The Rice Wireless Localization Toolkit

The Rice Wireless Localization (RWL) Toolkit can be used to estimate the position of a device based on the signal strength of nearby 802.11 wireless access points. While prior systems have required significant investments of human labor to build a detailed signal map, our system can be trained by spending less than one minute per office or region, and can then localize the device with very high accuracy after only two or three signal strength measurements.

The RWL was evaluated in Duncan Hall, a large office building with over 200 offices. After about 28 man-hours of training time, the RWL was able to localize a device to the precise, correct location in over 95% of our attempts, across the entire building. Even in the most pathological cases, it almost never localized the device any more distant than to a neighboring office. Furthermore, with a brief calibration period, the RWL can be adapted to work with previously unknown user hardware, and it is robust against a variety of untrained time-varying phenomena.

Downloads

  • Toolkit source code
    Contains the source code of the toolkit, as well as some example programs and tools for gathering data. The toolkit is written in C++/Java and runs under Linux. The techniques used in the toolkit are described in our MOBICOM'04 paper.
    [TGZ]

  • Duncan Hall data set
    This data set contains 51,249 base station scans from 510 locations in Duncan Hall, the computer science building on the Rice University campus. The archive includes a README file that specifies the data format and describes how the data was collected.
    [TGZ]

Related publications

  • Practical Robust Localization over Large-Scale 802.11 Wireless Networks
    Andreas Haeberlen, Eliot Flannery, Andrew M. Ladd, Algis Rudys, Dan S. Wallach, and Lydia E. Kavraki.
    Proceedings of the 10th ACM International Conference on Mobile Computing and Networking (MOBICOM'04), Philadelphia, PA, September 2004
    [PDF] [Slides]

  • Robotics-Based Location Sensing using Wireless Ethernet
    Andrew M. Ladd, Kostas E. Bekris, Algis Rudys, Lydia E. Kavraki, and Dan S. Wallach.
    Wireless Networks, Volume 11, Number 1-2, January 2005, pages 189-204
    [PDF]

  • On the Feasibility of Using Wireless Ethernet for Indoor Localization
    Andrew M. Ladd, Kostas E. Bekris, Algis Rudys, Dan S. Wallach, and Lydia E. Kavraki.
    IEEE Transactions on Robotics and Automation, Vol. 20, No. 3, June 2004, pages 555-559
    [PDF]

  • Wireless LAN Location-Sensing for Security Applications
    Ping Tao, Algis Rudys, Andrew M. Ladd, and Dan S. Wallach.
    Proceedings of the ACM Workshop on Wireless Security (WiSe), San Diego, CA, September 2003
    [PDF]

  • Using Wireless Ethernet for Localization
    Andrew M. Ladd, Kostas E. Bekris, Guillaume Marceau, Algis Rudys, Dan S. Wallach, and Lydia E. Kavraki.
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'02), Lausanne, Switzerland, September 2002
    [PDF]

  • Robotics-Based Location Sensing using Wireless Ethernet
    Andrew M. Ladd, Kostas E. Bekris, Guillaume Marceau, Lydia E. Kavraki, and Dan S. Wallach.
    Proceedings of the 8th ACM International Conference on Mobile Computing and Networking (MOBICOM'02), Atlanta, GA, September 2002
    [PDF]

Contact

If you have any questions about the RWL toolkit, please contact Andreas Haeberlen, Kostas Bekris, Prof. Dan Wallach, or Prof. Lydia Kavraki.

Acknowledgments

The following people contributed to this project (in alphabetical order):

We are grateful for the generous financial support of Microsoft and Schlumberger. The RWL toolkit is dedicated to the memory of Andrew M. Ladd (1978-2007), who played an enormous role in its design, implementation, and evaluation.