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An Optimized Cluster Structure Routing Method Based on LEACH in Wireless Sensor Networks

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Abstract

In order to improve the accuracy and speed of traditional K-Nearest-Neighbor (KNN) algorithms and solve the problem of determining the most appropriate initial center and number of clusters of K-Means algorithm, an improved clustering dynamic threshold location algorithm (ICD-KNN) is proposed in this paper. The algorithm consists of two stages: clustering stage and position estimation stage. The clustering stage employs K-Means clustering algorithm based on Canopy. The Canopy is used as the preprocessing procedure to improve efficiency and clustering accuracy of K-Means algorithm. In the position estimation stage, threshold is set dynamically according to the dispersion of reference points to filter out singular reference points and improve positioning accuracy compared with the previous threshold algorithms. We compare proposed algorithm with several existing algorithms. The simulation results show that the positioning accuracy of ICD-KNN is improved by 10%, 38% and 39% respectively compared with dynamic threshold algorithms (DH-KNN), K-Means algorithms and KNN algorithms.

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Acknowledgements

This work was supported by Science and Technology Major Project of Anhui Province (No. 17030901055) and  Development and Reform Commission of Anhui Province, China (2020478).

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Authors and Affiliations

  1. School of Physics and Electronic Information, Anhui Normal University, Wuhu, 241000, China

    Xu-Xing Ding & Ya-Ni Liu

  2. Anhui Huamin Avionics System Co., Ltd., Wuhu, 241002, China

    Liang-Yong Yang

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  1. Xu-Xing Ding
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Correspondence to Xu-Xing Ding.

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Ding, XX., Liu, YN. & Yang, LY. An Optimized Cluster Structure Routing Method Based on LEACH in Wireless Sensor Networks. Wireless Pers Commun 121, 2719–2733 (2021). https://doi.org/10.1007/s11277-021-08845-x

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