Alpha Node: Smart Data Retrieval System Using Network Simulator (NS)-3

Today, networking is the core of modern communication and connectivity. The reality is that it is the underlying framework for data and resource exchanges across various types of computing devices. Alpha node (cluster head) selection, a crucial factor in optimizing networks of groups comprising nodes that can work cooperatively in a localized environment, takes the highest place in the clustered networks hierarchy. The behavior and competence of the alpha node, the most central and leading node of any cluster, profoundly impacts the network’s capacity to coordinate its activities and allocate resources effectively. This study investigates the integration of machine learning methods into the ns-3 simulation framework, i.e., a discrete event simulator with many complicated features. This paper aims to provide the theoretical basis and solutions for managing clustered networks, particularly tree networks. The research will concentrate on combining machine learning models and simulations employing a machine learning model that crystallizes all methods of alpha nodes with a particular eye on the efficiency and quality of alpha node selection. This is aimed at adding significant flow into the network optimization practice.