Air quality monitoring is a critical component of environmental management, especially in developing countries where pollution levels are a growing concern. This review systematically analyzes recent advances in Internet-of-Things (IoT)-based air quality monitoring systems and highlights the impact of artificial intelligence (AI) technologies. A comprehensive selection process was used to identify 220 relevant studies using databases such as Google Scholar, Scopus, and ResearchRabbit. Following a selection process based on specific eligibility criteria, a total of 147 studies were chosen for a detailed analysis. These studies present notable advances in the application of artificial intelligence to improve data accuracy, predictive capabilities, and real-time analysis in air quality monitoring systems. A key contribution of this review is the proposal of a classification framework for AI techniques in air quality monitoring, organized into five main application areas: data imputation, sensor calibration, anomaly detection, air quality index (AQI) estimation, and short-term forecasting. The review takes an in-depth look at the different uses of these technologies in both urban and industrial settings, presenting successful case studies that showcase their effectiveness in addressing pressing air quality issues. Additionally, the paper identifies research gaps in the literature, particularly related to data quality, system scalability, and integration challenges in AI-driven IoT systems. The insights provided aim to guide researchers and practitioners in selecting appropriate AI techniques and system architectures, inform the design of more reliable and scalable air quality monitoring frameworks, and support future efforts to mitigate air pollution through data-driven decision-making.

In a former paper [Fluct. Noise Lett. 13 (2014) 1450020] we introduced a vehicular communication system with unconditionally secure key exchange based on the Kirchhoff-Law–Johnson-Noise (KLJN) key distribution scheme. In this paper, we address the secure KLJN key donation to vehicles. This KLJN key donation solution is performed lane-by-lane by using roadside key provider equipment embedded in the pavement. A method to compute the lifetime of the KLJN key is also given. This key lifetime depends on the car density and gives an upper limit of the lifetime of the KLJN key for vehicular communication networks.

Climate change is one of the main factors causing environmental variations that affect the crops in agriculture worldwide. These variations are expected to not only continue, but also to increase, putting future agricultural production and food security at risk. In this work, an agroclimatic monitoring system for greenhouses composed of three main parts: monitoring stations, a wireless communication network, and a data processing and visualization platform is proposed. The aim of this work is to provide a technological solution based on the Internet of Things (IoT) that helps the agricultural sector to avoid crop losses due to climate change. The proposed system consists of several monitoring stations inside and outside the greenhouse, with multiple sensors to measure temperature, relative humidity, soil humidity, wind speed, wind direction, precipitation, radiation, pH, and electroconductivity. The information generated by the sensors is pre-processed and temporarily stored in the LinkIt Smart 7688 Duo microcontroller. Then, this information is sent via wireless through the inbuilt Wi-Fi module of the stations to the Thinger.io platform, where the data is processed, analyzed, and presented in real time in graphical form. Authorized users have access to this platform and can visualize the collected data from any electronic device with Internet access, following protocols to guarantee the security of the system. Using open hardware and open-source tools, and based on the IoT concept, a low-cost greenhouse monitoring system with six internal monitoring stations, one external monitoring station, and one portable monitoring station, that costs US, 180.00 was implemented. The results show that the total energy consumption of the system is approximately 20W, with a very good performance in terms of sampling time. This system was implemented in a 5000 square meter greenhouse with melon crops, where tests to evaluate the network performance within each zone in the greenhouse allowed us to consider WiFi technology to improve network coverage. Also, maximum, minimum, and average measurement values were evaluated to determine the critical levels recorded by the different sensors of agroclimatic variables during the period of study. The proposed system demonstrated to provide the agricultural sector with a low-cost, efficient, and easy-to-use tool to monitor agroclimatic variables in greenhouses that can help to prevent undesired climatic variations in advance, thus guaranteeing adequate conditions and reducing losses in production.

For a significant number of people with visual impairments, public transport plays an important role in productivity, community participation, and independence, since it may be the only feasible mobility option to participate in their education, work, medical care, food, and to attend many other places in their community. To use the public bus system safely, effectively, and autonomously, these people need to collect information about their physical environment and visible information at stops and terminals, such as timetables, routes, etc. Unfortunately, most people who are blind or visually impaired experience difficulties in getting on the right bus or getting off at the right destination. These situations usually force them to depend on other people that assist them in activities close to their homes, or settle for simpler jobs, or simply stay at home. Therefore, our efforts should aim to develop a system where technology is used to empower people with visual disabilities, allowing them to navigate autonomously in the public transport system. This paper presents a system based on radio frequency (RF) communication proposed within the framework of the MOVIDIS (Mobility for Visually Disabled People) research project (funded by the National Secretariat of Science, Technology and Innovation-SENACYT, under Grants No. 109-2015-4-FID14-073 and No. 99-2018-4-FID17-031), which provides an alternative to assist people with visual disabilities with their mobility in the public transport system. The various modules of this system communicate with each other by means of radio frequency and allow users to interact with buses and their respective stops. The first experimental results show that RF communication represents a viable option to help people with visual disabilities in public transport services.

This paper presents a study of the main contributions, in terms of the development of electronics and RFID technologies, to assist people with visual disabilities in their mobilization in indoor spaces. The main aspects taken into consideration when designing an assistive system for people with visual impairments for planning and execution of a movement from one place to another and/or for exploration of a site of interest autonomously are included. This study is important to identify the main technologies based on circuits and systems, electronics, ICT, and RF communication that have been proposed to help people with this type of disability in their indoor mobilization, as well as the techniques of location, navigation, orientation, and detection of objects, among other important functionalities for this type of systems. This work allowed us to propose the conceptual design of a novel system based on RFID and electronics technologies to help visually impaired people in their navigation in indoor environments, which will contribute to their social inclusion.

In recent years, the escalation of industrial activities has significantly increased natural resource pollution, with air pollution becoming a major cause of diseases affecting living organisms. To address this critical environmental challenge, this study proposes a comprehensive air pollution monitoring system utilizing advanced technological instruments based on the Internet of Things (IoT). The system’s primary objective is to provide precise, rapid, and efficient measurements, enabling detailed examinations of pollutant behaviors and facilitating data dissemination. The system includes a monitoring station equipped with sensors to measure ambient temperature, relative humidity, and concentrations of pollutants such as carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), suspended particles (PM_2.5, PM₁₀), and ozone (O₃). Additionally, it captures meteorological variables like wind speed, wind direction, and precipitation, allowing a nuanced analysis of their correlation with air pollutants. The collected data are transmitted via the Internet and visualized on a user-friendly platform accessible from any internet-enabled device. A protective case, designed with SolidWorks CAD software and fabricated using 3D printing, was validated through simulations for extreme conditions to ensures the system’s robustness in tropical climates. The cost-effective, low-energy system offers a scalable solution for monitoring air pollution, advancing understanding of pollutant behaviors, and supporting environmental management.