通过使用强化学习和基于元启发的节能系统来提高无线传感器网络的功能
1School of Electrical and Mechanical Engineering, Xuchang University, Xuchang, 461000, Henan, China. shiwei_zhang_xcu@126.com.
Scientific reports
|August 21, 2025
概括
这项研究介绍了无线传感器网络的智能节能集群机制. 与现有方法相比,新方法显著增加了网络寿命和数据传输量.
科学领域:
- 计算机科学
- 电气工程
- 网络工程
背景情况:
- 由于能源限制,无线传感器网络 (WSN) 在有效的路由和数据传输方面面临挑战.
- 现有的集群机制,如低能适应集群层次 (LEACH),在优化能源消耗和数据传输方面存在局限性.
研究的目的:
- 为WSN提出一种新,节能,智能化的集群机制.
- 通过优化集群头选择和路由来提高网络寿命和数据传输量.
主要方法:
- 使用哈里斯·霍克斯优化 (HHO) 进行能量意识集群,考虑传感器距离,分散和能量平衡.
- 开发了一种基于强化学习 (RL) 的技术,用于集群头的选择,并与模糊逻辑系统集成.
- 使用野生马优化 (WHO) 建立基于最大能量和最小节点间距离的模糊规则.
主要成果:
- 与LEACH相比,拟议的机制显示网络寿命增加了29%.
- 实现了传送到基站的数据量增加46%.
- 多个网络的模拟验证了拟议方法的有效性和稳定性.
结论:
- 新的集群机制在长寿和数据吞吐量方面显著提高了WSN的性能.
- 整合HHO,RL,模糊逻辑和WHO提供了一个智能WSN管理的强大策略.
- 这种方法为提高无线传感器网络的效率提供了可行的解决方案.
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