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在使用K-最近邻近算法的异质无线传感器网络协议中提高集群效率

Abdulla Juwaied¹, Lidia Jackowska-Strumillo¹, Artur Sierszeń¹

¹Institute of Applied Computer Science, Lodz University of Technology, ul. Stefanowskiego 18, 90-537 Lodz, Poland.

Sensors (Basel, Switzerland)

|February 26, 2025

概括

这项研究引入了一种新的K-Nearest Neighbours (KNN) 算法,以优化无线传感器网络 (WSN) 的集群. 这种方法提高了能源效率,减少了连接距离,并延长了网络寿命,以提高性能.

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:

无线传感器网络 (WSN) 对于数据收集至关重要,但在能源消耗和网络寿命方面面临挑战.
有效的集群协议对于安全连接和WSN中稳定的网络寿命至关重要.
现有的协议如LEACH,SEP,TEEN和DEC在能源效率和连接优化方面存在局限性.

研究的目的:

引入一种新的K-Nearest Neighbours (KNN) 算法,用于优化WSN中节点选择和集群.
提高能源效率,减少网络连接长度,延长异质WSNs的运行寿命.
评估KNN算法在增强四个已建立的WSN协议中的有效性:LEACH,SEP,TEEN和DEC.

主要方法:

实施K-最接近邻居 (KNN) 算法,以优化WSN协议中的集群机制.
使用拟议的KNN方法修改和模拟四个不同的WSN协议 (LEACH,SEP,TEEN,DEC).
通过专注于能源消耗,连接距离和网络寿命的 MATLAB 模拟进行性能评估.

主要成果:

优化KNN的协议显示集群头和传感器节点之间的距离更短.
在所有修改后的协议中观察到总体能源消耗的显著减少.
提出的基于KNN的方法导致了整体网络寿命的显著增加.
通过优化集群实现了提高网络运营效率和安全性.

结论:

K-最接近邻居 (KNN) 算法为无线传感器网络的能源管理提供了强大而有效的解决方案.
使用KNN优化节点选择和集群可显著改善WSN中的关键性能指标.
拟议的方法为异质WSN提供了有价值的增强,延长了它们的实际适用性和寿命.

关键词:

十月十日十月十日十月十日在 KNN KNN 标签上.李奇 (Leach) 公司在SEP中,SEP是SEP.十几岁的青少年青少年集群头的位置集群头的位置聚类集群是指聚类的聚类.能源消耗能源消耗能源消耗传感器传感器传感器

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