Increasing disruption from Industry 4.0/5.0, climate change, digital transformation, wars and high levels of volatility, uncertainty, complexity and ambiguity (VUCA) are leading to major changes in the labor market. In addition, improvements in chatbots and Generative AI are opening new opportunities for students and the future workforce. They seem to have become ‘creative’ in their field of study and are turning to AI applications for their learning, assignments or theses. However, there are concerns about academic integrity and cybersecurity. Students and researchers have already started using AI chatbots to learn, program, write academic essays or conduct research faster and more effectively. This paper explores whether AI can serve as a bicycle for human creativity and innovation. What are the first words that come to mind when you think of Engineering Education 5.0? Have you used Artificial Intelligence tools in your teaching, learning or research? This paper presents the results of a survey conducted at the Faculty of Electronics, Technology and Information Technology on the challenges and opportunities of using Generative AI(GenAI) in Engineering Education. This paper also outlines the Generative AI ecosystem for teaching, learning and research. Engineering Education 5.0 needs a future ready Curricula including AI Literacy, new teaching and learning methods, analytical and critical thinking, reflection, collaboration and complex problem-solving, and communities of practice with the industry to foster innovation and creativity.

We aim to evaluate the performance of a ZigBee wireless sensor network using OPNET. The network is intended for a general application and should be able to collect data from many nodes, every node connected to several sensors. One possible application for such a network is in the medical area. At first the network will be used to display the evolution of blood pressure; the body temperature, the ambient temperature and pressure and the level of the transfusion liquid can be collected as well. The network coordinator will transmit the data to a local server for further processing. The simulation will be used to evaluate the general parameters of the wireless network and to optimise it.

This paper provides a comprehensive study to investigate the potential of multi-relay methods in V2V-VLC systems through detailed performance analysis using realistic channel modeling based on ray tracing. Key performance metrics evaluated include maximum achievable communication distances, system capacity, and the influence of system parameters, transceiver design, and environmental factors, such as fog and rain. Results demonstrate that adverse conditions, such as thick fog, can reduce the direct communication range by up to 19.5% at high transmission power (P_t = 25 dBm), limiting it to 33 m. However, incorporating multi-relay links significantly extends the transmission range, reaching 140 m under thick fog conditions and up to 170 m in clear weather with three relays. Additionally, analysis shows that increasing the receiver aperture diameter enhances the system’s maximum achievable range, with a 4 cm aperture enabling distances up to 700 m in a multi-relay setup. Furthermore, the study highlights that increasing the number of relays improves system capacity, achieving up to 358 m at 5 Mbit/s with seven relays under clear atmospheric conditions. These findings underscore the importance of multi-relay strategies in improving the reliability and effectiveness of V2V-VLC systems across diverse operating scenarios.

The IoT domain is in growing exponentially. Monitoring the values collected by sensors, displaying, interpreting and making a decision after analyzing them, are very important steps that are performed within an IoT (Internet of Things) system. At the same time, another important concept is that of energy saving, for example by using energy delivered by photovoltaic panels. Two important parameters to monitor are the level of carbon monoxide and temperature in an enclosed space since high values of these parameters can endanger human life. This project falls within the field of IoT and is based on the concepts described above; the temperature value and the carbon monoxide level are processed by a microcontroller and transmitted to a database, and to a telephone application respectively, in order to be able to permanently monitor their values. If the carbon monoxide level exceeds a certain level, the buzzer associated with the system will alert the people in the room and a supervisor, by means of the Internet connection. In the following chapters the block diagram, together with the main components, the implementation and testing of the system will be presented.

This paper describes the design and implementation of an automated irrigation control system that uses data collected by a wireless sensor network. Each sensor node, built on a custom-designed printed circuit board, includes sensors for light intensity, temperature, and a custom soil moisture sensor. Data is transmitted to a central control node via ESP-NOW, where it is processed and compared with configurable thresholds retrieved from Google Sheets over Wi-Fi. Irrigation is triggered automatically when conditions meet the remotely defined thresholds. A key contribution is the development and testing of a custom soil moisture sensor, with results compared to commercial models. The system supports low-power operation through deep sleep modes, enabling long-term field deployment. The novelty lies in the complete integration of hardware, software, and cloud-based control, providing a flexible and low-cost solution for precision agriculture. The system can be deployed in greenhouses or open fields and serves as a platform for future research in smart irrigation. The fundamental aspect is a very user-friendly solution for any farmer attributable to easy accommodation to the Google Sheets interface, no maintenance cost over the cloud account, and up to 45 days of battery life or a built-in alternative for solar power.

The paper presents some investigations on the current carrying capabilities of 3D-printed graphene-enhanced conductive poly-lactic acid based samples. Such samples with different dimensions and shapes are tested at different currents and electrical and thermal measurements are performed. An automated measurement system is used for observing any change in sample resistance for the time span of each test. Simple qualitative conclusions modelling and simulation of resistance of 3D-printed samples will be performed and an empirical model for their maximal current capabilities will be identified.