Enhancing Subcluster Identification in IoT Sensor Networks with Hierarchical Clustering Algorithms and Dendrograms

IoT sensor networks (ISNs) have gained significant attention due to their broad applicability in various fields. One crucial aspect in ISNs is the efficient utilization of network resources, including energy, latency, and scalability. In this paper, we propose a hierarchical clustering-based approach to improve energy efficiency, latency reduction, and scalability in ISNs. By employing complete-linkage hierarchical clustering, the network is divided into clusters, and dendrograms are utilized to further partition clusters into subclusters. The objective is to optimize network performance with respect to energy efficiency, latency, and scalability. Extensive simulations and performance evaluations are conducted to assess the effectiveness of the approach. The results demonstrate that the hierarchical clustering approach offers improved energy efficiency, reduced latency, and enhanced scalability in ISNs, making it a promising solution for resource optimization in these networks.