Industrial Internet of Things (IIoT). Группа авторов

identifying the material variables, the weaknesses to be improved, and the possibility of using augmented reality to make tests before actually putting it into production; or even through data analysis, it is possible to get an agile response to new market demands, considering that the needs and interests of consumers are changing with great velocity.

      AI brings great advantages to the industry related to the reduction of errors, because after being trained, intelligent algorithms are able to perform very well tasks that are susceptible to errors in processes executed by humans. The reduction of costs since e-commerce stores or banks use robots (chatbot) for customer service, this allows employees to be allocated in more strategic areas, which can increase profit. So, with fewer errors and employees focused on more important processes, the company will have more time to think about the business and leave other tasks to AI.

      Thus, AI through an automated process uses large volumes of data to make decisions, dispensing with human intervention and increasing productivity in different activities.

1.5 Trends

      Adaptive Intelligence is about helping to generate better business decisions by integrating the computational power of internal and external data in real time with the computing infrastructure and highly scalable decision science. In this type of systems, relating the adaptive learning, the characteristics are monitored so that there is an adjustment in order to improve the process. The efficiency of these systems depends on methodologies adopted to collect and diagnose information related to needs and characteristics, in relation to how this information is processed to develop an adaptive context. These applications essentially make businesses smarter, allowing them to provide customers with better products, recommendations, and digital services, all of which generating better business results [55].

      Digital twins are related to the practice of creating a computer model of an object, such as a machine or even a human organ, or yet a process like a climate. By studying the behavior of the digital twin, it is possible to analyze, understand, and predict the behavior of its counterpart in the real world and to solve issues before they occur. However, to take full advantage of the digital twin’s potential, real systems need not only be networked with each other but also need to develop the ability to “think” and act autonomously [56].

This development tends toward AI, from simple mutual perception and interaction to independent communication and optimization, also requiring integrated information systems that allow a continuous exchange of information, still demanding powerful software systems that can implement them along the entire value chain, and planning and designing products, machines, and plants, in addition to operating products and production systems. The technology of digital twins allows users to act in a much more flexible and efficient way, as well as personalize their manufacturing [57].

      Intelligent Edge refers to the place where data is digitally generated, interpreted, analyzed, and treated, i.e., the use of this technology means that analyses can be managed more quickly and that the probability of data being unduly intercepted or leaked is considerably less. This technology refers to the analysis of data and the development of solutions in the place where the data is generated, reducing latency, costs, and security risks, making associated businesses more efficient, still pondering that the three largest categories of Intelligent Edge are the edges of operational technologies, IoT edges, and IT edges [58].

      The use of Intelligent Edge technology helps to maximize business efficiency, since instead of sending data to a data center or even to a third party to perform processing, the analysis is performed at the location where the data is generated. This means not only that the analysis can be performed more quickly, but it also means that companies are much more self-sufficient and do not depend on potentially flawed network connections to do their job [58].

      Predictive maintenance is one of the most promising branches for industrial applications based on the use of data received from the factory to avoid production failures. This type of system eliminates unnecessary maintenance and increases the probability of avoiding failures, which involves a machine or even a component with sensors capable to collect and transmit data and then analyze it, and perform storage in a database. Then, this database offers comparison points for events, as they occur [59, 60].

      The predictive maintenance model aims to periodically monitor the operation of machinery, equipment, and parts in a factory, in order to detect failures before they occur and prevent interruptions in the production line, relating IoT and AI in order to assist in the survey and management of data from all sectors of production, integrating the company’s departments, performing analyzes to take advantage of the useful life of industrial equipment, indicating the real conditions of its operation, detecting possible deterioration of parts and components, and ensuring the reliability and availability of services. This information obtained is used to support decisions and present suggestions for actions and interventions, generating better results than with the use of raw data [59, 60].

1.6 Conclusions

IoT refers to the network of intelligent devices that are concerned with issues of connectivity, competition, and protocols, among other aspects. Relating the respective AI to the branch of cognitive computing caring for principles of data analysis, statistics, and other aspects. Considering that when applied together, it brings results related to the data generated by the IoT and can be processed by an AI software, which will optimize decision-making and contribute to the increase in the agility of the processes.

      From the historical point of view, objects (things), people, and even nature, emitted a huge amount of data; however, humanity just could not to perceive, i.e., see, hear, or make sense of them. However, through the IoT and the data collected, humanity began to see, understand, and use it to its advantage with technological advances in practically all sectors of society. It is in this aspect that the IoT came to change the reality of the contemporary and modern world, considering that everything around the environment has intelligence and is interconnected, so that through this technology, it is possible to have access to data, or better, information. Having access to this sea of data, which through the technological potential brought by AI is able to put digital intelligence and transform them into information, i.e., knowledge, and finally, into wisdom.

      Starting from the premise that it is possible to perceive the patterns of all these data, society will become more efficient, effective, increasing productivity, enhancing the quality of life of people, and the planet itself. Reflecting on the possibility of generating new insights, new activities promoting even more technological innovation. In this respect, the bridge between data collection (information) and the suitable sharing of that data, with safety and protection digital for all parties, abides the key in technological evolution.

      Reflecting on the industrial sector, it is possible to identify a behavioral trend and anticipate the application of a new idea, and this premise shows that the world is heading toward the Fourth Industrial Revolution. This represents the introduction of information technology in industries, correlating a hidden potential that is the use of data, since the good use of this data increases operational efficiency, better decision-making, and even creates new business models.

Finally, IIoT brings together different technologies correlating the Information Technology (IT) initiative for resource management, planning, and decision support systems, Operations Technology (TO) that monitors, analyzes, and controls field equipment, manufacturing, and production procedure, through AI. One of the applications of this is predictive analysis, which makes it possible to forecast a given situation in the future based on information from the past and probability. From this, it is possible to get an AI to perform a certain action corresponding to a specific sensor in the IoT network indicating a specific state of the shop floor, optimizing this activity with increased precision.

      Still reflecting on the digitization of processes and the entire production chain of the industry, it is the basis of Industry 4.0, with the layers of IoT and IIoT enabling the planning, control, and even tracking of production, both by digital simulation and virtualization, winning decision-making time and cost reduction. Thus, AI and