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Adapting Wi-Fi samples to environmental changes automatically

Published:13 September 2014Publication History

ABSTRACT

In recent years, a positioning method which utilizes wireless LAN without using GPS has attracted attention. Especially, in the case of a method which combines absolute position with a Wi-Fi radio environment in advance, the cost of operation and management becomes enormous. Therefore, by sampling Wi-Fi radio information observed at points where users stay frequently or in the long-term, a method which automates to collect and update the Wi-Fi radio information has been proposed. In the case of a long-term operating, the positioning accuracy, however, decreases because this method does not perform well in maintaining and managing samples. It cannot adapt samples to environmental changes although Wi-Fi radio signals change in case of long-term operating. Accordingly, this paper proposes a new calculation formula for improving a positioning accuracy. The formula is calculated with the weight of each base station for avoidance of ill-behaving stations. In addition, this paper also proposes the automated management system with two steps. It adapts samples to changes of Wi-Fi radio signals and a user's behavior. As a result, a positioning accuracy of the new system is higher than existing one.

References

  1. Nishio Nobuhiko, Fukuzaki Yuuki, and Azumi Takuya. Detecting wi-fi base station behavior inappropriate for positioning method in participatory sensing logs. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, UbiComp '13 Adjunct, pages 665--672. ACM, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Sinno Jialin Pan, James T. Kwok, Qiang Yang, and Jeffrey Junfeng Pan. Adaptive localization in a dynamic wifi environment through multi-view learning. In Proceedings of the 22Nd National Conference on Artificial Intelligence - Volume 2, AAAI'07, pages 1108--1113. AAAI Press, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Yin Jie, Yang Qiang, and Lionel Ni. Adaptive temporal radio maps for indoor location estimation. In Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications, PERCOM '05, pages 85--94. IEEE Computer Society, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Jiang Yifei, Pan Xin, Li Kun, Lv Qin, Dick Robert P., Hannigan Michael, and Shang Li. Ariel: Automatic wi-fi based room fingerprinting for indoor localization. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, UbiComp '12, pages 441--450. ACM, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. Adapting Wi-Fi samples to environmental changes automatically

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    • Published in

      cover image ACM Conferences
      UbiComp '14 Adjunct: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication
      September 2014
      1409 pages
      ISBN:9781450330473
      DOI:10.1145/2638728

      Copyright © 2014 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 September 2014

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