In the design and development of Wireless Sensor Networks (WSNs), one of the main challenges is to achieve maximal battery lifetime , because of the constrained energy resources. The application of WSNs for real time monitoring contributes to minimization of potential production risks, emerging mainly from environmental influences and human actions. In the paper mathematical tools for optimization of the energy consumption of wireless sensor nodes while collecting distributed data in environmental parameters monitoring will be presented. In this contribution the stochastic optimization method-genetic algorithm is used to minimize the energy consumption of the wireless sensor nodes depending on the frequency of the transmitted data and the period of the transmission process. The optimization is conducted in cases of different scenarios: while the frequency of the transmitted data as well as the period of transmission of all the active components in a sensor node is increasing.

Air pollution is one of the most important topics as it can cause various reactions of the organisms, such as mental health disorders, respiratory problems or various cardiovascular despises. Many of the side effects of pollution are caused by particulate matter (PM). Therefore air pollution, especially the concentration of PM is monitored in many European countries. In the past years, Skopje has been one of the top-ranked cities in the world concerning the concentration of PM. This paper investigates the effect of the pandemic with COVID-19 and the restriction measures on air quality. The data collected before the pandemic (May 2018), during the global pandemic (May 2020 and May 2021), and after the period with restriction measures (May 2022) are analyzed. The measurement parameters are collected at the technical campus of the Ss Cyril and Methodius University in Skopje, North Macedonia, in May 2018, May 2020, May 2021, and May 2022. In this research, it can be confirmed that the restriction measures had a significant positive impact on air pollution.

The study delves into the realm of air quality forecasting, employing the LASSO (Least Absolute Shrinkage and Selection Operator) modeling technique for enhanced predictive accuracy. Utilizing a diverse dataset encompassing meteorological parameters, pollutant concentrations, and other relevant factors, the research explores the robustness of LASSO regression in predicting air pollution dynamics. The analysis establishes correlations and identifies key predictors, shedding light on the intricate relationships within the data. The paper contributes valuable insights to the field of air quality prediction, showcasing the efficacy of LASSO modeling in providing accurate and reliable forecasts, thus facilitating proactive measures for pollution mitigation and environmental management. Additionally, the aim of the paper is to investigate whether the COVID-19 pandemic exerted any discernible impact on pollution levels.

Sustainable management of indoor and outdoor air quality is essential for protecting public health, enhancing well-being, and supporting resilient urban environments. Low-cost air quality sensors enable continuous, real-time monitoring of key pollutants and, when combined with data analytics, provide scalable and cost-effective insights for smart building operation and environmental decision-making. This pilot study evaluates an indoor–outdoor air quality monitoring system deployed at the Faculty of Electrical Engineering and Information Technologies in Skopje, with a focus on: (i) PM2.5 and PM10 concentrations and their relationship with meteorological conditions and human occupancy; (ii) sensor responsiveness and reliability in an educational setting; and (iii) implications for sustainable building operation. From January to March 2025, two indoor sensors (a classroom and a faculty hall) and two outdoor rooftop sensors continuously measured PM2.5 and PM10 at one-minute intervals. All sensors were calibrated against a reference instrument prior to deployment, while meteorological data were obtained from a nearby station. Time-series analysis, Pearson correlation, and multiple regression were applied. Indoor particulate levels varied strongly with occupancy and ventilation status, whereas outdoor concentrations showed weak to moderate correlations with meteorological variables, particularly atmospheric pressure. Moderate correlations between indoor and outdoor PM suggest partial pollutant infiltration. Overall, this pilot study demonstrates the feasibility of low-cost sensors for long-term monitoring in educational buildings and highlights the need for adaptive, context-aware ventilation strategies to reduce indoor exposure.