overview of a typical system design • A quick example • Ranging technologies • Coordinate system synthesis techniques • Spatial scalability • Recent results: the GALORE panel

17. Filtering Filtering Filtering Filtering Filtering Filtering Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Localization System Components • Generally speaking, what is involved with a “localization system”? Stitching and Refinement This step applies to distributed construction of large-scale coordinate systems This step estimates target coordinates (and often other parameters simultaneously) Coordinate System Synthesis Coordinate System Synthesis • Parameters might include: • Range between nodes • Angle between nodes • Psuedorange to target (TDOA) • Bearing to target (TDOA) • Absolute orientation of node • Absolute location of node (GPS)

18. Example of a Localization System • SHM system, developed at Sensoria Corp. Each node has 4 speaker/ microphone pairs, arranged along the circumference of the enclosure. The node also has a radio system and an absolute orientation sensor that senses magnetic north. Microphone Speaker 12 cm

19. System Architecture • Ranging between nodes based on detection of coded acoustic signals, with radio synchronization to measure time of flight • Angle of arrival is determined through TDOA and is used to estimate bearing, referenced from the absolute orientation sensor • An onboard temperature sensor is used to compensate for the effect of environmental conditions on the speed of sound

20. System Architecture • Nodes periodically emit acoustic pulses. Other nodes detect these pulses and compute a range and angle of arrival. • Range data, angle data, and absolute orientation are broadcast N hops away. • Based on this table of ranges, angles, and orientations, each node applies a multilateration algorithm with iterative outlier rejection to compute a consistent coordinate system. Range, Angular Data Multilat Engine Range, Angular Data Multilat Engine Range, Angular Data Multilat Engine

21. Parameters might include: • Range between nodes • Angle between nodes • Psuedorange to target (TDOA) • Bearing to target (TDOA) • Absolute orientation of node • Absolute location of node (GPS) Filtering Filtering Filtering Filtering Filtering Filtering Filtering Filtering Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Localization System Components • Sensing layer: Ranging, AOA, etc. Stitching and Refinement This step applies to distributed construction of large-scale coordinate systems This step estimates target coordinates (and often other parameters simultaneously) Coordinate System Synthesis Coordinate System Synthesis • Parameters might include: • Range between nodes • Angle between nodes • Psuedorange to target (TDOA) • Bearing to target (TDOA) • Absolute orientation of node • Absolute location of node (GPS)

22. Active and Cooperative Ranging • Measurement of distance between two points • Acoustic • Point-to-point time-of-flight, using RF synchronization • Narrowband (typ. ultrasound) vs. Wideband (typ. audible) • RF • RSSI from multiple beacons • Transponder tags (rebroadcast on second frequency), measure round-trip time-of-flight. • UWB ranging (averages many round trips) • Psuedoranges from phase offsets (GPS) • TDOA to find bearing, triangulation from multiple stations • Visible light • Stereo vision algorithms • Need not be cooperative, but cooperation simplifies the problem

23. Passive and Non-cooperative Ranging • Generally less accurate than active/cooperative • Acoustic • Reflective time-of-flight (SONAR) • Coherent beamforming (Yao et al.) • RF • Reflective time-of-flight (RADAR systems) • “Database” techniques • RADAR (Bahl et al.) looks up RSSI values in database • “RadioCamera” is a technique used in cellular infrastructure; measures multipath signature observed at a base station • Visible light • Laser ranging systems • Commonly used in robotics; very accurate • Disadvantages: directionality, expense, no positive ID of target

24. Using RF for Ranging • RF TOF techniques • Accurate, deterministic transponders hard to build • Temperature-dependence problems in timing of path from receiver to transmitter • But, you can use “RBS” techniques… (compare receptions) • Measuring TOF requires fast, synchronized clocks to achieve high precision (c 1 ft/ns) • Fast synchronized clocks generally at odds with low power • Trade-off: synchronized infrastructure vs. nodes (e.g. GPS) • Ultra wide-band ranging for sensor nets? • Current research focus in RF community • Based on very short wideband pulses, measure RTT to fixed, surveyed base stations • FCC licensing?

25. RSSI Practical Difficulties with RSSI • RSSI is extremely problematic for fine-grained, ad-hoc applications • 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 • Potential applications • Approximate localization of mobile nodes, proximity determination • “Database” techniques (RADAR) Path loss Shadowing Fading Distance Ref. Rappaport, T, Wireless Communications Principle and Practice, Prentice Hall, 1996.

