Future smart healthcare systems—often referred to as Internet of Medical Things (IoMT) – will combine a plethora of wireless devices and applications that use wireless communication technologies to enable the exchange of healthcare data. Smart healthcare requires sufficient bandwidth, reliable and secure communication links, energy-efficient operations, and Quality of Service (QoS) support. The integration of Internet of Things (IoT) solutions into healthcare systems can significantly increase intelligence, flexibility, and interoperability. This work provides an extensive survey on emerging IoT communication standards and technologies suitable for smart healthcare applications. A particular emphasis has been given to low-power wireless technologies as a key enabler for energy-efficient IoT-based healthcare systems. Major challenges in privacy and security are also discussed. A particular attention is devoted to crowdsourcing/crowdsensing, envisaged as tools for the rapid collection of massive quantities of medical data. Finally, open research challenges and future perspectives of IoMT are presented.

This article presents a blockchain-based scheme for energy trading between electric vehicles (prosumers) and critical load (consumer) in a microgrid. Unlike traditional wholesale energy markets where retailers sell energy to consumers, our proposed model directly connects prosumers with consumers to meet temporary energy demands. We exploit blockchain technology to establish a trusted energy trading ecosystem. The article provides an overview of the blockchain enabled architecture that facilitates peer-to-peer energy trade in a logical network, using the building blocks provided by 5G network. An energy trading prototype is developed to remotely monitor energy trading activities between prosumers and consumer, using graphical user interface. Experimental results based on a real map of Paisley illustrate that the energy trading system is effective in finding, associating and routing prosumers to consumers, while protecting privacy of entities. Numerical results show a favorable performance of our optimization model in comparison to traditional frameworks.

This research aimed to comprehend the impact of the first school closure on secondary education in Cyprus from students’ perspectives. Through the administration of an online survey with both closed and open questions, the study investigated the main issues and challenges that students experienced during their abrupt transition from face-to-face to emergency remote education and their main worries and concerns during this period, as well as their perceptions regarding the effectiveness of the teaching and learning process followed during the distance education period. The study also elicited students’ suggestions, based on their experiences from the lockdown period, on improving instruction in case of further lockdown(s) and in the post-COVID-19 era. Three hundred twenty-two (322) students of both upper and lower secondary education participated in the study. The data from closed questions were analysed using descriptive and inferential statistics, and for the data from the open questions, a qualitative thematic analysis approach was followed. The valuable insights gained from this study illustrate how important it is for educators and educational policymakers to give voice to students, closely listen to what learners felt worked and what did not during the period of remote learning, and develop plans and policies that incorporate their feedback.

The current article provides an overview of a Teacher Professional Development (TPD) program that has been designed, pilot tested, and implemented to investigate the impact of augmented reality (AR) on: (a) Teachers’ level of technology (AR) acceptance, adoption of inquiry-based instructional approaches, and confidence towards teaching twentieth-first century skills in STEM-related courses; and (b) students’ potential enhancement of specific twentieth-first century skills and motivation and interest during a STEM- (science, technology, engineering, mathematics)-related course supported with AR. This article focuses on the teachers’ points of view concerning the impact of their STEM-related interventions on their students’ motivation and learning, as well as the factors that influence the teachers’ technology acceptance. The TPD program has been implemented in Cyprus and Greece with twenty-five lower secondary school teachers (20 in Cyprus and 5 in Greece). The research methodology applied is Educational Design Research (EDR), including an initial phase of the TPD program and a second (improved) phase. The data collection tools consisted of questionnaires, interviews, and observation of classroom interventions. Initial findings and their implications for teaching and future research are discussed, indicating the potential benefits and challenges surrounding the integration of AR within the educational process.

5G wireless is the next step in the evolution of mobile communications with the aim being to provide connectivity for any kind of device and any kind of application. Wireless Body Area Networks (WBANs) constitute just one component of connected healthcare utilising small intelligent physiological sensors either on or implanted in the human body. This contribution examines the 5G technologies that will make a significant contribution to providing secure healthcare-orientated WBANs with improved energy efficiency, interference mitigation and wireless power transfer capability.

Mobile crowd sensing (MCS) is an application that collects data from a network of conscientious volunteers and implements it for the common or personal benefit. This contribution proposes an implementation that collects the data from hypertensive patients, thus creating an experimental database using the cloud service Platform as a Service (PaaS). The challenge is to perform the analysis without the main diagnostic feature for hypertension—the blood pressure. The other problems consider the data reliability in an environment full of artifacts and with limited bandwidth and battery resources. In order to motivate the MCS volunteers, a feedback about the patient’s current status is created, provided by the means of machine-learning (ML) techniques. Two techniques are investigated and the Random Forest algorithm yielded the best results. The proposed platform, with slight modifications, can be adapted to the patients with other cardiovascular problems.