DBSCAN-Based Mobile AP Detection for Indoor WLAN Localization

  • Wei Nie
  • Hui YuanEmail author
  • Mu Zhou
  • Liangbo Xie
  • Zengshan Tian
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


The vast market of location-based services (LBSs) has brought opportunities for the rapid development of indoor positioning technology. In current indoor venues, by considering the fact that the wireless local area network (WLAN) infrastructure is widely deployed, the indoor WLAN localization method has become the focus of study. Nowadays, the WLAN module is used widely in a large number of advanced mobile devices, and meanwhile there are a variety of WLAN mobile access points (APs) in indoor environment. In this circumstance, due to the uncertainty of the state of mobile APs, the associated received signal strength (RSS) data are usually lowly dependent on the locations, which will consequently result in the decrease in localization accuracy. To solve this problem, a new method of mobile AP detection based on the density-based spatial clustering of applications with noise (DBSCAN) is proposed. This method aims to identify mobile APs in target area so as to eliminate the adverse impact of mobile APs on localization accuracy.


Indoor localization mobile AP detection Location dependency DBSCAN WLAN 



This work is supported in part by the National Natural Science Foundation of China (61771083, 61704015), Program for Changjiang Scholars and Innovative Research Team in University (IRT1299), Special Fund of Chongqing Key Laboratory (CSTC), Fundamental Science and Frontier Technology Research Project of Chongqing (cstc2017jcyjAX0380, cstc2015jcyjBX0065), Scientific and Technological Research Foundation of Chongqing Municipal Education Commission (KJ1704083), and University Outstanding Achievement Transformation Project of Chongqing (KJZH17117).


  1. 1.
    Saha HN, Basu S, Auddy S, et al. A low cost fully autonomous GPS (Global Positioning System) based quad copter for disaster management. In: IEEE annual computing and communication workshop and conference; 2014. p. 654–60.Google Scholar
  2. 2.
    Chan F, Chan YT, Inkol R. Path loss exponent estimation and RSS localization using the linearizing variable constraint. In: Military communications conference; 2016. p. 225–9.Google Scholar
  3. 3.
    Zhou M, Tang Y, Tian Z, et al. Semi-supervised learning for indoor hybrid fingerprint database calibration with low effort. IEEE Access. 2017;5(99):4388–400.CrossRefGoogle Scholar
  4. 4.
    Chai P, Zhang L. Indoor radio propagation models and wireless network planning. In: IEEE international conference on computer science and automation engineering; 2012. p. 738–41.Google Scholar
  5. 5.
    Cheung KW, Sau JHM, Murch RD. A new empirical model for indoor propagation prediction. IEEE Trans Veh Technol. 1998;47(3):996–1001.CrossRefGoogle Scholar
  6. 6.
    Wang J, Tan N, Luo J, et al. WOLoc: WiFi-only outdoor localization using crowdsensed hotspot labels. In: INFOCOM 2017—IEEE conference on computer communications; 2017. p. 1–9.Google Scholar
  7. 7.
    Markom MA, Adom AH, Shukor SAA, et al. Scan matching and KNN classification for mobile robot localisation algorithm. In: IEEE international symposium in robotics and manufacturing automation; 2017. p. 1–6.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Wei Nie
    • 1
  • Hui Yuan
    • 1
    Email author
  • Mu Zhou
    • 1
  • Liangbo Xie
    • 1
  • Zengshan Tian
    • 1
  1. 1.Chongqing Key Lab of Mobile Communications TechnologyChongqing University of Posts and TelecommunicationsChongqingChina

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