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Area of Science:

  • Computer Science
  • Network Engineering
  • Data Privacy

Background:

  • Data privacy regulations restrict WiFi network operators' ability to track client activity.
  • Randomized MAC addresses in client devices complicate traditional methods of counting and mapping users via Probe Requests.

Purpose of the Study:

  • To develop a method for mapping WiFi client densities in outdoor networks despite MAC randomization.
  • To extend the statistical proportionality between client counts and Probe Requests to spatial mapping.
  • To eliminate the need for ground truth measurements (e.g., cameras) for calibration.

Main Methods:

  • Proposed a toolkit of nine distinct tools.
  • Utilized raw, randomized MAC Probe Request counts as input.
  • Developed a method to transform Probe Request data into a calibrated client density map.

Main Results:

  • Successfully extended the client count proportionality to spatial mapping.
  • Created a density map of client distribution in outdoor WiFi networks.
  • Enabled client mapping without reliance on external ground truth data.

Conclusions:

  • The proposed toolkit effectively addresses the challenge of localizing randomized MAC clients.
  • This approach provides a viable solution for WiFi network operators to understand client distribution for commercial and security purposes.
  • The method offers a privacy-preserving way to gain insights into network usage patterns.