Data Clustering and Routing using Improved Water Wave Optimization in Wireless Sensor Network

Wireless Sensor Networks (WSNs) are pivotal in various applications where data collection from distributed sensors is essential. However, optimizing data collection efficiency while preserving energy resources remains a challenge. A data clustering model proposed in this paper implements the concept of Water Wave Optimization (WWO) methodology. The purpose of using Water Wave Optimization in this paper is to improve the accuracy of data grouping. We have suggested a grouping model using WWO, the recommended method aims to improve the accuracy of data clustering. We have demonstrated the validity of the proposed idea through the Friedman test and statistical analysis. Experiments revealed that the suggested grouping model works, as evidenced by its higher accuracy rate. In the past decade, routing in Wireless Sensor Networks has emerged as the focus of most research. Implementing a routing model that can determine the most beneficial path depending on an assortment of performance parameters is an extensive task. Then it has been demonstrated that after being compared to the currently employed clustering approach, the accuracy and F-score rates of the proposed WWO techniques improved by an average of 4% and 7%, respectively.