# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 1 Introduction
Delays are one of the most critical measures for IoT, especially in network traffic prediction and healthcare monitoring, where urgent situations need to be managed. In IoT applications, intelligent processing and big data analytics are the main drivers for development. Data science with the IoT is mainly used in various fields where volume, speed, and pattern recognition are concerned. Due to predictive ML analysis, the software can predict both desired and undesired incoming events. Therefore, the ML system detects abnormal behavior and helps to understand and establish long-term trends, which requires continuous correction and monitoring to achieve effectiveness and efficiency in data analysis [9; 10]. There is an apparent convergence between IoT and AI. IoT can connect objects and devices to use the data generated by these devices, while AI can model the intelligent behavior of all types of devices. Since IoT systems generate substantial data sets, AI helps process these datasets and understand the information. However, traditional methods for structured data, analytics, and definition processes cannot effectively manage the massive data flowing in real time from IoT devices. There is a high degree of convergence between IoT and AI. IoT can connect the devices, the data generated by those devices, and AI to model the intelligent behavior of all types of devices. Since IoT devices will produce an enormous amount of information, AI will process and understand the information. However, with traditional approaches to structuring information, analytics, and specific processes, you will not efficiently manage all the information about the entities that make up the IoT in real time. AI-based analysis and response can be used to extract the optimal value of the information, if any. The proliferation of ML and IoT algorithms can significantly improve the application infrastructure. Using ML can improve the management of the network to avoid congestion, optimize the allocation of resources, and examine the essential information to adopt decisions or offloading. ML methods, including artificial neural network (ANN)-based approaches, can effectively store and process large amounts of data [1]. Network information prediction is among the best active domains that combine AI studies for information networks. Nowadays, it can be used in various applications and is attracting more attention from many researchers. Information prediction is an effective way to ensure the security, reliability, and connectivity of selected networks. Many network traffic prediction methods have been proposed and tested, including internet-based data extraction methods. Many interesting network prediction strategies have been developed to achieve powerful results [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. Figure 1 illustrates an example of IoT traffic data prediction using ML techniques. Predicting historical information refers to predicting the following values of the system from previous and current information. Typically, predefined computational modeling (or hybrid modeling) is used to achieve the expected data. A recurrent neural network (RNN) essentially has a memory that can predict time-dependent targets. RNNs can store the previously sensed state of the inputs to determine the future time step. Recently, many variations were introduced to adapt recurrent networks to a variety of domains. Nonlinear autoregressive with external input (NARX) is a powerful RNN used to solve the problem with nonlinear historical information. The widely used NARX model provides promising results for time series problems based on lagged input and output variables and prediction errors, as discussed in these studies. Unlike the conventional RNN, the NARX network provides optimal prediction performance for almost any nonlinear function with little or no computational losses. The NARX model [8; 9; 10; 20; 22; 23] has been used for various applications in previous studies.