K-Means clustering of optimized wireless network sensor using genetic algorithm
K-Means clustering of optimized wireless network sensor using genetic algorithm
复制标题
使用遗传算法优化无线网络传感器的 K 均值聚类
DOI：
10.21533/pen.v10i3.3059
复制
发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Mohammed Sahib Mahdi Altaei
中科院分区：
文献类型：
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作者：
Azhar M. Kadim;Farah Saad Al;Najwan Abed Hasan;Aseel B. Alnajjar;Mohammed Sahib Mahdi Altaei
无线传感器网络是一个主要的技术趋势，用于收集，处理和分发大量的数据在几个不同的应用。它成为许多与感知周围环境相关的应用中必不可少的核心技术。在本文中，一个二维的无线传感器网络计划，用于获得各种无线传感器网络模型，旨在通过遗传算法优化，以实现优化的无线传感器网络模型。这种优化的无线传感器网络模型可能包含两个彼此靠近的簇头，其中它们之间的距离包括在感知范围内，这表明存在冗余数量的簇头。通过重新应用在WSN模型中发现的所有传感器的聚类，超过了这个问题。该算法利用距离测度检测待处理问题，利用K-均值聚类将传感器重新分布在备选簇首周围。结果是非常令人鼓舞的，在重新安排的传感器在检测区域中的分散与保守的方法的适度数量的簇头，承认附近的所有传感器的关联。
Wireless sensor network is one of the main technology trends that used in several different applications for collecting, processing, and distributing a vast range of data. It becomes an essential core technology for many applications related to sense surrounding environment. In this paper, a two-dimensional WSN scheme was utilized for obtaining various WSN models that intended to be optimized by genetic algorithm for achieving optimized WSN models. Such optimized WSN models might contain two cluster heads that are close to each other, in which the distance between them included in the sensing range, and this demonstrates the presence of a redundant number of cluster heads. This problem exceeded by reapplying the clustering of all sensors found in the WSN model. The distance measure was used to detect handled problem, while K-means clustering was used to redistributing sensors around the alternative cluster head. The result was extremely encouraging in rearranging the dispersion of sensors in the detecting region with a conservative method of modest number of cluster heads that acknowledge the association for all sensors nearby.