KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Path Loss Exponent Estimation for Indoor Wireless Sensor Positioning

  • Lu, Yu-Sheng (Department of Engineering Science, National Cheng Kung University) ;
  • Lai, Chin-Feng (Department of Engineering Science, National Cheng Kung University) ;
  • Hu, Chia-Cheng (Department of Information Management, Naval Academy) ;
  • Huang, Yueh-Min (Department of Engineering Science, National Cheng Kung University) ;
  • Ge, Xiao-Hu (Department of Electronics and Information Engineering, Huazhong University of Scince & Technology Wuhan)
  • Received : 2010.05.28
  • Accepted : 2010.06.14
  • Published : 2010.06.30
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Abstract

Rapid developments in wireless sensor networks have extended many applications, hence, many studies have developed wireless sensor network positioning systems for indoor environments. Among those systems, the Global Position System (GPS) is unsuitable for indoor environments due to Line-Of-Sight (LOS) limitations, while the wireless sensor network is more suitable, given its advantages of low cost, easy installation, and low energy consumption. Due to the complex settings of indoor environments and the high demands for precision, the implementation of an indoor positioning system is difficult to construct. This study adopts a low-cost positioning method that does not require additional hardware, and uses the received signal strength (RSS) values from the receiver node to estimate the distance between the test objects. Since many objects in indoor environments would attenuate the radio signals and cause errors in estimation distances, knowing the path loss exponent (PLE) in an environment is crucial. However, most studies preset a fixed PLE, and then substitute it into a radio propagation loss model to estimate the distance between the test points; such method would lead to serious errors. To address this problem, this study proposes a Path Loss Exponent Estimation Algorithm, which uses only four beacon nodes to construct a radio propagation loss model for an indoor environment, and is able to provide enhanced positioning precision, accurate positioning services, low cost, and high efficiency.

Keywords

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