A Weighted K-AP Query Method for RSSI Based Indoor Positioning

  • Huan Huo
  • Xiufeng Liu
  • Jifeng Li
  • Huhu Yang
  • Dunlu Peng
  • Qingkui Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9877)

Abstract

The paper studies the establishment of offline fingerprint library based on RSSI (Received Signal Strength Indication), and proposes WF-SKL algorithm by introducing the correlation between RSSIs. The correlations can be transformed as AP fingerprint sequence to build the offline fingerprint library. To eliminate the positioning error caused by instable RSSI value, WF-SKL can filter the noise AP via online AP selection, meanwhile it also reduces the computation load. WF-SKL utilizes LCS algorithm to find out the measurement between the nearest neighbors, and it proposes K-AP (P,Q) nearest neighbor queries between two sets based on Map-Reduce framework. The algorithm can find out K nearest positions and weighted them for re-positioning to accelerate the matching speed between online data and offline data, and also improve the efficiency of positioning. According to a large scale positioning experiments, WF-SKL algorithm proves its high accuracy and positioning speed comparing with KNN indoor positioning.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Huan Huo
    • 1
  • Xiufeng Liu
    • 2
  • Jifeng Li
    • 3
  • Huhu Yang
    • 1
  • Dunlu Peng
    • 1
  • Qingkui Chen
    • 1
  1. 1.School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghaiChina
  2. 2.Technical University of DenmarkKongens LyngbyDenmark
  3. 3.University of OuluOuluFinland

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