Abstract
The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.
Highlights
Wireless sensor networks have played a crucial role in communication in the past few decades due to their unique features, which make them a major carrier of data across networks [1,2,3]
This study presents a new methodology for improving the congestion control mechanism based on the machine learning decision tree (DT) approach
The results showed that DT had higher accuracy and precision than support vector machine (SVM) and K-nearest neighbor and was the best choice for prediction
Summary
Wireless sensor networks have played a crucial role in communication in the past few decades due to their unique features (e.g., mobility and ease of connection), which make them a major carrier of data across networks [1,2,3]. Mobile wireless communication utilizes a transport layer to transfer data and launches a specific protocol for data transmission. One of the most critical events of the transport layer protocol is congestion control over wireless networks. If this event is used in a mobile wireless communication platform such as 5G, and does not satisfy the requirements of the platform, the performance of the wireless network will deteriorate
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