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.

Evolution of mobile devices has led to the development of Mobile E-Learning. E-Learning platforms have been developed to be accessed from personal computers. Now the question is how to organize the content so that it can be accessed from mobile devices. To solve this problem is required a statistical analysis on accessing e-learning platforms on mobile devices. It was developed E-Learning platform with automated data collection and information on how it is accessed by the users. The application has been provided for testing to the students. Based on data obtained automatically building statistical analyzes highlight trends of use of Mobile E-Learning environment

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.