An optimized clustering using hybrid meta‐heuristic approach for wireless sensor networks,International Journal of Communication Systems

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An optimized clustering using hybrid meta‐heuristic approach for wireless sensor networks
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-10-12 , DOI: 10.1002/dac.4638
Rajaram V ₁ , Kumaratharan N ₂

Affiliation

Power efficiency is one of the major attributes that has to be concentrated in wireless sensor network (WSN). Efficiency in the consumption of power is achieved by clustering, routing, and balancing the load in the network. The proposed work focuses on clustering to balance the load in the network, which in turn improves the power consumption by the sensor nodes. Clustering is one of the prominent techniques in WSN where research is still going on to improve efficiency. In the proposed work, the sensor nodes are collected together for the formation of multiple groups called as clusters. Cluster heads are selected using efficient satin bower bird optimization algorithm where the weight of the node is taken as a parameter. Among all these multiple cluster heads, the highly powered cluster heads are named as chief cluster head utilizing crow search optimization. In a multihop manner, the sensor nodes sense the collected data to the cluster heads, which in turn send the aggregated data to the chief cluster heads. All the selected chief cluster heads send the collected data to the central server node. Simulation is carried out in MATLAB R2020a and the performance of the proposed heuristic‐based clustering is compared with other clustering protocols in terms of energy efficiency, throughput, and delivery ratio, and it is verified that the proposed protocol gives better results.

中文翻译：

使用混合元启发式方法的无线传感器网络优化集群

功率效率是必须集中在无线传感器网络（WSN）中的主要属性之一。通过群集，路由和平衡网络中的负载来实现功耗的效率。拟议的工作集中在群集上，以平衡网络中的负载，从而改善了传感器节点的功耗。聚类是WSN中的一项突出技术，其研究仍在继续进行以提高效率。在提出的工作中，传感器节点被收集在一起以形成称为簇的多个组。使用高效的缎面凉亭鸟优化算法选择簇头，其中将节点的权重作为参数。在所有这些多个簇头中，利用乌鸦搜索优化功能将功能强大的簇头称为首席簇头。传感器节点以多跳方式将收集到的数据感测到簇头，簇头又将聚合后的数据发送到主要簇头。所有选定的主群集头均将收集的数据发送到中央服务器节点。在MATLAB R2020a中进行了仿真，并将所提出的基于启发式聚类的性能与其他聚类协议在能效，吞吐量和交付比率方面进行了比较，并验证了所提出的协议可提供更好的结果。所有选定的主群集头将收集的数据发送到中央服务器节点。在MATLAB R2020a中进行了仿真，并将所提出的基于启发式聚类的性能与其他聚类协议在能效，吞吐量和交付比率方面进行了比较，并验证了所提出的协议可提供更好的结果。所有选定的主群集头均将收集的数据发送到中央服务器节点。在MATLAB R2020a中进行了仿真，并将所提出的基于启发式聚类的性能与其他聚类协议在能效，吞吐量和交付比率方面进行了比较，并验证了所提出的协议可提供更好的结果。

更新日期：2020-11-09

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