# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 2 Literature Review  
Recently, several researchers have focused on the time series prediction of wireless network traffic using ML methods and in 5G technology. This paper aims to predict a delay in IoT and tactile internet traffic using the ML approach, especially the k-step ahead prediction approach using NARX-enabled RNN. Therefore, in this section, we introduce the previous works that relate to our field of study.  
The author of [9] presented a method for time series prediction for IoT data streams with multi-step prediction (MSP), using NARX-RNN. He calculated the prediction performance using different loss functions, such as MSE, sum squared error (SSE), Mean absolute error (MAE), and MAPE. IoT delay prediction was also performed using a deep neural network (DNN): a multiparameter method in [13]. In [8], traffic prediction was performed using a NARX-enabled RNN based on a software-defined networking (SDN) infrastructure. The ANN training algorithms were trained using three ANN training algorithms: Trainlm, Traincgf, and Traincgp. The prediction accuracy, the MAPE, was measured. ML-based low latency bandwidth prediction was performed for H2M networks [14].  
[PERSON] et al. [10] performed IoT delay prediction using the ML method based on the NARX recurrent neural network. The author proposed two methods for time series prediction: MSP and single-step prediction (SSP). The model ANN was trained with three ANN training algorithms: Traincgf, Trainlm, and Trainrp. The prediction performance was measured using RMSE and MAPE. The packet transmission delay prediction in an ad hoc network was analyzed using ANN [15]. [PERSON] et al. [16] studied QoS prediction in IoT systems regarding transmission time and traffic capacity. In [17], the short-term prediction was discussed using different ANN methods. Research directions for further applications of ANN-based techniques were also proposed. The evaluation of a graph structure DL was studied for network traffic flow prediction with superior performance [18]. In research [19], the multilayer neural network (MLNN) was proposed for speed prediction by integrating the convolutional neural network (CNN) and gated recurrent units (GRU) to estimate the predicted speed performance with the network performance.  
[PERSON] et al. [22] studied the performance of time series modeling with the NARX network for network traffic prediction. [PERSON] et al. [26] studied the problem of predicting IoT traffic using ML, DL, and statistical time series-based prediction technologies, including long short-term memory (LSTM), ARIMA, Vector autoregressive moving-average (VARMA), and feedforward neural networks (FFN). [PERSON] et al. [27] predicted subway traffic based on ARIMA and the gray prediction model, but this model has the drawback of being unable to capture rapid changes in traffic data. [PERSON] et al. [28] gave an overview of techniques for traffic forecasting and examined the limitations of each technique. Additionally, they provided an overview of the different types of raw traffic data. Finally, they described a hybrid approach for traffic forecasting. The hybrid system is based on ARIMA and SVM techniques.