Numerous municipalities employ the smart city model in large cities to improve the quality of life of their residents, utilize local resources efficiently, and save operating expenses. This model incorporates many heterogeneous technologies such as Cyber-Physical Systems (CPS), Wireless Sensor Networks (WSNs), and Cloud Computing (ClCom). However, effective networking and communication protocols are required to provide the essential harmonization and control of the many system mechanisms to achieve these crucial goals. The networking requirements and characteristics of smart city applications (SCAs) are identified in this study, as well as the networking protocols that can be utilized to serve the diverse data traffic flows that are required between the dissimilar mechanisms. Additionally, we show examples of the networking designs of a few smart city systems, such as smart transport, smart building, smart home, smart grid, smart water, pipeline monitoring, and control systems.

Smart cities are urban areas that utilize digital solutions to enhance the efficiency of conventional networks and services for sustainable growth, optimized resource management, and the well-being of its residents. Today, with the increase in urban populations worldwide, their importance is greater than ever before and, as a result, they are being rapidly developed to meet the varying needs of their inhabitants. The Internet of Things (IoT) lies at the heart of such efforts, as it allows for large amounts of data to be collected and subsequently used in intelligent ways that contribute to smart city goals. Time-series forecasting using deep learning has been a major research focus due to its significance in many real-world applications in key sectors, such as medicine, climate, retail, finance, and more. This review focuses on describing the most prominent deep learning time-series forecasting methods and their application to six smart city domains, and more specifically, on problems of a multivariate nature, where more than one IoT time series is involved.

Over recent years, massive open online courses (MOOCs) have gained increasing popularity in the field of online education. Students with different needs and learning specificities are able to attend a wide range of specialized online courses offered by universities and educational institutions. As a result, large amounts of data regarding students’ demographic characteristics, activity patterns, and learning performances are generated and stored in institutional repositories on a daily basis. Unfortunately, a key issue in MOOCs is low completion rates, which directly affect student success. Therefore, it is of utmost importance for educational institutions and faculty members to find more effective practices and reduce non-completer ratios. In this context, the main purpose of the present study is to employ a plethora of state-of-the-art supervised machine learning algorithms for predicting student dropout in a MOOC for smart city professionals at an early stage. The experimental results show that accuracy exceeds 96% based on data collected during the first week of the course, thus enabling effective intervention strategies and support actions.

This study explores the integration of ChatGPT, a generative AI tool, into academic advising systems, aiming to assess its efficacy compared to traditional human-generated advisories. Conducted within the INVEST European University, which emphasizes sustainable and innovative educational practices, this research leverages AI to demonstrate its potential in enhancing sustainability within the context of academic advising. By providing ChatGPT with scenarios from academic advising, we evaluated the AI-generated recommendations against traditional advisories across multiple dimensions, including acceptance, clarity, practicality, impact, and relevance, in real academic settings. Five academic advisors reviewed recommendations across diverse advising scenarios such as pursuing certifications, selecting bachelor dissertation topics, enrolling in micro-credential programs, and securing internships. AI-generated recommendations provided unique insights and were considered highly relevant and understandable, although they received moderate scores in acceptance and practicality. This study demonstrates that while AI does not replace human judgment, it can reduce administrative burdens, significantly enhance the decision-making process in academic advising, and provide a foundation for a new framework that improves the efficacy and sustainability of academic advising practices.

The current era of (e-)learning invests heavily on Massive Open Online Courses (MOOCs). Various dedicated MOOC platforms are used, such as Coursera, Udacity, and Open edX. Moodle, one of the most popular open-source Learning Management Systems (LMS), has been also used for MOOC implementations. Usability and user experience (UX) are very important for e-learning software. This paper presents an in-depth comparative UX evaluation of two platforms used for MOOC implementations: Open edX and Moodle. The reported evaluation study combines user interaction metrics and wearable biosensors. Participants performed four typical interaction tasks in both platforms. User performance data (task success, time on task), self-reported data (SUS, AttrakDiff), and stress-related physiological data (skin conductance, heart rate) were collected and analyzed. It was found that Open edX performs better than Moodle in almost every UX aspect we examined. However, the reported results do not imply that building and delivering a MOOC via Moodle is prohibitive. The choice of the platform to be used by an institution is affected by several factors of non-technological nature, such as students’ familiarity with an e-learning platform, available financial and human resources, as well as delivery deadlines.

Over the last few years, Massive Open Online Courses (MOOCs) have expanded rapidly and tend to become the most typical form of online and distance higher education. As a result, a tremendous amount of data is generated and stored on MOOCs online learning platforms. In any case, this data should be effectively transformed into knowledge, thus providing valuable feedback to learners, and enhancing decision making practices in the educational field. Despite the benefits and learning prospects that MOOCs offer to learners, there is a considerable divergence between enrollment and completion rates. In this context, the main scope of this study is to exploit predictive analytics and explainable artificial intelligence for the early prediction of student certification in a 11-week MOOC for smart cities, namely DevOps. A plethora of Machine Learning models were built employing familiar classification algorithms. The experimental results revealed that the models based on Gradient Boosting, Logistic Regression and Light Gradient Boosted Machine classifiers prevailed in terms of Accuracy, Area Under Curve, Recall, Precision, F1-score, Kappa, and Matthews Correlation Coefficient, getting a predictive accuracy of 94.41% at the end of the second week of the course. Therefore, students who are less likely to obtain a certificate could be envisaged at an early enough stage to provide sufficient support actions and targeted intervention strategies to them. Finally, the performance attributes (i.e., overall grades per week) proved to be the most important predictors for identifying students at risk of failure.