One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

Nowadays there is a growing demand for small- and medium-sized enterprises (SMEs) to improve their level of digitalisation. This situation becomes even more critical in cases when SMEs act in the role of a subcontractor of large enterprises who demand the utilisation of certain digital operations. This paper aims to identify the essential Industry 4.0 attributes for the requirements of SMEs that enterprises can purchase to deploy an adequate solution with a view of increasing their competitiveness in the market. By analysing research articles and statistical data from the worldwide Web of Science database, we identify the major Industry 4.0 attributes for SME: Internet of Things (IoT), Big Data, Artificial Intelligence (AI), Cloud Computing, Simulation and Cybersecurity. Based on the review results and a survey by the European Commission, we propose devices primarily designed to implement AI tasks in industrial environments that meet the essential attributes for SMEs and have low entry costs. The subject of IoT is thoroughly addressed. Its subsets and the relationship between Industrial Internet of Things (IIoT) and Artificial Intelligence of Things (AIoT) are introduced and described. The characteristics of the listed devices as related to usability in the identified attributes are verified. Therefore, the description of the devices is provided with respect to their usability in SMEs. The main purpose of this paper is to identify attributes for SMEs and to develop strategic plans for the digitalisation requirements, particularly in the development of Artificial Intelligence as part of the implementation of the IoT pillar.

Internet of Things (IoT) devices are not only finding increasing use in ordinary households, but they have also become a key element for the Industry 4.0 concept. The implementation of industrial IoT devices into production streamlines the production process and reduces production costs. On the other hand, connected IoT devices bring new security risks to production and expose an industrial environment to new types of attacks. The article analyzes the vulnerability of the production line with implemented industrial IoT devices with consideration of a possible Distributed Denial-of-service (DDoS) attack led by attackers from the internet. Various types of DDoS attacks abusing the presence of IoT devices in the system were performed on an automated production line implementing sorting, preparation, and dosing of bulk and liquid materials for filling into containers. The leading attacks caused failure of the production line during the production, as well as the dysfunction of communication with IoT devices. The article also demonstrates the implementation of countermeasures against DDoS attacks and possible strategies to protect and mitigate such attacks on the production line.

The purpose of this research is to develop an innovative software framework with AI capabilities to predict the quality of automobiles at the end of the production line. By utilizing machine learning techniques, this framework aims to prevent defective vehicles from reaching customers, thus enhancing production efficiency, reducing costs, and shortening the manufacturing time of automobiles. The principal results demonstrate that the predictive quality inspection framework significantly improves defect detection and supports personalized road tests. The major conclusions indicate that integrating AI into quality control processes offers a sustainable, long-term solution for continuous improvement in automotive manufacturing, ultimately increasing overall production efficiency. The economic benefit of our solution is significant. Currently, a final test drive takes 10–30 min, depending on the car model. If 200,000–300,000 cars are produced annually and our data prediction of quality saves 10 percent of test drives with test drivers, this represents a minimum annual saving of 200,000 production minutes.

The essential component of all the control and sensory systems is a communication layer. The MODBUS protocol used in automation and sensory industry defines a simple format of communication using the master/slave principle, although since it originated in the 1970s it has become obsolete. In this article is presented uBUS protocol as an enhancement of the MODBUS protocol uBUS protocol proposal brings advanced features for today’s requirements for embedded applications. Enhancements include address extension to 4079 addresses, adding a new communication mode (group broadcast), delegating selected master features to slave, linking with other communication interfaces and securing communications layer. The main part of the article deals with the description of design and implementation of the uBUS communication security. An RSA algorithm that uses asymmetric encryption was used to implement communication encryption. The article presents a way of implementing such encryption and its effectiveness on various types of microcontrollers implementing uBUS slaves. Since the communication protocol security is designed as a separate layer, it is possible to implement different types of encryption.

Industry 4.0 collects, exchanges, and analyzes data during the production process to increase production efficiency. Internet of Things (IoT) devices are among the basic technologies used for this purpose. However, the integration of IoT technology into the industrial environment faces new security challenges that need to be addressed. This is also true for a production line. The production line is a basic element of industrial production and integrating IoT equipment allows one to streamline the production process and thus reduce costs. On the other hand, IoT integration opens the way for network cyberattacks. One possible cyberattack is the increasingly widely used distributed denial-of-service attack. This article presents a case study that demonstrates the devastating effects of a DDOS attack on a real IoT-based production line and the entire production process. The emphasis was mainly on the integration of IoT devices, which could potentially be misused to run DDoS. Next, the verification of the proposed solution is described, which proves that it is possible to use the sampled flow (sFlow) stream to detect and protect against DDoS attacks on the running production line during the production process.