Maximizing Lifetime of the Network with ML Driven Cluster Head Selection in WSN
Keywords:
Cluster Head Selection, Machine Learning, Wireless Sensor Networks, Data Aggregation, Communication, Dynamic Network, Resource Constraint.Abstract
Cluster head selection is a crucial task in wireless sensor networks (WSNs) for efficient data aggregation and communication. Traditional methods often rely on predefined parameters or heuristics, which may not adapt well to dynamic network conditions. In this study, we propose a novel approach for cluster head selection using machine learning techniques. By leveraging the power of machine learning algorithms, our method aims to dynamically select cluster heads based on various network parameters and environmental factors. We present experimental results demonstrating the effectiveness and efficiency of our approach compared to traditional methods. Our findings suggest that machine learning-based cluster head selection can significantly improve the performance and scalability of WSNs, particularly in dynamic and resource-constrained environments.
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