IoT Pro-Active SDN: A Smart Predictive-Based Approach for IoT Devices in SDN

The immense and diverse nature of data created by the rapid growth of Internet of Things (IoT) devices poses substantial issues in network management. Conventional static frameworks find it difficult to adjust to the dynamic surroundings that these devices provide. This study presents an intelligent framework that combines time-series analysis to anticipate device behaviour, a Software-Defined Network (SDN) to enable adaptive network setup, and machine learning (ML) and deep learning (DL) algorithms for categorizing IoT devices. We constructed and shortlisted several machine learning (ML) methods, such as Decision Trees, Random Forests, and Long Short-Term Memory (LSTM) networks, utilising a publicly accessible dataset of eighteen Internet of Things (IoT) devices. With the help of real-time data insights, the suggested system might dynamically modify settings to improve network performance.