Advanced Clustering Metric for Mobility Model Using DBSCAN

Current research aims to gain an in-depth understanding of mobility characteristics in order to accurately assess each mobility defect and maintain network performance. Therefore, this article investigates the use of the Density-Based Spatial Clustering of Applications with Noise(DBSCAN) algorithm to examine the performance of a cluster within a mobility model. The six mobility models: Manhattan Grid Mobility Model(MGMM), Reference Point Group Mobility(RPGM), Nomadic Community Mobility Model (NCMM), PURSUE, Self-Similar Least Action Walk Mobility Model (SLAW), and SMOOTH, are used to evaluate the effectiveness of clustering in capturing the spatial structure of mobile models, including their spatio-temporal locations. The algorithm measures various factors, such as the number of clusters, points in each cluster, and cluster indices. We continue the study that validates synthetic mobility models, focusing on a metric known as ”Mobile Neighbors Range”. This metric allows a detailed analysis of the interactions between moving neighbors over time. In this article, a new index called ”Global Index” is introduced, based on the variation of clustering, to evaluate the movement of nodes in synthetic mobility models over time using artificial intelligence techniques, namely DBSCAN clustering, which integrates the concept of clusters. The goal is to analyze the dispersion and clustering of nodes over time in each mobility model to gain a more complete understanding of their behavior. This in-depth investigation, marked by 1,200,000 iterations, highlights the scale and precision of our efforts to delineate mobility models, thereby significantly enriching the scientific discourse on mobile networks and systems. The need for this exploration wasapparent, filling a gap in current scientific understanding