# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 2 Related Work
The proposed model is a combination of DTs (random forest and oblique random forest) with an efficient neural network (random vector functional link network). The result of model evaluation on 65 multi-class datasets was better than those on medium- and large-sized datasets. The model experiment on the multi-class datasets produced good results, and the model could perform similarly on large- and medium-sized datasets. The model considers the problem of multi-class classification and provides the same training datasets for two-class datasets. The result of the proposed ensemble of classifiers was better than those of other classifiers. Furthermore, [PERSON] et al. [36] compared the performance of various ensemble algorithms based on DT and presented a new classifier. The computational capacity of small network devices was not a crucial limitation. [PERSON] et al. [37] conducted a review of a machine-to-machine communication technology related to the system model architecture anticipated by diverse standards-developing organizationsand prospective machine-to-machine communication challenges and their intended state-of-the-art solutions. [PERSON] et al. [38] proposed a new system for solving the flow table congestion problem in a software-defined network (SDN) based on a DT algorithm (C4.5). C4.5 was used to compress the flow entries in the data flow table in order to reduce the flow matching cost and time. This machine-learning-based online process in the SDN that is the first model to utilize a DT with a flow table. Then, a new trend of localization based on adaptive cluster splitting and AP reselection was proposed; [PERSON] et al. [39] presented a new fingerprint-based indoor wireless LAN that adopts adaptive cluster splitting and access point reflection. However, only received signal strength (RSS) RSS was considered for fingerprinting, a large area of wireless technologies was not applied, and many other factors were not considered (e.g., packet loss, information of channel state, queue size, throughput). Following this trend, research published by [PERSON] and [PERSON] [40] used the machine learning approach (random forest algorithm) to predict traffic congestion. Variables such as weather conditions, road quality, time period, and type of day were used to build the model. However, many other factors, such as time and type of day (holiday), influence traffic congestion, make the prediction easy, and eliminate the need for prediction. Following the trend of models based on Bayesian neural networks, [PERSON] et al. [41] created a model based on a Bayesian neural network with gradient-boosted DTs to predict secondary incident occurrences. The model aims to reduce potential accidents by alerting drivers about events that cause accidents. However, similar to many other models, this model cannot be applied in real-time systems that predict secondary incidents because the model needs real parameters to train and build the prediction results. Following the trend of implementing methods for link quality estimation based on DT and support vector machine (SVM) to propose an improved route of data delivery over WSNs, [PERSON] et al. [42] utilized two parameters for estimation--namely, receive signal strength and link quality indicator. DT was combined with SVM in this model because SVM can only handle problems with binary classification. For energy-saving solutions for data centers and network infrastructures, a solution that is not a base technique was proposed in [43]. Following the trend of using DTs, [PERSON] and [PERSON] [44] presented a scheme using DT cognitive routing to determine the optimal path for sending data over a mesh cognitive radio network. The model allows a node to select the best candidate after analyzing the result tree and eliminate choices that decrease the node gain. Mesh cognitive networks and cognitive routing fields require analysis and intensive research to determine the critical points.