This work presents the design and implementation of an IoT-based system that monitors the groundwater from the water table in a farming area in Argentina. Particularly, the system measures in real-time the fluctuations in the depth and quality of groundwater, and it represents a proof-of-concept led by the National Institute of Agriculture Technology (INTA). In this first stage, the system allows INTA to create a sandbox to both understand the behavior of groundwater and determine appropriate ways to deploy the monitoring system in semi-arid areas that produce crops. The management of soil water is mandatory to ensure the subsistence of life in the planet. It is particularly important in a climate change scenario, where water resources are becoming increasingly scarce. For that reason, several research projects are currently under development to understand the water table fluctuations, and based on it, to implement preparedness plans for particular areas. To the best of the authors knowledge, this is the first monitoring system of this kind that is being developed and deployed in the southern cone of Latin America. It is expected that the system allows farmers and government authorities to perform a more efficient soil water management and forecasting, reducing thus the uncertainty about the capability of particular areas to produce crops.
As driver assistance technologies become increasingly autonomous and pervasive, there is a growing demand for co-pilot systems that are not just technically sophisticated but also adaptable to individual drivers and dynamic situations. Modern Advanced Driver Assistance Systems (ADAS) fall short of offering personalized and context-sensitive support, leading to low user uptake and effectiveness. This article offers a conceptual review of new directions in intelligent driver assistance based on advances in human–machine interaction, behavior modeling, and affective computing. We outline the main shortcomings of current solutions and suggest a framework of design principles to shape the next generation of co-pilot systems. These principles are focused on continuous personalization, real-time contextual sensitivity, proactive assistance, and user-oriented interaction and are aimed at fostering a more empathic and adaptive driving experience. Our contribution is intended to guide the development of co-pilots that learn with users, improve safety and comfort, and fit into the vision of human-centered mobility systems, while also outlining directions for empirical validation and ethical deployment.
Technological advancements are opening new opportunities for managing chronic diseases such as diabetes. Current systems enable real-time glucose monitoring, but recent studies show that incorporating physiological and activity variables can improve glucose prediction. This work presents the architecture and implementation of an IoT infrastructure that combines glucose monitoring with physiological data from a sports wristband, together with contextual events such as carbohydrate intake and insulin administration. Glucose values are obtained through the FreeStyle Libre 2 sensor and the DiaBox app, while a custom Android application collects wristband data. All information is transmitted in real time via MQTT for structured storage. This infrastructure provides a unified dataset of glucose, physiological, and contextual variables, laying the foundation for the development of AI-based predictive systems to improve diabetes management.
There are several scenarios where the number and location of nodes in a network of meteorological stations may not be sufficient to obtain the information needed. An interesting alternative is the generation of virtual meteorological stations by applying a model that allows the limited information available to be used to estimate meteorological variables in areas where installing a physical station is not feasible. This paper presents a quantum computing-based model for generating virtual nodes in a network of meteorological stations. The properties of the generated virtual nodes are based on the estimation of meteorological variables from scarce or incomplete information from nearby nodes. The preliminary results of the software prototype that was built are presented. The quality of the estimate is assessed by comparing the figures produced by the model with control data from real historical sources.
This work presents an indoor localization system based on Ultra-Wide Band technology, enhanced with a semantic inference layer through graph-based modeling. These systems, focused on the person using them, have high applicability in the context of the Internet of Things and the Internet of Everything, as they provide not only precise location but also contextual information through reliable room identification. The proposed methodology models the monitored space as an undirected graph, where the nodes represent rooms defined by bounding boxes, and the edges indicate physically valid transitions. This improves spatial coherence and reduces erroneous room changes. The solution was validated in the SmartLab of the University of Jaén, achieving a 94.08
