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 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.
Abstract:The recent phenomenon of worldwide declining enrolments in engineering-related degrees has led to the gradual decrease in the number of engineering graduates. This decrease occurs at a time of increasing demand in the labour market for highly qualified engineers, who are necessary for the implementation of fundamental societal functions. This paper initially presents a survey of practices, which are currently employed by academic institutions in Europe in order to increase the attractiveness of their engineering studies. It then provides a detailed analysis of the benefits and proliferation of distance education to increase attractiveness of engineering studies based on a set of interviews. Results of this study, highlight a lack of a distance-learning dimension in the implementation of engineering studies in the European Area and discusses in detail ways in which distance learning can be utilised in engineering studies for the benefit of increasing their attractiveness. It has also been noted that institutions employing distance learning as part of their engineering studies, see this as highly beneficial for their students but also for the academic institution itself with some reservations in terms of the pedagogical adequacy of materials and instructional approaches used in distance education courses.
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.
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