# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 2 Related Work
[20], the authors emphasized and demonstrated the necessity of overviewing the scattering mechanisms on deep learning applications for several network traffic control features, the state-of-the-art deep learning architectures and algorithms related to network traffic control systems, and also the potential enablers of deep learning for network systems. However, this was without a perspective of 5G environment utilization. Furthermore, authors in [45; 55] proposed techniques for detecting previously unknown attacks on networks by identifying attack features. However, the relevance of window size and the attacks were not presented clearly. Most of the previous studies frequently used decision tree machine learning to improve the network status without considering congestion control mechanisms over the recent environments (specifically the 5G IoT environment), even though DT presents a tree-based graph for prediction and classification. Therefore, DT should be developed for the prediction of the optimal congestion control mechanism among current congestion control transport protocol mechanisms in the 5G IoT environment. Despite these proposed methods, most studies only implemented machine learning capabilities in classifiers to improve network status without considering the current mechanisms required to adapt to new environments, such as advanced 4G and 5G, which could improve throughput and reduce end-to-end delay. Thus, 5G and the Internet of Things (IoT) represent a new era of applications and infrastructures that require the use of current mechanisms and protocols. Adapting to new environments is challenging in 5G, IoT, and current transport protocol mechanisms because transported protocols comprise the core of networks, which have unique characteristics compared with other protocols in mobile communication. The SCTP was introduced as the next-generation transport protocol to facilitate traffic in a way superior to conventional TCP and User Datagram Protocol (UDP). However, standard SCTP, TCP, and other transport protocols must be modified to suit the 5G environment. For these reasons, rapid response and reduced latency should be considered when improving a congestion control mechanism within 5G IoT networks. A demonstration of new approaches to overcome the limitation of current mechanisms should be considered to score the evaluation metrics of prediction and select the optimal techniques for congestion control as a means of achieving improved WSN performance in adjusting transport protocols for the improved support of 5G IoT environments. The purpose of this work was to develop a new method for the prediction of congestion control mechanisms to adapt to 5G and evaluate the performance factors according to the machine learning capabilities for the SCTP. An analysis of the literature indicated several parameters for assessment/evaluation or utilization as evaluation metrics. However, in this research, we used the DT concept of machine learning capabilities during the development phase to predict the best I-cwnd for selecting the optimal threshold to adapt the SCTP to 5G IoT.