Real-Time Mobile Data Traffic and Noise Monitoring System for AI Data Prediction Using Open Source Frame Work

The predictive analysis of mobile network traffic is important for future generation cellular networks. Knowing user requests in advance enables the system to allocate resources in the best way possible. In this manuscript, Real-Time Mobile Data Traffic and Noise monitoring System for AI Data Prediction Using open Source Frame Work (RMTNMS-OSF) is proposed. Unlike previous studies that primarily remained theoretical, this research aims to identify areas with the highest demand for 5G internet service and also promptly provide the information to IT professionals. This is significant because of the high demand for internet services among tech professionals working from home in rural areas. This developed software now utilizes HTML, OpenLayers, and real-time spatial location data along with the Google Satellite Map API as its base layer to detect user locations as well as to ensure uninterrupted high-speed internet service. The innovation of this proposed RMTNMS-OSF model lies in the integration of AI-driven predictive models with real-time geospatial data processing to optimize network performance in rural areas by dynamically predicting network demand, detecting congestion, and preventing data loss using cost-effective open-source technology, and this mark up a significant advancement in mobile network traffic prediction and resource allocation. The performance of the proposed RMTNMS-OSF method is evaluated with existing methods.