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A Clustering Density Weighted Algorithm of KNN Fingerprint Location Based on Voronoi Diagram

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Wireless Sensor Networks (CWSN 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 812))

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Abstract

Many existing wireless sensor network localization methods encounter low accuracy and high computational complexity, to address this problem, this paper proposes an improved clustering density weighted algorithm of fingerprint location based on Voronoi diagram. First, the seed points are selected by using a uniform design method within the location region, then the location region is divided based on seed points and Voronoi diagram. At the same time, aiming to estimate the location area accurately, the Dixon’s test is employed to filter the gross errors. Finally, considering the problem of low accuracy for traditional K-nearest neighbors (KNN) method, it combines the clustering algorithm and KNN method, proposes a new positioning algorithm with density weighted to obtain the final results. The experiment indicates that the improved algorithm reduces the searching time of fingerprint database effectively on the premise of high positioning accuracy and improves the efficiency without adding any costs of network or energy consumption.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 61363059, No. 61762079, and No. 61662070, Key Science and Technology Support Program of Gansu Province under Grant No. 1604FKCA097 and No. 17YF1GA015, Science and Technology Innovation Project of Gansu Province under Grant No. 17CX2JA037 and No. 17CX2JA039.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Xiaochao Dang, Yili Hei & Zhanjun Hao

  2. Internet of Things Engineering Research Center of Gansu Province, Lanzhou, 730070, China

    Xiaochao Dang & Zhanjun Hao

Authors
  1. Xiaochao Dang
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  2. Yili Hei
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  3. Zhanjun Hao
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Corresponding author

Correspondence to Zhanjun Hao .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Jianzhong Li

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Huadong Ma

  3. Tianjin University, Tianjin, Tianjin, China

    Keqiu Li

  4. Chinese Academy of Sciences, Beijing, China

    Li Cui

  5. Chinese Academy of Sciences, Beijing, China

    Limin Sun

  6. Tianjin University, Tianjin, China

    Zenghua Zhao

  7. Tianjin University, Tianjin, China

    Xiaofei Wang

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Dang, X., Hei, Y., Hao, Z. (2018). A Clustering Density Weighted Algorithm of KNN Fingerprint Location Based on Voronoi Diagram. In: Li, J., et al. Wireless Sensor Networks. CWSN 2017. Communications in Computer and Information Science, vol 812. Springer, Singapore. https://doi.org/10.1007/978-981-10-8123-1_16

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  • DOI: https://doi.org/10.1007/978-981-10-8123-1_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8122-4

  • Online ISBN: 978-981-10-8123-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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