一种基于智能算法的优化聚类方法,用于收集能量 WSNN
Sanjai Prasada Rao Banoth1, Biswa Mohan Sahoo2, Anil Kumr Gankotiya3
1School of Technology, Woxsen University, Hyderabad, India.
Scientific reports
|December 8, 2025
概括
收集能源的无线传感器网络 (EH-WSNs) 从MCSOC集群中受益,这可以优化能源,通信成本和节点距离. 这种方法显著提高了实际应用的网络寿命和吞吐量.
科学领域:
- 无线传感器网络 无线传感器网络
- 优化算法 优化算法
- 收集能源 收集能源
背景情况:
- 收集能源的无线传感器网络 (EH-WSNs) 在平衡传感,能源动态和通信成本方面面临着挑战.
- 有效的集群对于最大限度地提高EH-WSNs的运营寿命和性能至关重要.
研究的目的:
- 为EH-WSNs引入MCSOC (基于修改的Cat-Swarm优化的集群).
- 根据剩余能量,集群内部距离,集群间传输成本和节点到下沉距离,共同优化集群头部选择.
主要方法:
- 开发了一个域内意识的,多目标的健身功能,用于集群头的选择.
- 使用了修改后的猫群优化算法.
- 在两个部署场景 (200x200m2和500x500m2与200个节点) 上使用一级无线电能量模型评估MCSOC.
主要成果:
- 与基准方法 (NEHCP,ROTEE,SMEOR,GAPSO-H) 相比,MCSOC证明了优越的网络寿命,吞吐量和稳定性.
- 实现了显著的性能提升:高达42.13%的网络性能,45.57%的稳定性和48.48%的吞吐量比GAPSO-H.
- 显示了早期死亡节点的更高节约比例,这表明能源管理得到了改进.
结论:
- MCSOC为EH-WSNs的长寿命传感提供了实用和有效的解决方案.
- 拟议的方法适用于精密农业,智能城市环境监测和工业健康监测.
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