# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 1 Introduction  
Artificial intelligence (AI) is emerging as a critical ingredient needed to understand multiple datasets collected and make them commercially valuable. AI can support data analysis from the internet of things (IoT), where the system can perform tasks or improve intelligent information. Moreover, AI in IoT devices can identify data, adopt decisions, and operate on that information without user intervention [1]. Machine learning (ML) is the method that deals with developing algorithms that can learn from information and make predictions. Moreover, modern central processing unit (CPU) technology enables an effective implementation of AI algorithms. However, the traffic volume is increasing, and the heterogeneity of traffic is increasing. IoT and ultra-reliable, low-latency communications bring a variety of demands that require greater efficiency in quality of service (QoS)-related decisions, as current QoS technologies cannot achieve the desired level. Most provisions are necessary to predict load and delay for specific facilities, considering geographic location and dynamics, such as subscriber movement and incredible speeds. Operators also need an overall system that allows for predictionsof infrastructure evaluation, considering the adoption rate of new technologies that will enable new online services and the associated expected changes in people's lifestyles [2]. AI has a crucial role in fifth-generation (5G) networks, such as the IoT and the tactile internet, because it can quickly extract insights from data. ML can automatically discover models and anomalies in information produced from sensors and smart devices. AI technologies extend ML strategies applied to smart IoT devices to make complex decisions based on recognition patterns, self-learning, self-healing, context awareness, and autonomous decision-making. These include and influence future applications of dual digital models and continuous learning that play a role in autonomous vehicle applications, IoT, and predictive maintenance [3; 4]. AI is widely applied in wireless network use cases, such as handling and recognizing information and data streams [5; 6]. It can also predict and process historical data series and geographical and positional information in real time, essential in almost all technology areas. It affects the applications, evaluation tools, computational level, data processing methods, and interface control capabilities in virtually all areas of information technology (IT). 5G will increase the speed and combination of other technologies, while AI will enable machines and systems to work intelligently like humans. In summary, 5G will accelerate services in the cloud while rapidly analyzing and learning from the same data. Wireless communication is a driving force in promoting economic, technical, social, security, and study conditions. It also supports the growth factor to meet the demands of the new generation. Researchers are constantly striving to develop new wireless communication technologies and mechanisms to facilitate human life, such as the advancement of IoT, 5G, transport networks, tactile internet, etc. Thanks to the advances in computing capabilities and ML techniques such as deep learning (DL), AI performs well in various applications, such as security, networking data processing, etc. Undoubtedly, AI reduces user interference and provides reliable results irrespective of the application domain. Therefore, the advancement of AI can also offer innovation and novelty to 5G networks to manage the network efficiently. With additional AI and automation layers that provide zero-touch and end-to-end, 5G will provide excellent revenue growth opportunities. Nowadays, many researchers are interested in integrating AI and wireless communication, which is reflected in the standardization schemes [7; 8]. The growth of IoT requires the installation of various applications with complex operational requirements.