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在WSN中基于模糊逻辑和Q学习的不平等集群和多节点路由协议
1School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.
Entropy (Basel, Switzerland)
|February 26, 2025
概括
本研究介绍了FQ-UCR,这是一种用于无线传感器网络 (WSN) 的混合方法,它使用模糊逻辑和Q学习来延长网络寿命并解决热点问题. FQ-UCR提高了能源效率和网络寿命.
科学领域:
- 计算机科学 计算机科学
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 无线传感器网络 (WSN) 依赖于集群,以延长运行寿命.
- 现有的集群方法往往无法解决多节点WSN中的热点问题,导致过早的网络故障.
- 解决能源消耗和网络寿命问题是WSN研究中的关键挑战.
研究的目的:
- 介绍FQ-UCR,这是一种用于无线传感器网络的新型混合方法.
- 通过解决热点问题来提高网络寿命和能源效率.
- 改进多跳 WSNs 中的数据传输路由.
主要方法:
- 一种混合方法,将不平等的集群与模糊逻辑 (FL) 和Q学习结合在一起.
- 使用模糊推理系统 (FIS) 进行集群头 (CH) 选择概率.
- 采用Q-learning来优化对数据传输路径的转发CH的选择.
主要成果:
- 在所有网络节点上,FQ-UCR显示了提高能源效率.
- 与现有协议相比,整体网络寿命的显著延长.
- 有效地缓解热点问题,这是WSN中常见的挑战.
结论:
- FQ-UCR方法成功地提高了WSN中的能源效率和网络寿命.
- FQ-UCR为多节点WSN中的热点问题提供了有效的解决方案.
- 混合模糊逻辑和Q学习策略为WSN路由优化提供了一个有希望的方向.
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