Optimizing Wireless Sensor Networks by Identifying Key Nodes Using Centrality Measures

This study underscores the critical role of graph theory in optimizing the functionality of Wireless Sensor Networks (WSNs). Our research aims to enhance network efficiency by utilizing a variety of centrality metrics, including degree, betweenness, closeness, eigenvector, Katz, PageRank, subgraph, harmonic, and percolation centrality, to identify pivotal nodes. Employing an extended Barabási-Albert model graph of a 50-node network, our methodology focuses on pinpointing nodes crucial for optimal data processing, monitoring, and analysis in WSNs. This comprehensive approach deepens our understanding of sensor networks and significantly boosts operational efficiency by leveraging strategic node functionalities. The findings from our study are poised to revolutionize network management strategies, promoting the development of more robust and efficient WSN operations.