26. Using Acoustics for Ranging • Key observation: Sound travels slowly! • Tight synchronization easily achieved using RF signaling • Slow clocks are sufficient (v = 1 ft/ms) • With LOS, high accuracy can be achieved cheaply • Coherent beamforming can be achieved with low sample rates • Disadvantages • Acoustic emitters are power-hungry (must move air) • Solid obstructions block sound completely  detector picks up reflections • Audible sound has good channel properties but isn’t always appropriate

27. Radio Radio CPU CPU Speaker Microphone Typical Time-of-Flight AR System • Radio channel is used to synchronize the sender and receiver (or use a service like RBS!) • Coded acoustic signal is emitted at the sender and detected at the emitter. TOF determined by comparing arrival of RF and acoustic signals

28. Narrowband vs. Wideband • Narrowband technique: pulse train at f0 • Works with tuned resonant ultrasound transducers • COTS parts implement detection (SONAR modules) • Crosstalk between nodes is a problem, introduces significant coordination overhead to system design • Used in ORL Active Bat, MIT Cricket, UCLA AHLoS • Wideband technique: pseudonoise burst • Detection requires ~100M FLOPs, ~128K RAM • High accuracy, excellent interference rejection • 100m range achieved over grass in outdoor environment • Excellent crosstalk rejection; each xmitter uses diff. code • Used in GALORE Panel, IPAQs, SHM

29. Wideband Acoustic Detection Autocorrelation Value of Correlation Function First arrivalandechoes Offset in samples (20.8s, or 0.71 cm)

30. Ringing Ringing Introduced by Speaker Probable Echo Can be corrected at the source (deconvolve source waveform) or at the receiver (correlation)

31. An Acoustic Ranging Error Model • A useful model for error in acoustic ranges is Rij = ||Xi – Xj||2 + nij + Nij, where • nij is a gaussian error term (=0,=1.3) • Nij is a fixed bias present only when LOS blocked • Error reduction: • nij can be reduced by repeated observations • Nij cannot because it is caused by persistent features of the environment, such as detection of a reflection. • The Nij errors must be filtered at higher layers • Cross-validation of multiple sensor modalities • Geometric consistency, error terms during multilateration

32. Microphone Microphone Microphone Microphone Array Typical Angle-of-Arrival AR System • TOF AR system with multiple receiver channels • Time difference of arrivals at receiver used to estimate angle of arrival Radio Radio CPU CPU Speaker

33. Sources of error in bearing & position est. • Quantization error in detection • Sample rate of detector lower bounds phase accuracy • Synchronization error • Detection error • Noise, interference, ringing, blurring in channel • Excess path length due to clutter • Non-LOS detection of reflected paths • Usually easy to filter if sensor positions are known • Angular dependence in sensor • Error in measurement or estimation of baseline • “Other”... various relatively unlikely occurrences • Collisions between codes, system failures, etc • It’s hard (impossible?) to root out the last 0.1% of the problems!

34. Parameters might include: • Range between nodes • Angle between nodes • Psuedorange to target (TDOA) • Bearing to target (TDOA) • Absolute orientation of node • Absolute location of node (GPS) Filtering Filtering Filtering Filtering Filtering Filtering Filtering Filtering Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Parameter Estimation Localization System Components • Coordinate system synthesis & refinement layer Stitching and Refinement Stitching and Refinement This step applies to distributed construction of large-scale coordinate systems This step estimates target coordinates (and often other parameters simultaneously) This step estimates target coordinates (and often other parameters simultaneously) Coordinate System Synthesis Coordinate System Synthesis Coordinate System Synthesis Coordinate System Synthesis • Parameters might include: • Range between nodes • Angle between nodes • Psuedorange to target (TDOA) • Bearing to target (TDOA) • Absolute orientation of node • Absolute location of node (GPS)

35. Position Est. and Coord. Systems • Position Estimation, Triangulation • Some of the nodes have known positions • Target’s position inferred relative to known nodes • e.g. Active Bat, single GALORE Panel • Forming a coordinate system, Multilateration • Most nodes have unknown positions • Consistent coordinate system constructed based on measured relationships between nodes • “Multilateration” is a commonly used term • e.g. N-hop ML, SHM, GALORE IPAQ system

36. Optimization Problems • Often implemented as an overconstrained optimization problem: • Input is set of measurements • Ranges, angles, other relationships • Output is estimated node position map • Environmental parameters often estimated concurrently • Gaussian error  least-squares minimization • Careful filtering required to ensure this property • Centralized vs. distributed

