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 1 st of 3/4 algorithms used Use arithmetic mean of positions of all AP’s Not actually use centroid AP Estimate

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 th AP 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