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Review: Accuracy Characterization for Metropolitan-scale Wi-Fi Localization

Review: Accuracy Characterization for Metropolitan-scale Wi-Fi Localization . Authors: Cheng, Chawathe , LaMacra , Krumm 2005 Slides Adapted from Cheng, MobiSys 2005 Review by: Jonathan Odom. Location, Location, Location.

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Review: Accuracy Characterization for Metropolitan-scale Wi-Fi Localization

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  1. Review: Accuracy Characterization for Metropolitan-scale Wi-Fi Localization Authors: Cheng, Chawathe, LaMacra, Krumm 2005 Slides Adapted from Cheng, MobiSys 2005 Review by: Jonathan Odom

  2. Location, Location, Location • Require accurate location for many applications but GPS only works well outdoors and drains battery • Wi-Fi APs are commonly found in populated areas and hardware is low cost/low power compared to GPS • Use Wi-Fi APs as location beacons • Requires map of APs • Indoor version RADAR has high overhead • Only need accuracy on the order of 10 m Manhattan (Wigle.net)

  3. War-driving • Used to create training data • Drive a laptop with Wi-Fi card and GPS through the streets of city and collect information • Data – “radio map” • AP unique ID • GPS location of received signal • Signal strength • Response Rate

  4. Experimental Data Sets Downtown (Seattle) Urban Residential (Ravenna) Suburban (Kirkland)

  5. Algorithm - Centroid • 1st of 3/4 algorithms used • Use arithmetic mean of positions of all AP’s • Not actually use centroid AP AP Estimate AP

  6. Algorithm – Fingerprinting SS • Use 4 closest APs in the Euclidean distance defined by signal strength (k-nearest neighbor) • Assuming is the signal strength from the thAP from the map and is from the received data • Weighting showed only marginal improvement • Allow +/- 2 APs for robustness over time • Based on Bahl 00

  7. Algorithm –Fingerprinting Rank • All hardware will not give same signal strength • Instead rank signal strength and use correlation with 3 points from radio map • Where and denotes the mean • Based on Krumm 03       =(-20, -90, -40) ->     =(1,3,2)

  8. Algorithm - Particle Filter • Particle filters, or a Sequential Monte Carlo method, is a recursive Bayesian estimator • Empirical data model, using training data • Signal strength as function distance to AP • Response rate as function of distance to AP • Random walk assumed for motion • Often used for noisy non-linear or non-Gaussian models

  9. Full Results • Rank algorithm does not work with sparse APs

  10. More APs Lowers Error • Rank requires more than 1

  11. AP Reduction • Localization works well even with 60% APs lost

  12. Adding Noise to GPS Data • Centroid and particle filter work with noise

  13. Reducing Map Density • Works well up to 25 mph, 1 scan/sec

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