Improving Location Fingerprinting through Motion Detection and Asynchronous Interval Labeling

  • Philipp Bolliger
  • Kurt Partridge
  • Maurice Chu
  • Marc Langheinrich
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5561)


Wireless signal strength fingerprinting has become an increasingly popular technique for realizing indoor localization systems using existing WiFi infrastructures. However, these systems typically require a time-consuming and costly training phase to build the radio map. Moreover, since radio signals change and fluctuate over time, map maintenance requires continuous re-calibration. We introduce a new concept called “asynchronous interval labeling” that addresses these problems in the context of user-generated place labels. By using an accelerometer to detect whether a device is moving or stationary, the system can continuously and unobtrusively learn from all radio measurements during a stationary period, thus greatly increasing the number of available samples. Movement information also allows the system to improve the user experience by deferring labeling to a later, more suitable moment. Initial experiments with our system show considerable increases in data collected and improvements to inferred location likelihood, with negligible overhead reported by users.


Access Point Receive Signal Strength Motion Detection Indoor Localization Indoor Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Philipp Bolliger
    • 1
  • Kurt Partridge
    • 2
  • Maurice Chu
    • 2
  • Marc Langheinrich
    • 3
  1. 1.Institute for Pervasive ComputingETH ZurichSwitzerland
  2. 2.Palo Alto Research CenterPalo AltoUSA
  3. 3.Faculty of InformaticsUniversity of LuganoSwitzerland

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