Francisco Luque

Design of a Versatile Custom IoT System Integrable in Industrial and Residential Environments

This project focuses on innovation and flexibility, with the development of a comprehensive IoT system from scratch, capable of operating efficiently in both industrial and home environments. The proposal is based on a modular and scalable architecture, aiming to facilitate the integration of multiple protocols, devices, and technologies, ensuring adaptability to various usage scenarios.

From the early stages, the design considered the incorporation of open and widely adopted technologies within the IoT ecosystem. The system is built using Docker containers and orchestrated with Kubernetes, enabling efficient management, scalability, and automated deployments across different platforms. The automation and visualization logic was implemented using Node-RED, allowing for agile development and visually accessible flows for complex data streams.

Device and service communication is structured through messaging protocols such as MQTT, enabling lightweight and real-time transmission. Additionally, support for widely used industrial protocols like BACnet, Modbus, and KNX was integrated, facilitating interoperability with existing infrastructures in industrial facilities.

The system supports both commercial and DIY custom-built devices, including those connected via Zigbee, generic IoT sensors and actuators, and tailor-made nodes for specific situations.

Once development was completed, the system was deployed in two real-life environments: one industrial—with machinery, environmental sensors, and building controllers—and one residential—including smart lighting, climate control, and energy monitoring. Tests in both settings demonstrated solid performance, high stability, and notable integration capabilities, validating its versatility and efficiency.

Home IoT Dashboard

Industrial IoT Dashboard

This project was presented as my Master's Final Project in Industrial Engineering. You can find more information by clicking the button below.

View Project on GitHub

Design and Implementation of Electrical Installation Modifications

Throughout my professional career, I have carried out various modification and improvement projects on electrical installations, both in rural areas and private homes. These tasks required a comprehensive technical approach, from the initial assessment to the final design and execution, always ensuring regulatory compliance and installation safety.

In rural areas, I have performed conversions from single-phase to three-phase installations, which involved planning and executing new power supply lines, as well as designing and assembling protection panels tailored to the new energy demands. These projects required a detailed analysis of the expected load, coordination with the utility company, and the correct selection of protections, cable sizes, and control components.

In domestic environments, my work has included the complete renovation of electrical panels, updating residual current and thermal protections in accordance with current regulations (REBT), as well as renewing internal wiring in homes to meet new demands such as kitchen electrification, climate control systems, charging points, or expanding circuits for lighting and power outlets.

The entire process was personally designed, assessing the specific needs of each installation, creating the electrical diagrams, selecting the appropriate materials, and carrying out the installation to the highest standards of quality and safety. Special attention was also given to the organization, labeling, and accessibility of the electrical panels to facilitate future expansions or maintenance work.

Industrial Automation Project

I participated in the design and implementation of a comprehensive production line automation project in an industrial environment, aimed at increasing operational efficiency, reducing human error, and optimizing cycle times.

The project included the installation and commissioning of industrial robots (manipulators and articulated arms), automated conveyor systems, and a network of presence, proximity, temperature, and counting sensors strategically distributed along the line.

My work covered both the design phase (electrical schematics, sensor layout, and control logic) and the physical installation of the system, including wiring, integration of PLCs and control systems, and configuring communication between devices using industrial protocols such as Modbus and Ethernet/IP.

In addition, I collaborated on programming the control logic and performing functional tests to ensure proper synchronization between the various system components (robots, conveyors, sensors, and actuators), ensuring smooth and safe operation.

This project significantly improved line productivity, reduced downtime, and enabled more accurate process traceability.

Design and Implementation of AI Systems

I participated in the implementation of an Artificial Intelligence system aimed at optimizing production processes through machine learning, advanced data analysis, and predictive maintenance. The main goal was to improve operational efficiency, reduce unplanned downtime, and anticipate critical equipment behavior.

From the outset, I worked on the systematic collection of data from industrial sensors, PLCs, and existing SCADA systems. These data included variables such as temperature, vibration, energy consumption, work cycles, and more. Once the database was consolidated, I developed machine learning models that enabled pattern detection, failure prediction, and evidence-based decision-making.

One of the project's major achievements was the deployment of predictive maintenance algorithms that anticipated mechanical failures with an accuracy rate above 90%. This led to a significant reduction in unplanned stoppages, better maintenance resource planning, and extended asset lifespan.

Additionally, smart dashboards and visualization panels were implemented to provide operations teams with real-time system status, early alerts, and automated recommendations based on continuous data analysis. This was achieved through the integration of real-time data processing tools, combining technologies such as Python, TensorFlow, Pandas, and time-series databases.

Another key aspect was the design of a robust, horizontally scalable data pipeline. I used Docker containers and Kubernetes orchestration to ensure high availability and ease of service updates. Furthermore, the models were trained and versioned using tools like MLflow, enabling traceability and continuous improvement.

The implementation experience was both enriching and challenging. I confirmed that integrating AI into industrial processes is not only feasible but also highly profitable when executed with a well-structured strategy. Thanks to this project, the organization transitioned from a reactive approach to a predictive, data-driven one, enhancing decision-making at all levels.