This project presents the design and implementation of an IoT-based system for early detection of Liquefied Petroleum Gas (LPG) leaks in residential environments. Three functional prototypes were developed, integrating gas sensors, microcontrollers, actuators, and wireless modules. The system achieved 89.48% service availability and response times under 2 s from leak detection to data storage and visualization. Operating under a distributed architecture, it enables continuous monitoring, automatic shut-off, and real-time alerts. The novelty lies in the applied integration of electronics, embedded systems, and automation into an affordable, replicable solution, that enhances household safety, promotes preventive behavior, and supports the adoption of affordable, replicable technologies in vulnerable domestic settings.

Controlling the microclimate is vital in fruit fly breeding. This project develops an automated IoT system for monitoring and controlling temperature and relative humidity in chambers, optimizing processes through accessible and flexible technology. It uses a multi-layer system starting with the application layer with the ESP32 for data acquisition and actuator control. The second layer is the network layer, and the perception layer uses VisionFive2 with MQTT and HTTP protocols for communication, as well as NodeRed for flow orchestration and MySQL for data management and storage. In the validation, there are absolute errors of ±0.434 °C and ±0.5 RH, which are values within the acceptable ranges for these applications.

We present the design and implementation of a Supervisory Control and Data Acquisition SCADA web system of pollution factors such as Nitrogen Dioxide (NO ² ), Carbon Dioxide (CO ² ) and Carbon Monoxide (CO) in order to manage them, providing comfort in indoor environments. The SCADA web system allows a comparative analysis of the recorded information with the alert, alarm and emergency levels, established in the Ecuadorian air quality regulations, annex 4 of Book VI of the Unified Legislation of Secondary Legislation of the Ministry of the Environment., The SCADA web system performs the comparison of the registered information, with the physical agents of environmental comfort, established in the ASHRAE (American Society of Heating, Refrigerating, and Air-Conditioning Engineers) standards. In the process of stabilization of the pollution factors, in indoor environments, air ionizers are used, which emit negative ions to maintain the standardized range in micrograms per cubic meter (μg/m ³ ). Finally, we have been able to develop a SCADA web system for controlling pollution factors in indoor environments thanks to the integration of firebase (Android Studio), authentication (Facebook), notification (Database and Web Server), control and monitoring (Android Studio) interfaces. Besides, the SCADA web system allows preventing long term health problems such as allergic reactions, physical exhaustion and respiratory diseases, which influence academic performance during study days in educational institutions.

This work analyzes the results of the use of the algorithm GSP_M (Generalized Sequential Patterns + memory)for processing videos of surveillance in supermarkets, in order to identify the perpetration of thefts. It describes the theoretical framework of concepts used in video surveillance systems, their architecture, and, characteristics. It justified the use of sequential pattern identification algorithms as a tool to analyze human behavior in domains of shoplifting and mathematically define some concepts used by the tool. It describes the objectives of the application of video surveillance systems in supermarkets to minimize shoplifting, explaining that this is based on the fact that, monitored people have a behavior that can be characterized by a set of sequential actions, whose analysis allows obtaining conclusions to consider a person as a suspect, generating an alert or definitely an alarm. The GSP and GSP_M algorithms are described and compared. It details the procedure defined to evaluate the performance of the algorithm GSP_M in the domain of shoplifting, and, the comparison of the results obtained with the results of the GSP application.