Air pollution is a major factor in global heating and an increasing focus is centered on solving this problem. Urban communities take advantage of Information Technology (IT) and communications technologies in order to improve the control of environmental emissions and sound pollution. The aim is to mitigate health threatening risks and to raise awareness in relation to the effects of air pollution exposure. This paper investigates the key issues of a real-time pollution monitoring system, including the sensors, Internet of Things (IoT) communication protocols, and acquisition and transmission of data through communication channels, as well as data security and consistency. Security is a major focus in the proposed IoT solution. All other components of the system revolve around security. The bill of the materials and communications protocols necessary for the designing, development, and deployment of the IoT solution are part of this paper, as well as the security challenges. The paper’s proof of concept (PoC) addresses IoT security challenges within the communication channels between IoT gateways and the cloud infrastructure where data are transmitted to. The security implementations adhere to existing guidelines, best practices, and standards, ensuring a reliable and robust solution. In addition, the solution is able to interpret and analyze the collected data by using predictive analytics to create pollution maps. Those maps are used to implement real-time countermeasures, such as traffic diversion in a major city, to reduce concentrations of air pollutants by using existing data collected over a year. Once integrated with traffic management systems—cameras monitoring and traffic lights—this solution would reduce vehicle pollution by dynamically offering alternate routes or even enforcing re-routing when pollution thresholds are reached.

The paper presents the construction of a proof-of-concept for a distributed and decentralized e-voting application in an IoT embedded device with the help of blockchain technology. A SoC board was used as an IoT embedded device for testing the PoC. This solution ensures complete voter anonymity and end-to-end security for all entities participating in the electronic voting process. The paper outlines the solution’s two layers. Implementation details are presented for the e-voting application, which was deployed inside of an IoT embedded device. The solution and presented protocols provide two major properties: privacy and verifiability. To ensure privacy, the proposed solution protects the secrecy of each electronic vote. As for implementing verifiability, the solution prevents a corrupt authority from faking in any way the process of counting the votes. Both properties are achieved in the presented solution e-VoteD-App.

This paper presents edge machine learning (ML) technology and the challenges of its implementation into various proof-of-concept solutions developed by the authors. Paper presents the concept of Edge ML from a variety of perspectives, describing different implementations such as: a tech-glove smart device (IoT embedded device) for controlling teleoperated robots or an UAVs (unmanned aerial vehicles/drones) that is processing data locally (at the device level) using machine learning techniques and artificial intelligence neural networks (deep learning algorithms), to make decisions without interrogating the cloud platforms. Implementation challenges used in Edge ML are described and analyzed in comparisons with other solutions. An IoT embedded device integrated into a tech glove, which controls a teleoperated robot, is used to run the AI neural network inference. The neural network was trained in an ML cloud for better control. Implementation developments, behind the UAV device capable of visual computation using machine learning, are presented.

The need of an automated support system that helps beekeepers maintain and improve beehive population was always a very stressing aspect of their work considering the importance of a healthy bee population. This paper presents a proof of concept, further referred as a PoC solution, based on the Internet of Things technology which proposes a smart monitoring system using machine learning processes, diligently combining the power of edge computing by means of communication and control. Beehive maintenance is improved, having an optimal state of health due to the Deep Learning inference triggered at the edge level of devices which processes hive’s noises. All this is achieved by using IoT sensors to collect data, extract important features and a Tiny ML network for decision support. Having Machine Learning inference to be performed on low-power microcontroller devices leads to significant improvements in the autonomy of beekeeping solutions.

This article analyzes and highlights the security perspective of Internet of Things (IoT) connected devices and their communication challenges, as IoT is considered one of the key emerging fields in Industry 4.0. The IoT architectures can consist of physical systems, virtual ones or even hybrids, combining a collection of different physically active things, sensors, cloud services, specific IoT protocols, communication layers, users and developers. On top of all, it is the business layer, because the scope of the entire IoT environment is to deliver data, to monitor and to facilitate the management of complex processes. In order to facilitate the data exchange between the IoT layers, there have been developed a series of protocols particular to the IoT domain. As in many IT related fields, the solutions are not perfect from the data security and privacy perspectives, many challenges being still open research issues. As the two concepts of IoT and Cloud of Things are connected, bringing real world data into the Cloud to process it, raises Cloud Computing security concerns regarding the privacy and security of data. Although in recent years, many efforts have been made to improve Cloud Computing security, there are risks that need to be taken into consideration. From the Web of Data’s point of view, things are even more prone to security risks. Because privacy is one of the fundamental right of digital users, it is extremely important for new technologies to comply with privacy regulations and policies, such as the new European data protection and privacy frameworks. In this context, companies must take into account standards, challenges and new trends in IoT. In the absence of specific measures, raw or processed data can be easily stolen from the Web of Data. In this paper we analyze and present the main protocols of communication in the IoT field from a data security perspective. Also, we do a review of the main architectures that can improve the security of the communication between IoT devices and the Cloud data storage.

The COVID-19 outbreak is an international problem and has affected people and students all over the world. When lockdowns were imposed internationally, learning management systems began to be used more than in the previous period. These systems have been used also for traditional forms of learning and not only for online learning. This pandemic has highlighted the need for online learning systems in the educational environment, but it is very important for these systems to be secure and to verify the authenticity of the students when they access a course or evaluation questions. In this period, everything is moving towards the digital world, with students that are connected from a distance to online systems. All activities in the educational environment will soon be performed digitally on learning management systems, which includes also the evaluation process of the students. In this paper, we propose a secure learning management system that uses the student’s behavior to identify if they are an authentic student or not. This system can support the teacher’s activities in the learning process and verify the authenticity of the students logged on to the system. This paper is aimed at learning management system developers, who can use the proposed algorithms in their developed platforms, and also at teachers, who should understand the importance of the identification of students on these platforms.