Unveiling Trends and Trajectories: An In-Depth Bibliometric Study of Unequal Clustering in Wireless Sensor Networks

Abstract

In the Wireless Sensor Networks (WSNs) environment, the optimization of the unequal clustering algorithm has been identified as an important factor affecting the efficiency and sustainability of the network. This research aims to analyse the development and impact of the unequal clustering algorithm through bibliometric analysis. Using bibliometric tools, this study catalogues and analyses data from high-impact journals in the Scopus database, tracing development trajectories from the last decade and assessing the influence of unequal clustering. The analysis shows an increasing trend in unequal clustering publications, with significant contributions from prominent researchers whose works receive many citations, indicating their strong influence in the field. Major journals such as "IEEE Transactions on Mobile Computing" and "Sensors" stand out as centres of innovation and knowledge publications in unequal clustering. Findings emphasize the tendency to integrate metaheuristic techniques and machine learning in developing unequal clustering. This hints at a future direction where generative AI can play an important role in WSN research, with the potential to explore new unexplored areas and strengthen the effectiveness of WSNs.