Application Based on Open Flow Using Deep Radial Basis to Enhance Mobile IP Networks

In the current age of widespread data connectivity and mobile computing, the effectiveness and caliber of mobile IP networks hold significant significance. The primary objective of this study is to investigate the integration of OpenFlow, a dynamic network control protocol, with deep RBNs, a variety of neural networks that have the ability to understand intricate patterns and make well-informed judgments. The integration of OpenFlow-based network control and deep RBNs enhances the capability of mobile IP networks to dynamically respond to evolving circumstances. This study presents a novel methodology that utilizes OpenFlow technology in combination with deep radial basis networks (RBNs) to augment the efficiency and dependability of mobile IP networks. The conducted experiments and evaluations provide compelling evidence of significant enhancements in crucial network metrics, including the decrease of latency, the optimization of routing efficiency, and the improvement of quality of service (QoS). The utilization of deep RBNs inside the OpenFlow framework facilitates the implementation of data-centric decision-making processes, hence guaranteeing the efficient allocation of network resources to effectively cater to the requirements of contemporary mobile apps and services.