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Efficient Wi-Fi Localization for Urban Scale: Coverage, Accuracy, and Cost

In urban environments, accurate Wi-Fi-based localization is essential for context-aware applications and emergency services. This study explores using existing Wi-Fi infrastructure with minimal calibration overhead to achieve high accuracy in positioning. By leveraging war-driving maps and various algorithms, including fingerprinting and particle filters, the research demonstrates that Wi-Fi positioning can provide accuracy comparable to GPS, especially in indoor settings. Factors like AP density, turnover, and GPS noise are analyzed to optimize localization performance, showing that dense AP records lead to better accuracy. Overall, the study concludes that low calibration overhead and minimal impact from AP turnover and GPS noise make Wi-Fi localization a cost-effective and reliable solution for urban environments.

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Efficient Wi-Fi Localization for Urban Scale: Coverage, Accuracy, and Cost

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  1. Accuracy Characterization for Metropolitan-scale Wi-Fi Localization Yu-Chung Cheng (UCSD, Intel Research) Yatin Chawathe (Intel Research) Anthony LaMarca (Intel Research) John Krumm (Microsoft Research)

  2. Introduction • Context-aware applications are prevalent • Maps • Location-enhanced content • Socialapplications • Emergency services (E911) • A key enabler: location systems • Must have high coverage • Work wherever we take the devices • Low calibration overhead • Scale with the coverage • Low cost • Commodity devices

  3. Riding the Wi-Fi wave • Wi-Fi is everywhere now • No new infrastructure • Low cost • APs broadcast beacons • “War drivers” already build AP maps • Calibrated using GPS • Constantly updated • Position using Wi-Fi • Indoor Wi-Fi positioning gives 2-3m accuracy • But requires high calibration overhead: 10+ hours per building • What if we use war-driving maps for positioning? Manhattan (Courtesy of Wigle.net)

  4. Why not just use GPS? • High coverage and accuracy (<10m) • But, does not work indoors or in urban canyons • GPS devices are not nearly as prevalent as Wi-Fi

  5. Methodology • Training phase • Collect AP beacons by “war driving” with Wi-Fi card + GPS • Each scan records • A GPS coordinate • List of Access Points • Covers one neighborhood in 1 hr (~1 km2) • Build radio map from AP traces • Positioning phase • Use radio map to position the user • Compare the estimated position w/ GPS

  6. Downtown vs. Urban Residential vs. Suburban Downtown (Seattle) Urban Residential (Ravenna) Suburban (Kirkland)

  7. Evaluation • Choice of algorithms • Naïve, Fingerprint, Particle Filter • Environmental Factors • AP density: do more APs help? • #APs/scan? • AP churn: does AP turnover hurt? • GPS noise: what if GPS is inaccurate? • Datasets • Scanning rate?

  8. Compare Accuracy of Different Algorithms • Centroid • Estimate position as arithmetic mean of positions of all heard APs • Fingerprinting • User hears APs with some signal strength signature • Match closest 3 signatures in the radio map • RADAR: compare using absolute signal strengths [Bahl00] • RANK: compare using relative ranking of signal strengths [Krumm03] • Particle Filters • Probabilistic approximation algorithm for Bayes filter

  9. Baseline Results • Algorithms matter less (except rank) • AP density (horizontal/vertical) matters

  10. Effect of APs per scan • More APs/scan  lower median error • Rank does not work with 1 AP/scan

  11. Effects of AP Turnovers • Minimal effect on accuracy even with 60% AP turnover

  12. Effects of GPS noise • Particle filter & Centroid are insensitive to GPS noise

  13. Scanning density • 1 scan per 10 meters is good == 25 mph driving speed at 1 scan/sec • More war-drives do not help

  14. Summary • Wi-Fi-based location with low calibration overhead • 1 city neighborhood in 1 hour • Positioning accuracy depends mostly on AP density • Urban 13~20m, Suburban ~40m • Dense ap records get better acuracy • In urban area, simple (Centroid) algo. yields same accuracy as other complex ones • AP turnovers & low training data density do not degrade accuracy significantly • Low calibration overhead • Noise in GPS only affects fingerprint algorithms

  15. Q & A http://placelab.org

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