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.

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.

Agriculture in the Tropical region, which includes Panama and other countries in the Caribbean and Latin America, has been developed using traditional tools with very little technology that has been designed and imported from countries with different environmental conditions. Some studies have suggested that the misuse of these tools may cause a negative impact on food production systems. Therefore, it is important to study, develop, and implement technological solutions that are appropriate for the conditions of specific regions and that help mitigating problems related to climate change and the production of food. One important solution is the implementation of technologies that interact with each other through the use of communication networks in Agriculture, known as “Smart Farming”. Many developed countries have promoted Research and Development (R&D) in Smart Farming, providing great benefits to their food production sector. The objective of this work is to document and provide an overview of the current situation of the Agriculture in Panama and the opportunities and challenges in the study, development, and implementation of Smart Farming as a technological solution in the agricultural sector in this country, in order to achieve an intelligent agriculture that allows a modern and sustainable food production. Finally, this paper provides some recommendations for the successful implementation of Smart Farming in Panama.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms, with tremors being one of the most prominent. Traditional assessment methods, such as the Unified Parkinson’s Disease Rating Scale (UPDRS), rely on subjective, intermittent evaluations, which can miss symptom fluctuations. This study presents the development and validation of the ELENA system, an IoT-based wearable device designed for the continuous monitoring of tremors in PD patients and medication tracking in PD patients. Named in honor of a 67-year-old woman who has lived with Parkinson’s since 2011 and inspired the project, the ELENA system integrates an MPU6050 accelerometer, an ESP32 microcontroller, and cloud-based data analysis and MATLAB. The ELENA system was calibrated and validated against an Apple Watch, demonstrating high accuracy with frequency deviations under 0.5% and an average percentage error of −0.37%. Unlike commercial devices, ELENA offers a clinical-grade solution with customizable data access and visualization tailored for healthcare providers. Participants, including PD patients and a non-PD control group, completed a series of clinical tasks to evaluate tremor monitoring capabilities. The results showed that the system effectively captured tremor frequency and amplitude, enabling the analysis of resting, action, and postural tremors. This study highlights the ELENA system’s potential to enhance PD management by providing real-time, remote monitoring of tremors. The scalable, cost-effective solution supports healthcare professionals in tracking disease progression and optimizing treatment plans, paving the way for improved patient outcomes.

Air pollution is a phenomenon that combines and rapidly spreads solid particles and gases in the air, being one of the great problems that affects the environment and health of living beings worldwide. Polluted air contains hazardous gases like carbon dioxide (CO2), nitrogen dioxide (NO2), sulphur dioxide (SO2), chloro-fluro-carbons, ozone (O3), and particulate matter (PM10, PM2.5). These pollution agents must be monitored and maintained within standards. One of the solutions currently implemented is the use of fixed monitoring stations that measure the concentration of pollutants. These stations normally operate using power from the electrical distribution network, which is not feasible when measurements are taken in remote locations. Another option is to use a battery bank to power the stations, which makes the system expensive, heavy, and dependent. Therefore, the main objective in this work is to propose an air pollution monitoring system that is powered by solar energy, which is an energy source abundant in nature in most countries. This prototype is implemented in project ITE18-R2-011: Monitoring network based on the Internet of Things for the generation of air pollution indicators in Panama, which is funded by the Panamanian Government and SENACYT. This would make the system cost-effective, compact, environmental-friendly, and autonomous in terms of its source. Design process starts with literature survey on conventional designs and solar cells. Additionally, there will be study on solar radiation in Panama. After that a PCB will be designed for this which can be optimized further for better efficiency in terms of power consumption.

As the market of electric vehicles is gaining popularity, large-scale commercialized or privately-operated charging stations are expected to play a key role as a technology enabler. In this paper, we study the problem of charging electric vehicles at stations with limited charging machines and power resources. The purpose of this study is to develop a novel profit maximization framework for station operation in both offline and online charging scenarios, under certain customer satisfaction constraints. The main goal is to maximize the profit obtained by the station owner and provide a satisfactory charging service to the customers. The framework includes not only the vehicle scheduling and charging power control, but also the managing of user satisfaction factors, which are defined as the percentages of finished charging targets. The profit maximization problem is proved to be NPcomplete in both scenarios (NP refers to “nondeterministic polynomial time”), for which two-stage charging strategies are proposed to obtain efficient suboptimal solutions. Competitive analysis is also provided to analyze the performance of the proposed online two-stage charging algorithm against the offline counterpart under non-congested and congested charging scenarios. Finally, the simulation results show that the proposed two-stage charging strategies achieve performance close to that with exhaustive search. Also, the proposed algorithms provide remarkable performance gains compared to the other conventional charging strategies with respect to not only the unified profit, but also other practical interests, such as the computational time, the user satisfaction factor, the power consumption, and the competitive ratio