Motivated by the recent explosion of interest around Educational Robotics (ER), this paper attempts to re-approach this area by suggesting new ways of thinking and exploring the related concepts. The contribution of the paper is fourfold. First, future readers can use this paper as a reference point for exploring the expected learning outcomes of educational robotics. From an exhaustive list of potential learning gains, we propose a set of six learning outcomes that can offer a starting point for a viable model for the design of robotic activities. Second, the paper aims to serve as a survey for the most recent ER platforms. Driven by the growing number of available robotics platforms, we have gathered the most recent ER kits. We also propose a new way to categorize the platforms, free from their manufacturers’ vague age boundaries. The proposed categories, including No Code, Basic Code, and Advanced Code, are derived from the prior knowledge and the programming skills that a student needs to use them efficiently. Third, as the number of ER competitions, and tournaments increases in parallel with ER platforms’ increase, the paper presents and analyses the most popular robotic events. Robotics competitions encourage participants to develop and showcase their skills while promoting specific learning outcomes. The paper aims to provide an overview of those structures and discuss their efficacy. Finally, the paper explores the educational aspects of the presented ER competitions and their correlation with the six proposed learning outcomes. This raises the question of which primary features compose a competition and achieve its’ pedagogical goals. This paper is the first study that correlates potential learning gains with ER competitions to the best of our knowledge.

The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fifth-generation millimeter-wave multicellular networks, where massive multiple-input multiple-output configurations are employed in all active base stations of the considered orientations. In this context, beamforming is performed with the help of a predefined set of configurations that can deal with various traffic scenarios by properly generating highly directional beams on demand. In parallel, a machine learning (ML) beamforming approach based on the k-nearest neighbors (k-NN) approximation has been considered as well, which is trained in order to generate the appropriate beamforming configurations according to the spatial distribution of throughput demand. Performance is evaluated statistically, via a developed system level simulator that executes Monte Carlo simulations in parallel. Results indicate that the achievable spectral efficiency (SE) and energy efficiency (EE) values are aligned with other state of the art approaches, with reduced hardware and algorithmic complexity, since per user beamforming calculations are omitted. In particular, considering a two-tier cellular orientation, then in the non-ML approach EE and SE can reach up to 5 Mbits/J and 36 bps/Hz, respectively. Both metrics attain the aforementioned values when the ML-assisted beamforming framework is considered. However, beamforming complexity is further reduced, since the ML approach provides a direct mapping among the considered throughput demand and appropriate beamforming configuration.

Image transmission over Low-Power Wide Area Networks (LP-WAN) protocols has always been a difficult task since it necessitates high data rates and high energy consumption. Long Range (LoRa) is one such protocol, which is excellent for transferring data over long distances but has generated severe doubts regarding the viability of image transmission due to its low data rate. This paper demonstrates the application results of an integrated LoRa and Deep Learning-based computer vision system that can efficiently identify grape leaf diseases using low-resolution images. In particular, the focus in this paper is to combine the two technologies, LoRa and Deep Learning, to make the transmission of the images and the identification of the diseases possible. To achieve this objective, the framework utilizes a combination of on-site and simulation experiments along with different LoRa parameters and Convolutional Neural Model (CNN) model fine-tuning. Based on the evaluation, the proposed framework proved that the transmission of images using LoRa is possible within the protocol limitations (such as limited bandwidth and low duty cycle). Our fine-tuned model can efficiently identify grape leaves diseases. The technique is both efficient and adaptive to the specifics of each leaf disease, while it does not need any training data to adjust parameters. It is worth noting that today, end-user trust in Machine and Deep Learning models has increased significantly because of novel solutions in the field of Explainable Artificial Intelligence (XAI). In this study, we use the Grad-CAM method to visualize the output layer judgments of the CNN. The disease’s spot region is highly activated, according to the visualization findings. This is how the network distinguishes between different grape leaf diseases.

The 21st century is considered to be “The Century of Cities”. By the end of this century, over 80% of the global population is expected to be living in urban areas. To become smart, a city should develop an approach of services that will focus mainly on citizens to be the primary beneficiaries of the services offered by a Smart City. In this work, we present through a survey of 545 participants, the citizens’ perception about the smart city concept and reveal the Greek and Cypriot citizens’ level of knowledge regards to a Smart City’s actions, applications, and elements. The final results of this study revealed several interesting outcomes. Firstly, this study showed that Cypriot citizens seem to know better what a “Smart City” is compared to Greek citizens, secondly, the study revealed that a large number of participants do not believe that any efforts have been made in their city in order to become “smart” and finally, regards to the most important challenges for the development of a smart city, the survey disclose that the cooperation of the private and public sector is the biggest challenge that needs to be tackled so as citizens can move towards a “smarter” future.

Today's industrial facilities, such as oil refineries, chemical plants, and factories, rely on wired sensor systems to monitor and control the production processes. The deployment and maintenance of such cabled systems is expensive and inflexible. It is, therefore, desirable to replace or augment these systems using wireless technology, which requires us to overcome significant technical challenges. Process automation and control applications are mission-critical and require timely and reliable data delivery, which is difficult to provide in industrial environments with harsh radio environments. In this article, we present the GINSENG system which implements performance control to allow us to use wireless sensor networks for mission-critical applications in industrial environments. GINSENG is a complete system solution that comprises on-node system software, network protocols, and back-end systems with sophisticated data processing capability. GINSENG assumes that a deployment can be carefully planned. A TDMA-based MAC protocol, tailored to the deployment environment, is employed to provide reliable and timely data delivery. Performance debugging components are used to unintrusively monitor the system performance and identify problems as they occur. The article reports on a real-world deployment of GINSENG in an especially challenging environment of an operational oil refinery in Sines, Portugal. We provide experimental results from this deployment and share the experiences gained. These results demonstate the use of GINSENG for sensing and actuation and allow an assessment of its ability to operate within the required performance bounds. We also identify shortcomings that manifested during the evaluation phase, thus giving a useful perspective on the challenges that have to be overcome in these harsh application settings.