KSII Transactions on Internet and Information Systems (TIIS)

Korean Society for Internet Information (한국인터넷정보학회)
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Group Power Constraint Based Wi-Fi Access Point Optimization for Indoor Positioning

  • Pu, Qiaolin (Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications) ;
  • Zhou, Mu (Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications) ;
  • Zhang, Fawen (Computer Science & Technology College, Chongqing University of Posts and Telecommunications) ;
  • Tian, Zengshan (Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications)
  • Received : 2017.01.13
  • Accepted : 2017.12.23
  • Published : 2018.05.31
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Abstract

Wi-Fi Access Point (AP) optimization approaches are used in indoor positioning systems for signal coverage enhancement, as well as positioning precision improvement. Although the huge power consumption of the AP optimization forms a serious problem due to the signal coverage requirement for large-scale indoor environment, the conventional approaches treat the problem of power consumption independent from the design of indoor positioning systems. This paper proposes a new Fast Water-filling algorithm Group Power Constraint (FWA-GPC) based Wi-Fi AP optimization approach for indoor positioning in which the power consumed by the AP optimization is significantly considered. This paper has three contributions. First, it is not restricted to conventional concept of one AP for one candidate AP location, but considered spare APs once the active APs break off. Second, it utilizes the concept of water-filling model from adaptive channel power allocation to calculate the number of APs for each candidate AP location by maximizing the location fingerprint discrimination. Third, it uses a fast version, namely Fast Water-filling algorithm, to search for the optimal solution efficiently. The experimental results conducted in two typical indoor Wi-Fi environments prove that the proposed FWA-GPC performs better than the conventional AP optimization approaches.

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References

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