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Enhanced Wi-Fi Localization for Metropolitan Applications

Review of Wi-Fi-based localization methods in urban settings. Discusses algorithms and experimental data sets for accurate location tracking, comparing centroid, fingerprinting SS, and particle filter methods. Evaluates the impact of different factors on localization performance such as AP density, noise in GPS data, and map density reduction.

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Enhanced Wi-Fi Localization for Metropolitan Applications

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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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