On the empirical performance of self-calibrating WiFi location systems

  • Authors:

  • Daniel Turner;Stefan Savage;Alex C. Snoeren

  • Affiliations:

  • Department of Computer Science and Engineering, University of California, San Diego;Department of Computer Science and Engineering, University of California, San Diego;Department of Computer Science and Engineering, University of California, San Diego

  • Venue:
  • LCN '11 Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks
  • Year:
  • 2011

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Abstract

The pervasive deployment of 802.11 in modern enterprise buildings has long made it an attractive technology for constructing indoor location services. To this end, a broad range of algorithms have been proposed to accurately estimate location from 802.11 signal strength measurements, some without requiring manual calibration for each physical location. Prior work suggests that many of these protocols can be highly effective--reporting median errors of under 2 meters in some instances. However, there are few studies validating these claims at scale, nor comparing the algorithms in a uniform, realistic environment. Our work provides precisely this kind of empirical evaluation in a realistic office building environment. Surprisingly, we find that median errors in our environment are consistently greater than 5 meters and, counter-intuitively, that simpler algorithms frequently outperform their more sophisticated counterparts. In analyzing our results, we argue that unrealistic assumptions about access point densities and underlying variability in the indoor environment may preclude highly accurate location estimates based on 802.11 signal strength.