• DocumentCode
    2935619
  • Title

    Using Wi-Fi Signal Strength to Localize in Wireless Sensor Networks

  • Author

    Chan, Eddie C L ; Baciu, George ; Mak, S.C.

  • Author_Institution
    Hong Kong Polytech. Univ., Hong Kong
  • Volume
    1
  • fYear
    2009
  • fDate
    6-8 Jan. 2009
  • Firstpage
    538
  • Lastpage
    542
  • Abstract
    Wireless sensor network (WSN) is widely used in many applications such as localization and real-time tracking system. Previous researches commonly suffer the line-of-sight (LOS) problem and dependence on contrast of the background light intensity. Location fingerprinting (LF) method uses a training dataset of received signal strength (RSS) at different location to track the target. The drawbacks of LF method are needed to have extensive training dataset surveying and highly affected by the changing of internal building infrastructure. In this paper, a sensor-based LF method will be implemented to replace extensive site-surveying. Using a Kalman Filter tracks multiple points to characterize a trajectory. Our experimental result shows that the effectiveness of our method leads to have more accurate and effective tracking system.
  • Keywords
    Kalman filters; tracking; wireless LAN; wireless sensor networks; Kalman filter tracks; WSN; Wi-Fi signal strength; background light intensity; line-of-sight problem; location fingerprinting; real-time tracking system; received signal strength; sensor-based LF method; wireless sensor networks; Acoustic sensors; Computer networks; Computer vision; Databases; Fingerprint recognition; Mobile communication; Mobile computing; Target tracking; Trajectory; Wireless sensor networks; Kalman Filter; Location Fingerprinting; Received Signal Strength; WiFi;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communications and Mobile Computing, 2009. CMC '09. WRI International Conference on
  • Conference_Location
    Yunnan
  • Print_ISBN
    978-0-7695-3501-2
  • Type

    conf

  • DOI
    10.1109/CMC.2009.233
  • Filename
    4797055