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<p>Wireless sensor network (WSN), composed of numerous distributed sensor nodes, is widely used across various fields but faces several challenges, with coverage optimization being one of the most critical. The size of the node coverage area directly impacts network performance. To address this issue, a multi-strategy dung beetle optimization algorithm (MsDBO) is proposed to optimize coverage in two-dimensional and three-dimensional regions. In MsDBO, to maximize the effectiveness of rolling ball beetles, the proportion of rolling ball beetles is dynamically changed. Optimal boundary control strategy effectively utilizes relevant positional information to ensure that the search capability of the algorithm is not affected by boundary constraints. Additionally, a random number is employed to change the inertia factor. Finally, to enhance the capability of the algorithm to escape local optima, the current solution, optimal solution, and position of randomly selected are integrated to generate a new solution. Subsequently, compared with other algorithms on the CEC2017 and CEC2022 test sets, the performance of the algorithm is evaluated in both two-dimensional and three-dimensional WSN scenarios. The results indicate that the maximum average coverage of MsDBO in 2D and 3D scenes can reach 96.97% and 77.7%, respectively.</p>

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Coverage optimization in wireless sensor network based on multi-strategy dung beetle optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年5月14日

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  • Zhilan Chen,
  • Jiatang Cheng,
  • Meng He

摘要

Wireless sensor network (WSN), composed of numerous distributed sensor nodes, is widely used across various fields but faces several challenges, with coverage optimization being one of the most critical. The size of the node coverage area directly impacts network performance. To address this issue, a multi-strategy dung beetle optimization algorithm (MsDBO) is proposed to optimize coverage in two-dimensional and three-dimensional regions. In MsDBO, to maximize the effectiveness of rolling ball beetles, the proportion of rolling ball beetles is dynamically changed. Optimal boundary control strategy effectively utilizes relevant positional information to ensure that the search capability of the algorithm is not affected by boundary constraints. Additionally, a random number is employed to change the inertia factor. Finally, to enhance the capability of the algorithm to escape local optima, the current solution, optimal solution, and position of randomly selected are integrated to generate a new solution. Subsequently, compared with other algorithms on the CEC2017 and CEC2022 test sets, the performance of the algorithm is evaluated in both two-dimensional and three-dimensional WSN scenarios. The results indicate that the maximum average coverage of MsDBO in 2D and 3D scenes can reach 96.97% and 77.7%, respectively.