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基于无监督学习的WSN集群,以有效监测环境污染

Catherine Nayer Tadros¹, Nader Shehata^2,3,4,5, Bassem Mokhtar^1,6

¹Department of Electrical Engineering, Faculty of Engineering, Alexandria University, Alexandria 21544, Egypt.

Sensors (Basel, Switzerland)

|July 8, 2023

概括

这项研究增强了使用无线传感器网络 (WSN) 的水质监测,使用修改的LEACH和K-means算法. 该方法提高了网络寿命和环境污染检测效率.

科学领域:

环境科学环境科学
计算机科学计算机科学
材料科学材料科学材料科学

背景情况:

无线传感器网络 (WSN) 对于环境污染监测至关重要,特别是对于重要的水质评估.
WSN 设备的能源限制限制网络寿命和运营能力,需要节能解决方案.
轻量级的机器学习集成可以提高基于WSN的监控系统的准确性和有效性.

研究的目的:

开发用于水质监测的WSN节能集群算法.
为了改进决策,将修改后的低能适应性集群层次结构 (LEACH) 协议与K-means集群集成.
分析氧化纳米颗粒在通过光火方式检测过氧化污染物的性能.

主要方法:

开发了一种修改的基于LEACH的聚类算法,与K-means数据聚类相结合.
为基于K-means LEACH的集群算法在WSN中提出了一个数学模型,用于水质分析.
实验测量使用氧化纳米颗粒通过光火的光学检测过氧化.

主要成果:

拟议的K-means基于LEACH的集群算法在延长WSN网络寿命方面表现出有效性.
修改后的集群和路由方法在静态和动态监控环境中都被证明是有效的.
该研究验证了使用ceria NPs用于在水中敏感检测过氧化的有效性.

结论:

综合的K-means和LEACH方法为水质监测中的节能WSN提供了强大的解决方案.
这种方法提高了环境传感应用的网络寿命和数据分析能力.
该研究有助于可持续和准确的环境污染检测系统.

关键词:

这就是K-means算法.李亚赫 (Leach) 公司 WSN 集群的集群是什么?没有监督的学习学习.水质监测监测水质的水质.

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