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Article

ProtoCalib: Interactive Kit for Sensor Monitoring and

Evaluation in Educational IoT Environments

ProtoCalib: Kit interactivo para monitoreo y evaluación de

sensores en entornos IoT educativos

Saúl Antonio Pérez Pérez ¹

and Yamith Romero Aldana ^1∗

1

Faculty of Engineering, Universidad Autónoma del Caribe, Barranquilla, 080001, Colombia; yamith.romero@uac.edu.co;

saul.perez@uac.edu.co

∗

Correspondence: yamith.romero@uac.edu.co

Citation: Pérez, S.; Romero, Y. . ProtoCalib: Interactive Kit for Sensor Monitoring and Evaluation in Educational IoT Environments.

OnBoard Knowledge Journal 2025, 1, 6. https://doi.org/10.70554/OBJK2025.v01n01.03

Received: 20/04/2025, Accepted: 14/05/2025, Published: 26/06/2025

DOI: https://doi.org/10.70554/OBJK2025.v01n01.03

Abstract: This paper presents ProtoCalib, a modular and interactive kit designed for the monitoring, calibration, and

evaluation of sensors in educational environments based on the Internet of Things (IoT). The proposal addresses the need

to strengthen practical engineering training through accessible, replicable, and connected tools, integrating elements of

hybrid simulation and rapid prototyping. The system supports multiple categories of sensors (temperature, gases, angle,

distance, contact, speed, and color/light) and is structured in independent modules connected to an ESP32 platform

programmed in MicroPython, linked to a desktop application developed with PySide6 for real-time data visualization

and logging. Its architecture enables signal analysis and calibration through linear regression algorithms, as well as data

export for advanced analysis. The open-source and low-cost design facilitates adoption in academic contexts, fostering

the practical teaching of sensing, instrumentation, and IoT concepts. Validation was carried out in collaboration with

the Mechatronic Engineering Research Group (GIIM), ensuring the system’s reliability and pedagogical relevance. As a

result, ProtoCalib stands as a tool that integrates hardware, software, and active learning, promoting the development

of competencies in digital instrumentation and strengthening the connection between theory and practice in higher

education.

Keywords: Evaluation; IoT; Monitoring; Prototyping; Sensors

Resumen: Este artículo presenta ProtoCalib, un kit modular e interactivo orientado al monitoreo, calibración y evaluación

de sensores en entornos educativos basados en Internet de las Cosas (IoT). La propuesta responde a la necesidad de

fortalecer la formación práctica en ingeniería mediante herramientas accesibles, replicables y conectadas, integrando

elementos de simulación híbrida y prototipado rápido. El sistema soporta múltiples categorías de sensores (temperatura,

gases, ángulo, distancia, contacto, velocidad y color/luz) y se estructura en módulos independientes conectados a una

plataforma ESP32 programada en MicroPython, enlazada a una aplicación de escritorio desarrollada con PySide6 para

visualización y registro de datos en tiempo real. Su arquitectura permite el análisis y calibración de señales mediante

algoritmos de regresión lineal, así como la exportación de datos para análisis avanzado. El diseño open-source y de

bajo costo facilita su adopción en contextos académicos, fomentando la enseñanza práctica de conceptos de sensado,

OnBoard Knowledge Journal 2025, 1, 6.

https://revistasescuelanaval.com/obk/

Licensed by Escuela Naval de Cadetes "Almirante Padilla", COL.

This article is freely accessible and distributed under the terms and conditions

of Creative Commons Attribution (https://creativecommons.org/licenses/by/4.0/).

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instrumentación e IoT. La validación se realizó junto al Grupo de Investigación en Ingeniería Mecatrónica (GIIM),

garantizando la conﬁabilidad del sistema y su pertinencia pedagógica. Como resultado, ProtoCalib se consolida como

una herramienta que integra hardware, software y aprendizaje activo, promoviendo el desarrollo de competencias en

instrumentación digital y fortaleciendo la conexión entre teoría y práctica en la educación superior.

Palabras clave: Evaluación; IoT; Monitoreo; Prototipado; Sensores.

1. Introduction

In electronics-related degree programs, the study of sensors and actuators reveals a critical need to

integrate theory and practice within dynamic IoT environments. The gap between academic knowledge

and industrial requirements remains a signiﬁcant challenge, particularly in regions with limited access to

advanced technology. Industry 4.0, driven by automation and digitalization, demands skills in sensor moni-

toring and analysis. According to a recent study on IoT applications in smart education, these technologies

enhance student engagement and practical application [3].

Industry 4.0, characterized by the convergence of automation, digitalization, and connectivity, requires

professionals capable of integrating physical and cyber systems, interpreting real-time data, and operating

technologies based on the Internet of Things (IoT). In this context, technological literacy and competencies

in sensor monitoring and analysis stand out among the fastest-growing skills, as reported by the World

Economic Forum [7].

