© 2004 Andreas Haeberlen, Rice University 1 Practical Robust Localization over Large-Scale Wireless Ethernet Networks Andreas Haeberlen Eliot Flannery Andrew Ladd Algis Rudys Dan Wallach Lydia Kavraki Rice University Houston, TX 10th Annual International Conference on Mobile Computing and Networking (MOBICOM) September 28, 2004 Philadelphia, PA

2 © 2004 Andreas Haeberlen, Rice University Motivation Location-aware computing has many interesting applications:  Navigation  Asset tracking  Tourist/visitor guides  Advertising  Finding resources  Visitor tracking  Content redirection  Robot navigation  Sensor networks  Intruder detection Goal: Locate a device in a building The ideal localization system : Cheap Easy to deploy Accurate Robust

3 © 2004 Andreas Haeberlen, Rice University Related Work Solutions with special hardware Good accuracy Expensive Hard to deploy Example: Cricket [Priyantha 2000] Ultrasound beacons

4 © 2004 Andreas Haeberlen, Rice University Related Work Bayesian localization [Ladd 2002] Good accuracy Inexpensive hardware But: Not practical! Needs many days of training Does not work with different hardware Accuracy varies during the day

5 © 2004 Andreas Haeberlen, Rice University Overview Improvements over [Ladd 2002]: Drastic reduction in training time Adapts to different hardware Robust against untrained variations Techniques used: Topological localization Simplified signal model Calibration

6 © 2004 Andreas Haeberlen, Rice University Training wireless signal propagation is complex  Need training Operator visits every location, measures signal strength Result: A signal map of the entire building Observed signal strength Occurrences

7 © 2004 Andreas Haeberlen, Rice University Markov Localization To localize 1. Initialize vector of location estimates 2. Perform a base station scan 3. Update estimate using Bayes' formula 4. Repeat steps 2-3 until estimates converge Signal map Location estimate Observed RSSI Bayes' formula New location estimate

8 © 2004 Andreas Haeberlen, Rice University Topological regions Many applications do not need 1-2 meter precision Can trade metric resolution for lower training time Localize to regions Offices Hallway segments Parts of larger rooms Reduces training effort by an order of magnitude Occupancy grid Regions

9 © 2004 Andreas Haeberlen, Rice University Gaussian signal model Previous methods keep a histogram of signal strengths Problems Overtraining Undertraining Use Gaussian as an approximation! More robust Saves memory Needs less training Observed signal strength Occurrences Observed signal strength Occurrences Minor mode Gap  

10 © 2004 Andreas Haeberlen, Rice University Experiment: Duncan Hall Duncan Hall: >200 offices, classrooms, seminar rooms Total area: 158 x 75 meters

11 © 2004 Andreas Haeberlen, Rice University Duncan Hall Architecture Large open spaces (low signal variation) Clerestory ceiling (reflections) Metal air ducts (distortions)

12 © 2004 Andreas Haeberlen, Rice University Experiment: Duncan Hall Manually created 510 cells, ~3x5m each Collected  100 BS scans/cell (51249 total) 28 man-hours were sufficient! Data collection: Experiments: Partition data set Training data Testing data scans

13 © 2004 Andreas Haeberlen, Rice University Results: Static localization Result: Excellent accuracy over the entire building Accuracy for cell: 70-80% 80-90% 90-95% >95% Base stations worst case (localizes to adjacent cells)

14 © 2004 Andreas Haeberlen, Rice University Results: Static localization II Experiment: Use only N scans/cell for training Result: Gaussian needs a lot less training data This is in addition to gains from topology model For 95% accuracy: Histogram: 84 scans Gaussian: 30 scans

15 © 2004 Andreas Haeberlen, Rice University Problem: Untrained variations 1. Differences in hardware, software, or antenna 2. Observed signal strength changes over time Signal Strength 3am 9am 3pm 9pm Source: [Tao 2003] Probability of registering signal strength

16 © 2004 Andreas Haeberlen, Rice University Calibration: New Hardware Approximate relationship between 'old' and 'new' values by a linear function Invert function, apply it to each observation Signal strength (reference card) Signal strength (new card) i 2 =m·i 1 +c

17 © 2004 Andreas Haeberlen, Rice University Calibration: Time-of-day Linear approximation works for time-of-day variations, too! Learn parameters using calibration Signal strength (nighttime) Signal strength (11am) i 2 =m·i 1 +c Parameters

18 © 2004 Andreas Haeberlen, Rice University Mobility: Markov chains Goal: Track location while user is moving Problem: Markov localization tends to 'lag' for mobile agent Need a motion model for the user Use markov chain to model possible cell- to-cell transitions 90% 5% 60%

19 © 2004 Andreas Haeberlen, Rice University "It doesn't work any more!" Base stations were upgraded to a/b/g New IOS software New radio module What we did: Configured new BSSIDs Ran calibration once System works, delivers good accuracy! 2.4 GHz radio module

20 © 2004 Andreas Haeberlen, Rice University Results: Mobility - Movie - Experiment on 09/23/04 (after a/b/g upgrade)

21 © 2004 Andreas Haeberlen, Rice University Conclusions Topological localization delivers good accuracy with a reasonable training effort Gaussian sensor model is more robust and requires less training time than histogram- based model Training data can be adapted for use with different hardware and under different conditions System is deployed in a large office building and in practical use