Tong Yinghua, Lin Lianhai, Tian Liqin, Wang Zhigang, Wu Wenxing, Wu Junyi
School of Computer Science, Qinghai Normal University, Xining, 810016, Qinghai, China.
School of Computer Science, North China Institute of Science and Technology, Langfang, 065201, Hebei, China.
Sci Rep. 2025 Jul 13;15(1):25332. doi: 10.1038/s41598-025-09849-4.
Wireless sensor networks (WSNs) are widely used in various applications requiring efficient coverage and minimal resource utilization. This paper presents an enhanced hybrid particle swarm optimization (EHPSO) algorithm that incorporates a spatial position encoding (SPE) strategy to optimize coverage while dynamically adjusting the number of sensors deployed in WSNs. The proposed approach leverages the strengths of particle swarm optimization (PSO) by integrating it with the SPE mechanism, which effectively guides the search process towards high-quality solutions. The EHPSO algorithm is designed to balance exploration and exploitation capabilities, enabling dynamic node adjustment and ensuring robust performance across different network configurations and environmental conditions. Extensive simulations are conducted to evaluate the performance of the proposed method against state-of-the-art algorithms in terms of coverage quality and node count. A multi-objective optimization model is also established, further illustrating the algorithm's performance and its effectiveness in balancing the number of sensors and coverage rate. Results demonstrate improvements in coverage optimization and reduction of node deployment compared to existing methods. This research contributes to more efficient and cost-effective deployment strategies for WSNs, particularly in scenarios where resources are limited and optimal coverage is critical.
无线传感器网络（WSNs）广泛应用于各种需要高效覆盖和最小资源利用的应用场景。本文提出了一种增强型混合粒子群优化（EHPSO）算法，该算法结合了空间位置编码（SPE）策略，以优化覆盖范围，同时动态调整无线传感器网络中部署的传感器数量。所提出的方法通过将粒子群优化（PSO）与SPE机制相结合，利用了粒子群优化的优势，有效地引导搜索过程朝着高质量的解决方案进行。EHPSO算法旨在平衡探索和利用能力，实现动态节点调整，并确保在不同网络配置和环境条件下具有强大的性能。进行了广泛的仿真，以根据覆盖质量和节点数量评估所提出方法相对于现有算法的性能。还建立了一个多目标优化模型，进一步说明了该算法在平衡传感器数量和覆盖率方面的性能及其有效性。结果表明，与现有方法相比，在覆盖优化和减少节点部署方面有改进。这项研究有助于为无线传感器网络制定更高效、更具成本效益的部署策略，特别是在资源有限且最佳覆盖至关重要的场景中。
