Review and Analysis of WSN with Cluster Routing Issues and Provide Solutions Through Artificial Intelligence and Machine Learning Algorithms

Wireless Sensor Networks (WSNs) technology has emerged extensively. Network clustering is the procedure to enhance energy efficiency. The conventional approaches resolve several issues. However, they may not develop a precise result for forecasting system behavior. In this article, we investigate WSN with Cluster routing issues and provide solutions through Artificial intelligence and Machine learning algorithms. This article describes WSN routing issues and functional issues. Also, it explains Artificial Intelligence and Machine Learning (ML) techniques to solve the data collection, aggregation, and distribution issues in WSN. The significant objective of cluster formation, Cluster Head (CH) selection, and distribution algorithms is to collect and forward the data in an energy-resourceful method, as a result improving the lifetime. The benefits of clustering in WSNs comprise better scalability, minimizing delay, and raising energy efficiency. This Artificial Intelligence and Machine Learning techniques applied in WSNs will be a lead for the research society in describing the typically adapted approaches for solving the issues interrelated to cluster with WSN routing.