Machine Learning Approaches for Subcluster in IoT Sensor Networks with Hierarchical Clustering and Dendrograms
Abstract
This research focuses on optimizing IoT Sensor Networks (ISNs) by implementing hierarchical clustering algorithms. Traditional clustering methods often lead to imbalanced energy consumption, impacting network lifetime and performance. Our approach leverages hierarchical clustering to partition the network into a set of clusters. Each cluster has a cluster head and a set of sensor nodes. To enhance data aggregation and energy efficiency, we introduce subclustering within clusters using dendrograms. We assessed performance metrics using simulation, including energy consumption and scalability. The proposed hierarchical clustering methodology significantly improves network lifetime, energy efficiency, and data aggregation.
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References
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