Leveraging Machine Learning for Network Redundancy Optimization, Cost Reduction, and Latency Improvement in IoT Systems

This paper introduces a novel approach for optimizing Internet of Things (IoT) network performance by employing machine learning techniques to predict network types based on measurable properties of network traffic. While traditional studies have primarily focused on either reducing latency or enhancing network redundancy, our work integrates these aspects by dynamically predicting network conditions that facilitate the selection of optimal communication pathways. This methodology not only significantly reduces latency and improves redundancy but also enhances the overall efficiency and reliability of IoT networks. By analyzing a synthesized dataset reflective of real-world IoT environments, our model achieves high accuracy in network-type prediction, demonstrating its potential for real-time network management applications. Furthermore, the study delves into error analysis and feature sensitivity, providing insights into the reliability of network predictions and their implications for network design and operation. This paper aims to serve as a cornerstone for future explorations into optimizing IoT networks, setting the stage for more resilient, efficient, and responsive IoT ecosystems.