Evaluating Centrality-Based Key Node Identification and Performance Optimization in Barabási–Albert Wireless Sensor Networks

Suneela Kallakunta

doi:10.5269/bspm.79980

Suneela Kallakunta Department of Electrical, Electronics and Communication Engineering, GITAM (Deemed to be University), Visakhapatnam, Andhra Pradesh, India

Résumé

This analysis focuses on examining the relevance of the centrality measures within the context of wireless sensor networks (WSNs) using the Barabási-Albert Model with 100 and 150 nodes. The centrality measures included in this work comprise Degree centrality (DC), Betweenness centrality (BC), Closeness centrality (CC), Eigenvector centrality (EVC) and Katz centrality (KC). The analysis addresses these measures to evaluate the ways they improve the performance of WSNs through key node identification. The analysis highlights the centrality measures and their role in WSNs’ effectiveness, offering insights into such utilisation in routing and overall sensing reliability. Further, the complex interrelations among measures of centrality have been analysed through correlation techniques of different rigour, such as Pearson, Kendall, and Spearman. The focus on the individual metrics has provided an understanding of the centrality measures to explain further the net outcome of the performance of the network. This study addresses the interconnected developments of WSNs and offers interventional measures to enhance performance in different circumstances.

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