At the same time, emerging pedagogical approaches, such as IoT-based education and digital ex-

periential learning, promote environments in which hybrid simulation and rapid prototyping foster the

understanding of physical phenomena through interaction with real and virtual data.

In response to these academic and industrial needs, ProtoCalib is introduced as a modular and interactive

kit designed for sensor monitoring and calibration in IoT-based educational contexts. The system combines

hybrid simulation, modular hardware, and open-source software to support experimental practices in both

in-person and remote settings. Built on rapid prototyping platforms (ESP32, MicroPython) and integrating

visualization through applications developed in Python with PySide6, ProtoCalib offers an accessible,

replicable, and adaptable learning experience across various engineering disciplines.

Validated in collaboration with a specialized research group, the kit aims to strengthen hands-on

learning in sensors and instrumentation and contribute to bridging the gap between theory and application

in institutions transitioning toward Industry 4.0 and Education 5.0 ecosystems.

This article is organized as follows: Section 2 presents the main contributions of the work. Section 3

reviews related literature. Section 4 explains the methodological approach used for the design and validation

of the system. Section 5 presents the results and discussion derived from the implementation and testing of

ProtoCalib. Finally, Section 6 outlines the conclusions and future research directions.

2. Contributions

The main contributions of this work are summarized as follows:

i.

Development of an open-source educational kit (ProtoCalib): A modular and low-cost platform for

monitoring, calibration, and evaluation of various types of sensors in IoT-based environments.

Integration of hybrid simulation and real hardware interaction: Enables students to transition seam-

lessly between virtual testing and physical experimentation, reducing risks and enhancing practical

understanding.

ii.

iii.

Implementation of an accessible data acquisition architecture: Combines MicroPython on the ESP32

microcontroller with a PySide6 desktop application for real-time visualization, calibration using

regression algorithms, and data export.

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iv.

v.

Validation through academic collaboration: Experimental testing conducted in collaboration with the

Mechatronics Engineering Research Group (GIIM) ensured the technical reliability and pedagogical

relevance of the system.

Educational impact: ProtoCalib enhances active learning in engineering education by bridging the

gap between theoretical knowledge and hands-on experience in sensor instrumentation.

3. Related Works

The development of didactic tools for teaching sensing and automation has evolved signiﬁcantly in

recent years, driven by the convergence of Industry 4.0 and Internet of Things (IoT) environments. The need

to strengthen practical training in engineering has promoted the creation of platforms that integrate accessible

hardware, wireless connectivity, and digital simulation, fostering active learning and the acquisition of

technical competencies.

Various international initiatives, such as the Mechatronics Course at the Massachusetts Institute of

Technology (MIT), have demonstrated the effectiveness of interactive laboratories and the integration of

software and hardware for learning mechatronic systems [

6]. Complementarily, Laboratory Exercises

in Mechatronics by Jouaneh [ ] offers a hands-on approach that combines theory and practice through

4

progressive exercises covering sensing, data acquisition, and control.

In Latin America, projects such as the ESP32- and Alexa-based home automation test bench developed

by Álvarez Saltos and Loor Torres [

the potential of IoT as a low-cost educational resource, promoting autonomy and remote interaction in

laboratory environments. Similarly, didactic modules developed under CDIO standards and described by [

5] at the Universidad Politécnica Salesiana of Ecuador have highlighted

5]

propose a replicable methodology for teaching basic electronics, aligned with the principles of active learning

and competency-based assessment [1].

In the Colombian context, signiﬁcant experiences have been developed in the ﬁeld of educational

mechatronics, including the modernization of laboratory test benches at the Universidad Autónoma del

Caribe and the implementation of microcontroller-based practices for sensor calibration [2]. However, these

solutions present limitations in terms of interoperability, standardization, and remote connectivity key factors

for adaptation to IoT environments.

Within this context, ProtoCalib emerges as an innovative proposal that extends previous approaches

by integrating hybrid simulation, wireless communication, and an open modular architecture, enabling

accessible, safe, and scalable laboratory practices. Its open-source design addresses the need to democratize

access to technological tools in educational settings with limited resources, contributing to the development

of practical competencies in sensing and digital instrumentation.

4. Methodology

An agile approach with iterative phases was applied, inspired by software development practices

adapted to the design of educational hardware. The methodological process combined Design Thinking,

progressive prototyping, and principles of Design Science Research (DSR), ensuring traceability across the

stages of analysis, design, implementation, and validation.

The development cycle comprised three main phases: requirements analysis, system design, and

prototyping and integration. This approach enabled the construction of a ﬂexible and scalable solution,

prioritizing no-code/low-code tools that democratize access to technological development and reduce

technical barriers in educational contexts.

The methodology emphasized modularity from the early stages, ensuring that each system component

remained compatible with IoT standards and facilitating a smooth transition between simulation and

physical deployment. This strategy aimed to minimize integration errors and optimize the hands-on learning

experience.

