1 / 11

Accuracy Characterization for Metropolitan-scale Wi-Fi Localization

Accuracy Characterization for Metropolitan-scale Wi-Fi Localization. March 3, 2010 Hon Lung Chu. Place Lab. Protocol which: Gives ubiquitous Wi-Fi based localization Trades accuracy for availability Accuracy: 13-40 meters Require mapping and “training”

rad
Download Presentation

Accuracy Characterization for Metropolitan-scale Wi-Fi Localization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Accuracy Characterization for Metropolitan-scale Wi-Fi Localization March 3, 2010 Hon Lung Chu

  2. Place Lab • Protocol which: • Gives ubiquitous Wi-Fi based localization • Trades accuracy for availability • Accuracy: 13-40 meters • Require mapping and “training” • Authors claim half an hour to map an entire city neighborhood • This is still less than some indoors calibrated system

  3. Place Lab

  4. Key Idea How do we get this?

  5. Localization: Signal Strength

  6. Localization: Response Rate This is much better!

  7. Algorithm #1: Centroid

  8. Algorithm #2: Fingerprinting • Match up the list of APs

  9. Algorithm #3: Particle Filters • Probability-based • Using signal strength and response rate

  10. Result • “Best algorithm varies based on location • Fingerprinting works best overall • Protocol worked best in suburban with a high-density of APs • Can tolerate 50% AP lost • Requires max mean distance of 10 meters to nearest AP

  11. What’s next? • Dynamically switch algorithm based on location and density • Build the Wifi database

More Related