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.

Currently, we are experiencing an ever-increasing demand for high-quality transportation in the distinctive natural environment of forest roads, which can be characterized by significant weather changes. The need for more effective management of the forest roads environment, a more direct, rapid response to fire interventions and, finally, the endeavor to expand recreational use of the woods in the growth of tourism are among the key factors. A thorough collection of diagnostic activities conducted on a regular basis, as well as a dataset of long-term monitored attributes of chosen sections, are the foundations of successful road infrastructure management. Our main contribution to this problem is the design of a probe for measuring the temperature profile for utilization in stand-alone systems or as a part of an IoT solution. We have addressed the design of the mechanical and electrical parts with emphasis on the accuracy of the sensor layout in the probe. Based on this design, we developed a simulation model, and compared the simulation results with the experimental results. An experimental installation was carried out which, based on measurements to date, confirmed the proposed probe meets the requirements of practice and will be deployed in a forest road environment.

Currently, industrial engineers are witnessing a rapid development of innovation in modern industry and the integration of critical elements of Industry 4.0 into production processes in order to remain competitive. Such changes are characterized by a large amount of effort and financial resources. To successfully deploy these changes requires not only the purchase of hardware and software but also the training of employees and the modification of the company’s organizational structure. The main objective of the article is to propose a framework for the modernization of SMEs to a level close to the Smart Factory by using the necessary attributes of Industry 4.0. The framework design is based on the initial state of a standard SME and consists of the design of fitting new IoT devices for efficient data collection, the design of a data warehouse for storing process data using Hadoop, and the integration of process- and operational-level data into the prepared data warehouse. The resulting design is developed in the form of a methodology and is generalized for use in manufacturing enterprises. The universal design is independent of the initial state of the enterprise.

This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.

This research addresses the long-term measurement of environmental data in geographically remote areas and an energy-optimized method of storing data on a storage medium. For this purpose, we have developed our measurement module ADL - Advanced Data Logger. In terms of connectivity, the module operates in 3 modes: offline - when measured data is primarily stored on the storage medium; IoT ready - measured data is stored on the storage medium and sent to the remote server in defined batches; online mode - when measured data is preferably sent to the remote server immediately after measurement. The design aims to minimize the module’s power consumption so that the autonomous operating time is close to one year. As part of the design, the simpleFS software module is designed for the role of a simple file system optimized to minimize I/O operations. Its other feature in data storage is the automatic normalization of the data transmitted from the attached sensors. The last part of the design is the AdlReader software solution, used to configure the hardware (HW) module and to retrieve the measured data files. We verified the correct operation of the ADL module along with nine sensors built in a vertical soil temperature profile probe in experimental installation and operation for two months. According to the requirements for our solution, the expected operation time of the ADL module is 9 - 12 months.

The design of control systems needs to ensure communication between multiple PLCs or external IoT devices. Furthermore, there are several ways of communication between them. This article dealt with the PLC communication based on TCP/IP protocol, and compared several communication options between two PLCs of the S7-300 and S7-1200 series connected using Profinet with a solution built in C# connected to the PLC via an OPC server. We used MODBUS TCP, TCON, and PUT/GET program blocks for comparison. We transmitted a digital Boolean data type and an analogue value of the integer data type via Profinet to read data from the S7-300 PLC and send data to the S7-1200. We compared the programming instructions standardly used as a client/server PLC programming with a Windows Forms application, and evaluated the advantages and disadvantages of this solution even when using external IoT devices. The solution was applied and successfully tested for communication between PLC S7-1200 and Nvidia Jetson Nano. We wrote program instructions for PLC in Siemens Tia Portal V15.