The integration of artificial intelligence into the Industrial Internet of Things (IIoT), supported by edge computing architectures, marks a new paradigm of intelligent automation. Tiny Machine Learning (TinyML) is emerging as a key technology that enables the deployment of machine learning models on ultra-low-power devices. This study presents a systematic review of 110 peer-reviewed publications (2020–2025) identified from Scopus, Web of Science, and IEEE Xplore following the PRISMA protocol. Bibliometric and thematic analyses were conducted using Biblioshiny and VOSviewer to identify major trends, architectural approaches, and industrial applications of TinyML. The results reveal four principal research clusters: edge intelligence and energy efficiency, federated and explainable learning, human-centric systems, and sustainable resource management. Importantly, the surveyed industrial implementations report measurable gains—typically reducing inference latency to the millisecond range, lowering on-device energy cost to the sub-milliwatt regime, and sustaining high task accuracy, thereby substantiating the practical feasibility of TinyML in real IIoT settings. The analysis indicates a conceptual shift from engineering- and energy-focused studies toward cognitive, ethical, and security-oriented perspectives aligned with the principles of Industry 5.0. TinyML is positioned as a catalyst for the transition from automation to cognitive autonomy and as a technological foundation for building energy-efficient, ethical, and sustainable industrial ecosystems.

The use of different energy sources for heating and year-round domestic water heating is driven by the European Union’s increasingly strict environmental and climate requirements. For this reason, consumers are seeking alternatives and show growing interest in implementing installations that utilize solar energy. Modern households typically employ at least two different energy sources for this purpose. In practice, these are hybrid installations that, depending on the season, can operate with one, two, or more energy sources. The system examined in this paper is of this type, comprising a pellet boiler, solar vacuum tubes, and electric heaters. Managing such a system is complex, and based on the conducted studies, process optimization can be pursued. This report presents an artificial neural network (ANN) model developed to predict the behavior of a real solar installation for domestic hot water heating during the summer season. This study aims, through the obtained model, to forecast the system’s performance during transitional periods such as autumn and spring, thereby enabling more efficient control.

This study explores the application of 3D printing as a strategic tool in engineering education and robotics development. An embedded control system for a 3D-printed MK2 manipulator is implemented, including an Arduino microcontroller, servo motors, an analog joystick interface, and an LCD, with software developed in Arduino IDE. The design uses PLA material and a modular architecture for flexibility and extensibility. The platform is applied in laboratory training to develop algorithmic thinking and engineering creativity, demonstrating the potential of 3D printing as an integrated educational and engineering tool.

Water is known to be a chemical compound and plays an essential role in sustaining life on our planet. Fresh water on earth is only 2.5% and the problems with the drought of the regions are known. Therefore, its sparing use will contribute to reducing this effect. This requires the revision of some of the policies of the agricultural sector and the implementation of new and innovative irrigation solutions. Through irrigation and their root system, plants grow and develop. The application of a proper irrigation regime will contribute to a reduction in water use. In this study, an attempt has been made to create an autonomous system for determining and applying the irrigation regime of plants. For this purpose, a greenhouse was used for the industrial production of cucumbers. A soil moisture sensor is used, and the signal is processed by software developed for this purpose, which produces a control signal for the beginning and end of the irrigation process. The process is controlled autonomously, depending on the current state of humidity and when a certain value is reached, the process is terminated. The sensor part is made of standard soil moisture sensors, and the control part is entrusted to a single-board computer with the ability to control via a wireless network.

Modeling an artificial neural network to predict the behavior of a heating and domestic hot water system in a residential building is the basis of this report. The heating system includes three types of energy sources: biofuel, solar, and electricity. During transitional seasons, such as spring and autumn, it is possible to use all three sources periodically or simultaneously, which leads to increased consumption and inefficiency in the system. In order to study the dynamics of the process and optimize the control, a monitoring system based on Raspberry Pi Pico W was developed. Data on the operating parameters of the boiler, tank, and radiator is transmitted in real-time to the ThingSpeak cloud platform, using the free access option that requires only registration. Due to the reinforced concrete structure of the building, the local Wi-Fi connection drops at times, so an additional router was added. Although individual values from the data are missing, they can be used to obtain graphs of the transient process. The data received in the platform does not arrive evenly due to the time required for transmission. The artificial neural network was created in MATLAB (v2025, MathWorks, Natick, MA, USA) and uses the raw data to predict changes in the processes. The missing fragments of the data have no impact, and a good match of the actual values with the predicted values was obtained for the tank and radiator, while for the boiler, the match was satisfactory.

The present work is aimed at developing and researching a robotic arm with five degrees of freedom, manufactured using 3D-printing technology and controlled by an ESP32 microcontroller. This technology is increasingly used in robotics, especially in the educational process, due to the possibilities for rapid prototyping, modification and restoration of individual components. In the development process, the mechanical, hardware and software parts of the system were implemented, and a basic kinematic analysis of the manipulator was performed. A control program was created, allowing the performance of “pick and place” tasks, as well as visualization and manual control through a developed application. The results obtained show that the developed system provides sufficient functionality and flexibility for use in robotics training, while at the same time allowing expansion and upgrading with additional functionalities. The main contribution of the work lies in the implementation of an accessible and adaptable robotic platform, suitable for educational and experimental purposes.