# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 1 Introduction
There are several uses for the NARX network, which can be used for predicting future information. It can be used for nonlinear cleanup operations, where the desired result is noise-free input information. The use of the NARX network brings several essential features, namely, the representation of dynamic systems. ANN contains a feedback and delay mechanism that allows information to flow between the neurons of the different layers. Feedback is a way of storing a temporary memory that allows the network to retrieve old data. While delay provides direct sets of past data for the current period, response methods perform processing (filtering) of past data [8; 9; 10]. Accurate network traffic prediction enables efficient resource management that ensures appropriate QoS measures. Traffic prediction can be used for various applications, such as network monitoring, resource management, congestion control, network bandwidth allocation, network intrusion detection (e.g., anomaly detection), etc. With the evaluation of 5G networks, the traffic volume and network complexity have increased. Many network traffic prediction algorithms have been proposed, such as autoregressive integrated moving average (ARIMA), \(k\)-nearest neighbors (KNN), random forest, support vector machine (SVM), linear regression, etc. However, when we have a large amount of contaminated data, these methods do not seem  
Figure 1: Machine learning for IoT traffic prediction. to work [24; 25]. Therefore, we use a k-step prediction approach with NARX-RNN for accurate network traffic prediction in this work. The motivations behind this study include the following:  
1. Optimize the QoS requirements and network monitoring to manage resources and ensure security. 2. The NARX-RNN technique predicts time series data, remembers the historical data, and accurately estimates future time series data. It also has the advantage over other time series prediction approaches in that it serves to maximize the accuracy of the learning method over the training iterations. As more data are added to the model, the model becomes smarter and can better predict network traffic, which is significant for actual traffic information forecasting. 3. There is insufficient knowledge about the delays in IoT communication parameters. 4. There is a lack of accurate ML analysis to achieve reasonable prediction accuracy. 5. The challenging problems in optimizing QoS measures are computationally complex. 6. Monitor network availability and activity to detect security and operational issues. Due to the limitations of the available solutions, this work focuses on forecasting delays in IoT and tactile internet networks. Several application domains are delay aware and necessary (e.g., healthcare, security, and medical emergency). In these applications, e.g., intensive remote patient monitoring, a momentous case must be revealed to a healthcare agency within a specific period to determine appropriate steps. Moreover, this might lead to different amounts of information relying on the number and type of observations performed. To perform predictive modeling of delay, we use an ML-based technique, such as the NARX-RNN model with a \(k\)-step prediction approach. The key contributions of the proposed work are summarized as follows:  
* We proposed a \(k\)-step-ahead time series prediction approach with a NARX-enabled RNN for delay prediction in IoT and tactile internet. * The ML model was trained using four ML algorithms: Bayesian regularization backpropagation (Trainbr), Levenberg-Marquardt backpropagation (Trainlm), conjugate gradient backpropagation with Fletcher-Reeves updates (Traincf), and resilient backpropagation algorithm (Trainrp). * The prediction accuracy was measured using three ML-based functions: mean square error (MSE) loss function, root mean square error (RMSE), and mean absolute percentage error (MAPE). * The above training algorithms were compared depending on the proposed time series prediction approach using RMSE and MAPE.