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An improved method for sink node deployment in wireless sensor network to big data

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Abstract

Wireless sensor network (WSNs) technology and Internet technology penetrate and extend each other. It is a good way for physical changes of objects, state recognition and data collection, and becomes an important source of network data in big data. Compared with traditional wireless networks, WSNs have the characteristics of integrating sensing, processing, and transmission, limited hardware resources, limited power supply capacity, no center, self-organization, multi-hop routing, dynamic topology, large number of nodes, and dense distribution. In order to improve the energy utilization rate of a single node to a greater extent, reduce the energy consumption of the entire WSNs, and extend the life cycle of WSNs, high-efficiency networking is essential in the application of WSNs. Networking is one of the foundations to the large-scale WSNs. The network model and node location deployment are important technologies for WSNs networking. Based on the network characteristics of large-scale WSNs and the transmission capacity of big data, a new type of network model suitable is presented which combined the advantages of Star model and Mesh model. More importantly, the deployment environment of sensor nodes is a spatial network. The data collected and transmitted by large-scale WSNs is very large. The deployment of sensor nodes in space can ensure that the big data collected and transmitted are true and effective. This research proposes the space density first (SDF) algorithm, which improves the neighbor density first algorithm with the space node deployment and the density-optimized SDF algorithm. The SDF algorithm saves network energy and extends the life of the network. Experimental results show that large-scale WSNs built with a new networking model and SDF algorithm can collect and transmit big data stably and reliably, which saves network energy and improves the accuracy of big data.

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Acknowledgements

This work was supported in part by the Innovative Province Construction Special Project of Hunan, China (Grant No. 2020NK2033), by the Scientific Research Fund of Hunan Provincial Education Department, China (Grant No. 20A249 and Grant No. 20A259), and in part by the Scientific Research and Technology Development Project of Hezhou, China (Grant No. HeGongJian1908016). (Corresponding authors: Xinghui Zhu; Kui Fang.)

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Authors and Affiliations

  1. The College of Information and Intelligence, Hunan Agricultural University, Changsha, 410128, China

    Yineng Chen, Xinghui Zhu, Kui Fang, Xiaoxuan Chen, Cheng Li, Qingshan Ren & Zhuoyang Zou

  2. The College of Food and Bioengineering, Hezhou University, Hezhou, 542899, China

    Ting Guo

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  1. Yineng Chen
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  2. Xinghui Zhu
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  3. Kui Fang
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  4. Xiaoxuan Chen
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  6. Cheng Li
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  7. Qingshan Ren
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  8. Zhuoyang Zou
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Correspondence to Xinghui Zhu or Kui Fang.

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Chen, Y., Zhu, X., Fang, K. et al. An improved method for sink node deployment in wireless sensor network to big data. Neural Comput & Applic 34, 9499–9510 (2022). https://doi.org/10.1007/s00521-021-06443-3

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