# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 1 Introduction  
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]. Consequently, the communication revolution embraces various standard wireless networks. Numerous types of wireless networks exist, and mobile wireless communication is one of the most widespread because it offers high data bandwidth. Mobile wireless communication utilizes a transport layer to transfer data and launches a specific protocol for data transmission. The transport layer protocol uses a congestion control mechanism to ensure good network resource allocation, and the network operates optimally even when demands reach the maximum network resources and size. 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, then the performance of the wireless network will deteriorate. Congestion control shortage is one of the main causes of the decrease in the lifetime and energy of wireless sensor network (WSN) nodes [4; 5]. This decrease leads to many other issues, such as delay, packet loss, and bandwidth degradation [6]. Network resources include buffer size, queue size, throughput, link bandwidth, and packet loss of intercommunication between nodes [7]. Congestion control influences diverse applications, such as event-based, continuous sensing, query-driven, and hybrid applications [8]. Congestion control is applied in different protocols in the transport layer, such as the transmission control protocol (TCP) and stream control transmission protocol (SCTP) [9]. Many conditions, such as packet collisions, interference, channel contention, and buffer overflow, require congestion control [4], whereas slow links, buffer size shortage, and slow processors do not need congestion control [7]. Many attempts have been made to enhance--while increasing the pace of--congestion control mechanisms, such as the proposal of a scalable distributed cluster-based congestion control scheme. In this scheme, the nodes are grouped in many clusters, where each cluster estimates the optimal path through message exchange within the cluster. However, this process may result in extra overhead along with energy consumption for head nodes within clusters [10]. An optimal model for network load level was proposed based on 3-bit representation; the model produced good convergence results and effective bandwidth allocation [11]. Measures have also been proposed to address issues in resource allocation, bandwidth delay, queue size, and high throughput [12], and a set of parameters based on channel load has been developed for distributed congestion control [13]. A previous study proposed a new algorithm to maximize the utilization of a link in data networking as well as overcoming data loss when congestion occurs [14; 15]. A machine learning approach was used to improve congestion control, and the approach produced a promising result for the practical and oracular measurements of path properties [16; 17]. Another study developed a novel method (expert framework) to estimate the end-to-end round-trip time using a machine learning approach. The simulation of this expert framework based on machine learning could adapt to changes in round-trip time, thereby reducing the number of transmitted packets while increasing the throughput [18]. As a part of machine learning, clustering was used in congestion control for a vehicular ad-hoc network that included three parts, namely, detecting congestion, clustering messages, and controlling data congestion. Machine learning plays a vital role in improving congestion control [19; 20]. The 5G environment is applied in many fields and utilized for many purposes, such as smart cities [21], e-Health [22], and environmental surveillance [23].