MoreAir is a low-cost and agile urban air pollution monitoring system. This paper describes the methodology used in the development of this system along with some preliminary data analysis results. A key feature of MoreAir is its innovative sensor deployment strategy which is based on mobile and nomadic sensors as well as on medical data collected at a children’s hospital, used to identify urban areas of high prevalence of respiratory diseases. Another key feature is the use of machine learning to perform prediction. In this paper, Moroccan cities are taken as case studies. Using the agile deployment strategy of MoreAir, it is shown that in many Moroccan neighborhoods, road traffic has a smaller impact on the concentrations of particulate matters (PM) than other sources, such as public baths, public ovens, open-air street food vendors and thrift shops. A geographical information system has been developed to provide real-time information to the citizens about the air quality in different neighborhoods and thus raise awareness about urban pollution.

Artificial Intelligence has the potential to contribute to sustainable cities, life on land, and climate action. Specifically, data-driven AI models can analyze large, interconnected databases to develop joint environmental actions. Air quality plays a pivotal role in both climate action and the development of sustainable cities, but developing countries face challenges due to insufficient monitoring stations and limited access to air quality data sets. This study builds upon the MoreAir project, which established a low-cost air pollution monitoring system and provided the first air quality data set from Morocco. We first exploit and delve into the details of the obtained dataset. Subsequently, we conduct a multi-level comparison of data-driven forecasting models, specifically focusing on short-term forecasting of Particulate Matter concentrations. Four forecasting frameworks are explored, using different combinations of exogenous data and spatio-temporal information. Our findings highlight that Machine Learning models, particularly LightGBM and CatBoost, outperform other models. Overall, our study demonstrates that the inclusion of the spatial dimension along with the diverse exogenous features enhances the models’ predictive performance, and provides valuable insights.

This article introduces an innovative approach integrating machine learning (ML) methods into a far-field wireless power transfer (WPT) system. Within this system, a radar is employed to detect the presence and height of individuals. These data, combined with information on the location of nodes, as well as the propagation channel between transmitters and nodes, are fed into the proposed ML algorithm. The ML model aims to predict the optimal power level for each transmitter, effectively establishing a safe 3-D zone around people while also maximizing power transfer efficiency. The advantages of using ML are the realization of a real-time system, which is crucial in indoor applications, keeping dangerous radiation at a safe level with a very low risk of harmful exposure, and simultaneously enhancing efficiency. Three ML models are evaluated, namely the random forest (RF), support vector machine (SVM), and neural network (NN). Simulation results highlight the superior performance of the NN model, demonstrating its ability to effectively capture the complex nonlinear characteristics of indoor propagation environments, with only approximately 6% of its predictions exceeding the predefined safety threshold. The experimental results show that NN-based WPT can maintain the electric field amplitude (EFA) below a defined threshold for multiple indoor experimental scenarios over the person’s height. In addition, the proposed approach outperformed the maximum ratio transmission (MRT) approach in terms of radio frequency–radio frequency (RF–RF) transmission efficiency in 21.43% of the measurements conducted with multiple people present in the testbed.

The evolution of 6G networks is a pivotal time to align technological advances with global sustainability objectives, specifically the United Nations’ Sustainable Development Goals (SDGs). This Systematic Literature Review (SLR) examines how 6G technologies approach sustainability through energy efficiency, reducing e-waste, reducing emissions, smart cities, and governance frameworks. We identify that SDG 9 (Industry, Innovation, and Infrastructure), SDG 7 (Affordable and Clean Energy), and SDG 13 (Climate Action) are the most frequently targeted SDGs in 6G and its development. In addition to the latest key innovations in 6G, our findings reveal research gaps and directions forward. The “drips on the heatplate” analogy represents how individually small but well-timed sustainability efforts can accumulate to reduce future communication networks’ environmental strain significantly. This review highlights the existing trends of SDG integration in 6G and emphasizes the need for sustainability to be a core part of 6G design in the future.