37. Simple Example: GALORE Panel • Pythagorean Theorem yields constraints: • Object is to find position estimate that minimizes squared sum of error terms GALORE panel system: 4 mics at measured locations Mote emitter at unknown location Where is measurement error in the range measurement

38. GALORE Panel Position Estimator • Rewrite to get error as function of position and meas. range: • Problem: error function is not linear: must use NLLS • Approximate the constraint function by a Taylor series: • Neglecting higher-order terms, and choosing an initial “guess” X0, • Linear approximation of the error function in that neighborhood • Solve for “X” that minimizes error • Iteratively improve X0 until it converges • Good initial guess  likely convergence if well constrained Ref. Strang, and G, Borre, K, Linear Algebra, Geodesy, and GPS, Wellesley-Cambridge Press, 1997

39. Importance of Outlier Rejection • Outliers in source data cause problems • Acoustic localization data • 480 ranges, 30 nodes • Outliers caused by code collisions and multipath • 4 outliers rejected

40. Even if most of the data is good, a few outliers can cause significant position error for this algorithm • Finding the outliers is easier with more data • “Studentized” residual analysis weights error terms by their impact on solution

41. Scaling NLLS Position Estimation • NLLS algorithm with outlier rejection: scaling issues • Centralized: all ranges are brought together • ~O(N4), thus does not scale well for large nets • Solution: “partial” distribution • Collect ranges from a few hops away • Accrete small set of well-constrained nodes, and process them into coordinate system • Pass along that system for neighbors to extend • Current data shows 1% degradation using set size of 14 nodes in a 30 node network with high connectivity

42. N-Hop Multilateration1 • Fully distributed algorithm • Very low overhead; avoids mutliplies • Designed to be robust to “long” ranges • Initial implementation • Running on “MK-2” platform (ARM-thumb + Atmel 103 radio controller, RFM transceiver) • Initial application: “Smart Kindergarten” project • Measured and deployed beacons on ceiling • Small receiver tags on toys and children 1A. Savvides, H. Park, M. Srivastava, The bits and flops of the n-hop multilateration primitive for node localization problems, First ACM Workshop on Wireless Sensor Networks and Application (WSNA), pp112-121, Atlanta GA, September 2002.

43. N-hop System Layers • Ultrasound ranging • Uses COTS ultrasound detection chipset, RF sync • N-hop lateration: • Nodes compute range to beacons • Pass their range to beacons to RF neighbors • If you have no direct range to a beacon: • Compute your range to neighbors who see that beacon • Add your range to that neighbor to their range to beacon • Keep minimum computed range to each beacon

44. Key Points •  Although the summed ranges will be larger than the true range, with sufficient and uniform node density the excess path range is bounded and fairly small •  And, long ranges will automatically be dropped! •  Caveat: Important to filter out short ranges…

45. Low-cost Multilateration Step • Once each node has ranges to N beacons: • “Min-Max” algorithm • Approximates beacon ranges to squares • Computes centroid of bounding box •  Approximation is often quite close (blue vs red) •  Redundant “long” estimates are implicitly dropped •  Caveat: Approximation as well as error tolerance is best if the node is inside a convex hull of beacons

46. N-hop multilateration refinement • Iterative refinement step can improve upon the min-max algorithm • Locally collects inter-node ranges and iteratively improves the estimates • Must include scheme to reject outlier data • Overall assessment of N-Hop Multilateration: • Simple and fully distributed • Implicitly eliminates many forms of outlier data • Position accuracy at least order of node density

47. Scaling Positioning Across the Network • Idea from RBS: transform to local time at every hop • Improves scalability by avoiding need for global time • Similar technique may be useful for localization • Transform to local coordinate system at each hop • However, • Error propagation characteristics are more difficult to estimate and model than timing errors: highly dependent on geometry and environment. • Likely not zero-mean Gaussian… but drift can be mitigated by including beacons or survey points with absolute positions.

48. System Design Case Study: IPAQ-based Localization • Followon to GALORE Panel System • Intended to be an ad-hoc deployable system of small units (IPAQs) that can self-organize and then localize motes. • Computational cost of sender is low; IPAQs do detection IPAQs are unmodified, running “Familiar” Linux; uses internal speaker and microphones. “Acoustic” Mote adds spkr, amp on daughterboard (N. Busek)

49. “Acoustic” Mote

50. Experimental Setup