Sustainable Wireless Sensor Networks: Energy-Efficient Clustering Protocols for Extended Operation

Because of their widespread use in contexts with constrained resources, wireless sensor networks' (WSNs) ability to conserve energy is of the utmost importance. Assigning cluster heads to each group of sensor nodes and classifying them as a cluster is the standard method for clustering. But it’s not easy to get the network performance and energy usage just right. Several hierarchical methods, several levels of clustering, and dynamic clustering models are covered in this study as energy-efficient clustering protocols. We evaluate these protocols' impact on network lifetime, energy consumption, and data transfer reliability using simulation results and performance metrics. Among the most recent developments in the field of energy-efficient clustering include the use of machine learning for the purpose of predicting network behavior and adjusting cluster formation, and the development of intricate protocols that combine several energy-saving techniques. The research highlights the importance of enhancing and incorporating clustering techniques into WSN systems to meet their future demands and adapt to their dynamic nature. Improving the operational life of WSNs and building sustainable and optimal solutions are both made possible by energy-efficient clustering methods.