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<p>To address the problems of low node deployment coverage and slow convergence of sensor networks (WSN) on three-dimensional surfaces, this paper proposes a three-dimensional surface sensor network node deployment scheme with an improved sparrow search algorithm, based on a three-dimensional surface sensor network coverage model with an improved blind area determination method, and addresses the shortcomings of the sparrow search algorithm’s population diversity decreasing too fast in its population initialization The Circle chaotic mapping that enhances the initialized population diversity is introduced in the process, while the idea of golden sine strategy is borrowed to further improve the performance of the discoverer in the sparrow search algorithm and avoid the algorithm from falling into local optimum. Experimental results show that the proposed 3dSN-SSA method can increase the coverage of the sensor network and complete the convergence of the algorithm with fewer iterations, effectively reducing the convergence time.</p>

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3dSN-SSA: wireless sensor network deployment of 3D surface based on improved sparrow search algorithm

  • 期刊论文

  • 发表日期：

    2025年8月14日

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  • Li Tan,
  • Yuzhao Liu,
  • Hongtao Zhang,
  • Tianli Yuan,
  • Ziliang Shang,
  • Xujie Jiang

摘要

To address the problems of low node deployment coverage and slow convergence of sensor networks (WSN) on three-dimensional surfaces, this paper proposes a three-dimensional surface sensor network node deployment scheme with an improved sparrow search algorithm, based on a three-dimensional surface sensor network coverage model with an improved blind area determination method, and addresses the shortcomings of the sparrow search algorithm’s population diversity decreasing too fast in its population initialization The Circle chaotic mapping that enhances the initialized population diversity is introduced in the process, while the idea of golden sine strategy is borrowed to further improve the performance of the discoverer in the sparrow search algorithm and avoid the algorithm from falling into local optimum. Experimental results show that the proposed 3dSN-SSA method can increase the coverage of the sensor network and complete the convergence of the algorithm with fewer iterations, effectively reducing the convergence time.