Integration Intelligence in 5G AIoT: Opportunities and Challenges

Internet of Things (IoT) and artificial intelligence (AI) are two technologies that are evolving rapidly and play a vital role in a smarter future. It is significant that these two technological concepts absolutely complement each other. The data collected by IoT devices and sensors can now be efficiently analyzed and contextualized using AI technologies. Of course, in reality, there are still many challenges. The complexity level of AI has revolutionized intensely in the past 10 years or so. A successful AIoT environment requires standardization that encompasses the interoperability, compatibility, reliability, and efficiency of new devices that can be self-configuring and self-adapting. Intelligent connectivity (ICon) concept anticipates the combination of artificial intelligence and the new 5th generation (5G) of IoT networks, to accelerate technological development and structural changes in industry and society. As technology continues to grow, there are both opportunities and challenges that are associated with it. In the first part of the chapter, we describe the process of 5G standardization and system architecture, which is specifically engineered as an infrastructure of IoT and artificial intelligence. Mobile network systems have evolved from communication infrastructure to critical and necessary social infrastructures. 5G increases the capacity of mobile cells and enables the connection of a huge number of IoT devices. Increased data transfer speeds, reduced end-to-end latency, improved coverage, as well as network slicing (NS) and mobile edge computing (MEC) enable even the most demanding and sophisticated industrial IoT (IIoT) applications. Ultra-fast and extremely low-latency of 5G networks, combined with a huge amount of data collected by the IoT, as well as the possibilities of contextualization and decision-making using AI technologies, accelerate technological development and structural changes in industry, transport and public services. In the second part of the chapter, we pointed out that big data (BD) analytics, combined with the underlying AI technologies, have found their applications in all aspects of intelligent connectivity (ICon). IoT generates data from the connected object and it is used for further analysis and decision making. However, the widespread deployment produces data both in quantity and category, thus providing the opportunity for the application and development of big data. On the other hand, the incorporation of big data analytics to 5G AIoT simultaneously accelerates the research advances and deployment models. Digital twin (DT) model is a perfect example of the merging of big data, AI and IoT technologies. A digital twin is a virtual representation that serves as the real-time digital counterpart of a physical object or process. Continuous upgrading and expansion of digital twin technology requirements have led to new demands and trends. It is becoming more evident that digital twin runs in parallel with AI and IoT technology resulting in shared challenges