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Research on an optimal selection method for sensor network node under high-speed mobile environment

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Abstract

The traditional selection methods for the selection of sensor network node under high-speed mobile environment is randomness, which cannot effectively use the multiple attributes of nodes, resulting in the irregular distribution of cluster head of nodes, and high energy consumption. An optimal selection method for sensor network node under the high-speed mobile environment based on EERNFS and Naive Bayesian Networks is proposed. By using EERNFS algorithm within each network intercept / sleep cycle, the sensor network node is made processing under high-speed mobile environment, to ensure the stable local connectivity and consistent collaboration intercepts, and reduces energy consumption. Naive Bayes algorithm is used to make optimization of general Bayesian classification method, and set the parameters. In the two-dimensional area, several sensor nodes are randomly placed, and the known two-dimensional area is divided. A certain node is selected arbitrarily in different regions to constitute the original set of Naive Bayes algorithm, and compute the training results and thresholds in the Bayesian system of node set at this moment. On the basis of the threshold, the optimal selection of sensor network node is achieved. Simulation results show that the proposed method not only has higher node coverage, but also the operating efficiency and energy consumption are better than the genetic method.

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Authors and Affiliations

  1. Teaching and Management Center for Information, Jilin Agricultural University, Changchun, Jilin, 130118, China

    Li Zhu

  2. Humanitas College of Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul, South Korea

    Hwa Young Jeong

Authors
  1. Li Zhu
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  2. Hwa Young Jeong
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Correspondence to Li Zhu.

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Zhu, L., Jeong, H.Y. Research on an optimal selection method for sensor network node under high-speed mobile environment. Multimed Tools Appl 76, 19635–19647 (2017). https://doi.org/10.1007/s11042-015-3234-9

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  • DOI: https://doi.org/10.1007/s11042-015-3234-9

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