二进制秘书鸟类优化集群通过基于无线传感器网络中沃罗诺伊图的新型健身函数在无线传感器网络中的集群
Mohammed Abdulkareem1, Hadi S Aghdasi1, Pedram Salehpour1
1Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166616471, Iran.
Sensors (Basel, Switzerland)
|July 30, 2025
概括
尽量减少无线传感器网络 (WSN) 的能源消耗至关重要. 拟议的二进制秘书鸟优化集群 (BSBOC) 方法通过优化集群头选择,有效降低能源使用量并延长网络寿命.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 由于电池的限制,能源效率是无线传感器网络 (WSN) 的一个关键挑战.
- 基于集群的层次通信是节约能源和平衡网络负载的共同策略.
- 最佳集群头 (CH) 选择对于网络寿命至关重要,但现有的方法往往忽视空间分布和动态能量状态.
研究的目的:
- 提出一种新的元启发方法,用于WSN中对能源有意识的集群头部选择.
- 解决现有方法在捕捉空间节点分布和动态能量条件方面的局限性.
- 通过改进集群,提高网络寿命并减少WSN的能源消耗.
主要方法:
- 引入了二进制秘书鸟优化集群 (BSBOC) 方法,这是秘书鸟优化算法 (SBOA) 的二进制变体.
- 开发了一种新的多目标健身功能,将CHs的Voronoi图形与其他既定目标相结合.
- 根据各种网络模型和部署场景,对BSBOC与MOBGWO和WAOA进行了全面的模拟比较.
主要成果:
- 与基准方法相比,BSBOC显示能耗显著降低.
- 提出的方法有效地延长了无线传感器网络的网络寿命.
- BSBOC被证明是一种可靠的方法,用于在同质和异质WSN中进行能源意识的集群.
结论:
- 二进制秘书鸟优化集群 (BSBOC) 方法为WSN中的能量意识的CH选择提供了优质的解决方案.
- 在健身功能中整合沃罗诺伊图表可以增强聚类过程的空间意识.
- BSBOC为延长电池驱动的无线传感器网络的运行寿命提供了有希望的进展.
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