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Effective RSS Sampling for Forensic Wireless Localization

  • Yinjie Chen
  • Zhongli Liu
  • Xinwen Fu
  • Wei Zhao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7992)

Abstract

In many applications such as wireless crime scene investigation, we want to use a single device moving along a route for accurate and efficient localization without the help of any positioning infrastructure or trained signal strength map. Our experiments show that in a complicated environment, such as building corridors and downtown areas, triangulation or trilateration cannot be used for accurate localization via single device. A simple approach, which is better and robust, is to use where the maximum RSS (received signal strength) is sensed as the target’s location. The question is how to make sure the maximum RSS is received while moving. Our novel RSS sampling theory presented in this paper answers this question: if RSS samples can reconstruct a target transmitter’s power distribution over space, the location corresponding to the peak of such power distribution is the target’s location. We apply the Nyquist sampling theory to the RSS sampling process, and derive a mathematical model to determine the RSS sampling rate given the target’s distance and its packet transmission rate. To validate our RSS sampling theory, we developed BotLoc, which is a programmable and self-coordinated robot armed with a wireless sniffer. We conducted extensive simulations and real-world experiments and the experimental results match the theory very well. A video of BotLoc is at http://youtu.be/FsWLrH8Nj50 .

Keywords

Child Pornography Indoor Localization WiFi Network Complicated Environment Search Warrant 
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 2013

Authors and Affiliations

  • Yinjie Chen
    • 1
  • Zhongli Liu
    • 1
  • Xinwen Fu
    • 1
  • Wei Zhao
    • 2
  1. 1.Computer Science DepartmentUniversity of Massachusetts LowellUSA
  2. 2.University of MacauMacau

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