Conference Agenda

The Internet of Things (IoT) is a new field that has already affected many aspects of people's lives. It is already happening in households, healthcare providers, smart cities, the energy sector, agriculture, and more. Nonetheless, people do not always accept it with open arms. There are debates on whether it has positively or negatively impacted people's lives. This article avoids taking a side and tries to explore different areas and ways in which IoTs have affected lives.

More specifically, it will explore reliability, security, privacy, interoperability, scalability, power consumption, cost-effectiveness, and user experience in the shade of different lights. All these aspects have both positive and negative effects, and this article tries to explore them objectively. It is not a complete analysis of all the possible consequences IoTs can have on people, but it serves as a survey on the matter. It is an informative article that leaves opinions to the reader and leaves the path open for others to explore.

In the face of the pressing water crisis affecting communities, an intelligent telemetric system is presented for monitoring, control, and fault detection in drinking water supply systems. This system integrates solenoid valves, water flow sensors, and water meters to efficiently manage real-time data within the pipeline. Teltonika RUT955 cellular routers were integrated for control, data transmission, and reception at designated points. Modbus was incorporated as a data conversion agent between the sensors and the router system. Subsequently, data is communicated via the MQTT protocol to a broker, where it is processed using JavaScript algorithms. Finally, the data is visualized on a dashboard with line charts, gauge charts, tables, and maps, facilitating further analysis. The project operates on solar energy for field devices, with power consumption not exceeding 12 volts per device. This approach considers the critical nature of data loss and the limited access to public energy supply available underground in communities. The simplified, precise, and versatile visualization interface was implemented using HTML in conjunction with CSS. Users have tools such as report generation and local data storage, all updated in real time. By identifying connection points for each device, sensors are visualized on a map, tracing their flow trajectory, calculating distances between pairs, and generating flow level states for each point.

This study develops an automated system for monitoring and controlling the exposure time to toxic gases in industrial environments, with the objective of ensuring occupational safety and reducing exposure risks, in compliance with OSHA/CAL regulations. The implementation is based on the PLC LOGO! V8.4, which allows efficient processing and control of gas sensors, and on MQTT communication, which enables real-time transmission of data to a monitoring platform in Node-Network. The system incorporates an intuitive interface with dynamic graphics, historical data logging and automatic notifications on WhatsApp and Remote-Red, facilitating monitoring on mobile devices. In addition, it integrates an adaptive control of exposure time, adjusting the dwell limits depending on the gas concentration detected.
The results obtained show that the system offers a fast response (less than 10 seconds) to the detection of dangerous concentrations, allowing the activation of corrective measures such as audible alarms and automated ventilation. Its implementation represents a robust and scalable solution to improve occupational safety in industrial environments.

The constant human pursuit of improving living conditions has driven a continuous evolution in housing solutions, with a focus on the use of IoT technologies for connectivity and intelligent control of household devices, as well as for automating routine tasks. This research compares the performance of two homes with identical dimensions: one built with a traditional approach in both design and selection of internal devices, and the other with a modern, smart approach incorporating inverter devices and IoT functionalities. The study results reveal that the smart home shows significant improvements in temperature control, natural ventilation, efficient lighting, and reduced energy consumption compared to the traditional home.

Workplace safety in industrial and construction environments is essential to prevent accidents and protect workers` health. The proper use of Personal Protective Equipment (PPE) is fundamental; however, manual supervision of its use is often inefficient. This study aims to implement a computational algorithm based on Convolutional Neural Networks (CNN) for PPE detection, using computer vision to identify in real time safety equipment such as helmets, vests and boots. A specific dataset was developed with over 2,000 images, and the model was implemented using the Roboflow platform. The best iteration of the network achieved a Mean Average Precision (mAP) of 91.1%, with an accuracy of 91% and a recall of 84.7%. These results highlight the potential of the model to improve the monitoring of compliance with safety standards at work, contributing to the reduction of occupational accidents. The methodology used offers an adaptable tool for monitoring the use of PPE, laying the groundwork for future studies that seek to optimize safety in different industrial sectors.

Z-Wave technology is widely used in smart home and building automation systems, especially in IoT equipment. However, it presents several security vulnerabilities that could jeopardize the integrity and privacy entrusted to us by users. Therefore, this systematic literature review (SLR) explores the main security risks associated with Z-Wave, as well as the protection measures proposed in the literature to mitigate these vulnerabilities. The review included 30 studies on Z-Wave implementation, security comparisons with other similar technologies (such as Zigbee and Wi-Fi), and evaluations of protection methods such as encryption, intrusion detection, and anonymous authentication. The findings highlight the need to strengthen Z-Wave security by developing advanced protection measures and constantly monitoring the network for intrusions in real time.