The recent advances in 5G and beyond wireless networking have enabled the possibility of using the cellular network as the infrastructure for wireless sensor networks, due to the high bandwidth availability and the reduced cost per data unit. In this paper, we perform an evaluation of the 5G infrastructure for sensor networks in order to quantify the performance in terms of energy efficiency and bandwidth within a testing environment. We used an ESP32 sensor with BLE-connected sensing devices for environmental conditions, and a Raspberry Pi with the Waveshare SIM8200EA-M2 5G module for cellular connectivity. We measured the power usage of each component of the system, in real conditions, as well as the power consumption for different bandwidth usage scenarios, and the end-to-end delay of the system. The results showed that the system is capable of achieving the required delay and bandwidth; however, the energy efficiency of the specific setup leaves room for improvement.

Almost five years have passed since the United Kingdom’s decision to leave the European Union was considered to be definite. Now, in 2021, we finally see the Brexiting process start and some more practical problems arise. Although the implementation of the Withdrawal Agreement as of 1 January 2021 clarifies what is due to happen in certain areas of policy, the language issue still remains unsolved. All these years English has not only been an official and a working language, but also the most commonly used for institutional communication and the one that the majority of the European citizens speak as a second language. Moreover, it is tied to the language regime of the Union, its citizens’ rights and its language policy, and has a special status worldwide. The fact that the United Kingdom left the European Union, but English remains an official language up to this day without the unanimous decision of the Member States is the purpose of this contribution. Furthermore, the position of English in the European Union and the citizens’ rights are analysed, and its future in this context is investigated through the presentation of some scenarios and their results.

Digital transformation in agriculture addresses key challenges such as climate change, water shortages, and sustainable production. Precision agriculture technologies rely on the Internet of Things (IoT) sensor networks, analytics, and automated systems to manage resources efficiently and increase productivity. Fragmented infrastructures and vendor-specific platforms lead to unintegrated data silos that obstruct regional solutions. This paper will emphasize FIWARE, an open-source, standard-based platform that can be integrated with existing agricultural sensors in municipalities or regions. FIWARE takes all these disparate sensors (soil probes, weather stations, and irrigation meters) and integrates them into a single real-time information system, providing a set of decision support tools to the user to facilitate adaptive irrigation. Case studies show the benefits of FIWARE, including water savings, reduced runoff, better decision-making, and improved climate resilience.

Artificial Intelligence (AI) and Media and Information Literacy (MIL) are recognised as vital components of adult secondary education. However, we know little about how adult learners engage with AI in authentic educational contexts, given that AI adoption is a complex sociotechnical process. In this study, AI for MIL (AI/MIL) refers to AI-supported tools that guide learners in interpreting and engaging with digital media and information. We report findings from an ethnographic study involving eight adult students in Greek secondary education. Informed by the Unified Theory of Acceptance and Use of Technology (UTAUT), we followed participants over an eight-month educational intervention through participant observation, interviews, and field notes. Our data show that learners moved from anxiety toward more agentic forms of AI engagement. This ethnographic reinterpretation of UTAUT highlights an interplay among technological, emotional, and sociocultural factors that shape AI/MIL adoption. Beyond traditional technology acceptance models, we underscore negotiated trust, critical AI literacy, ethical awareness, and human agency within AI-mediated information ecosystems. These findings have implications for designing participatory, transparent, and ethically grounded AI initiatives in educational settings.