# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 5 Conclusions  
The congestion control problem in all network generations faced by many models necessitates the improvement of network performance through enhanced mechanisms or combinatorial algorithms. This study addressed the congestion control problem in 5G IoT environments by proposing a new model constructed based on a machine learning DT approach to predict the optimal node in 5G IoT environments. The proposed approach can help to improve congestion control and enhance overall network performance. The optimal congestion window management can be determined based on specific conditions, such as high throughput, high queue size, high congestion window, and low packet loss. A table of node readings was adopted to determine the optimal parameter values, and the resulting table was used for the machine learning model to make future predictions of optimal nodes.  
The C4.5 DT approach showed superior performance to other ML algorithms; it presents a tree-based graph that helps in finding the optimal alternative and the best path for congestion control. The resulting decision graph is an effective method to make a decision due to its clear presentation of all cases and highly scalable route selection. Many factors, such as low packet loss, high throughput, high queue size, and high congestion window, can affect the determination of optimal nodes in a network. The size of the training dataset can increase the accuracy rate of the results. The larger the training dataset size is, the more accurate the results are. The (C4.5) DT algorithm proved its effectiveness  
Figure 11: ORG-SCTP versus IMP-SCTP.  
in prediction and classification based on this model. The DT graph can provide a simplified plan to visually determine optimal and non-optimal nodes based on assigned values. Compared with other models that adopt the machine learning approach for improving the performance of congestion control, the proposed model presented promising results, with an accuracy of 92% in terms of predicting the optimal node. The results prove the model's efficiency and accuracy.  
In future work, we will compare three machine learning techniques in congestion control in 5G IoT environments for optimization and enhancement. Further, we will attempt to implement the optimal machine learning prediction techniques on hardware and test it in a real test-bed environment. Machine learning and 5G environments are promising research trends related to the development of prediction and optimization network protocol layers. Other aspects may also be considered for autonomous systems in optimal network utilization improvement, where integration will present a multi-objective domain and obtain multidisciplinary domains like AI and human control and deliver the best network performance.  
Conceptualization, I.A.N.; methodology, I.A.N.; software, A.K.H.; validation, I.B.; formal analysis, A.K.H.; investigation, I.A.N.; resources, J.L.; data curation, I.B.; writing--original draft preparation, I.A.N.; A.K.H.; writing--review and editing, I.A.N.; A.K.H., J.L.; visualization, I.B.; supervision, J.L.  
This research received no external funding.  
The authors would like to thank the anonymous reference and reviewers for their helpful comments that have significantly improved the quality of the presentation.  
The authors declare no conflict of interest.