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4.1. Requirements Analysis

A systematic literature review was conducted on IoT-based educational environments and remote

engineering laboratories, complemented by qualitative consultations with instructors and students. From

this process, key needs were identiﬁed: modularity in both hardware and software, compatibility with

commonly used sensors (temperature, gas, distance, light), real-time data acquisition and visualization, and

multiplatform accessibility for in-person or remote learning environments.

As a result, technical speciﬁcations were established with a focus on interoperability, using standard pro-

tocols such as MQTT and REST, and usability, through intuitive graphical interfaces and open documentation,

ensuring replicability across different academic settings.

4.2. System Design

The system was structured under a hybrid architecture, integrating virtual simulation with plug-and-

play physical hardware. UML diagrams and data ﬂow models were used for conceptual modeling, enabling

a clear deﬁnition of interactions among system components.

Universal interfaces (REST APIs) were implemented to support communication between the simulation

environment and the microcontroller, while MQTT protocols were adopted for lightweight data transmission

in low-latency environments.

The design ensured scalability through decoupled modules that can be replaced without global redesign;

security, via lightweight encryption (AES-128) applied to educational data transmitted over Wi-Fi; and

compatibility with simulation tools such as Node-RED or open-source IoT emulators, facilitating virtual

testing prior to physical assembly.

This phase also addressed technical sustainability aspects, prioritizing the use of accessible materials

and low-cost components.

4.3. Prototype and Integration

The initial prototypes were built using low-cost components such as the ESP32 and low-power sensors,

starting with validations on breadboards and evolving toward 3D-printed modules to improve durability.

The microcontroller ﬁrmware was developed in MicroPython, with adaptable conﬁgurations for differ-

ent sensor types and support for remote data acquisition.

In addition, a Data Acquisition Application (DAQ) based on web frameworks was developed, enabling

real-time visualization of variables through local and remote web browsers (Figure 1).

Figure 1. Main application window.

Source: The authors.

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During the testing iterations, interoperability between virtual and physical environments was veriﬁed

by adjusting parameters such as sampling rate, network latency, and concurrent processing on the microcon-

troller. These tests were conducted through collaborative debugging cycles with the participating research

group, incorporating continuous improvements in accuracy, stability, and user experience.

The process culminated in a validated functional assembly, ready for expansion into diverse educational

contexts and adaptable to future advanced sensing modules (Figure 2).

Figure 2. Physical sensor modules.

Source: The authors.

5. Results

The development of ProtoCalib made it possible to validate a modular and scalable model for hands-

on teaching of sensing in Internet of Things (IoT)-based educational contexts. The results are presented

across three dimensions: technical system performance, educational validation, and impact discussion in

comparison with existing solutions.

5.1. Technical Performance

The ﬁnal kit integrates an ESP32 microcontroller programmed in MicroPython and connected to a

desktop application developed in Python (PySide6), ensuring wireless communication via WiFi TCP/IP.

During testing, the average sampling rate remained stable between 0.8 Hz and 1.2 Hz for analog sensors, and

up to 5 Hz for digital readings, which proved sufﬁcient for laboratory practices without packet loss.

The physical modules were manufactured using FDM 3D printing, facilitating sensor replacement

and reconﬁguration. Compared to previous designs such as SensoraCore, ProtoCalib demonstrated a 27%

reduction in assembly time and a 35% increase in WiFi connection stability, attributed to improvements in

ﬁrmware thread management.

The calibration system incorporated linear and polynomial regression algorithms, achieving a root

mean square error (RMSE) below 2% with respect to reference standards, conﬁrming the technical validity of

the procedure. Furthermore, interoperability with simulation platforms such as Node-RED and Blynk IoT

enabled the integration of remote practices, extending system usage beyond the physical laboratory.

Table 1 presents the modules validated during the experimental phase, along with the associated sensors

and the type of equation applied for calibration. These results conﬁrm the system’s modular ﬂexibility and

the correct correspondence between hardware and software.

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Table 1. Relationship between software modules and sensors

Software Module

Sensor

Applied Equation

Basic Angular Measurement

Proximity Detection

Ultrasonic Monitoring

Motion Analysis

Potentiometer

IR and Capacitive

Ultrasonic Sensor

Optical Encoder

Linear

Environmental Control

Light Evaluation

Remote IR Control

Thermistor and Gas Sensors

Photoresistor

Polynomial

Linear

Combined IR

Source: The authors.

5.2. Educational Validation

Pedagogical validation was conducted with students from the Mechatronics Engineering program and

faculty members of the GIIM research group. Perception surveys, performance rubrics, and time-on-task

records for practical activities were applied.

The results showed that 83% of the students considered ProtoCalib to facilitate the understanding of

calibration and characterization concepts, while 78% highlighted the possibility of autonomous learning

enabled by the visual environment and the documentation available in the open repository.

Field tests evidenced an average reduction of 40% in sensor conﬁguration errors compared to traditional

practices, attributable to module self-identiﬁcation support and immediate visualization of readings. Faculty

members also reported improved management of laboratory sessions, supported by automatic logs exportable

to Excel and session-based data traceability.

These ﬁndings are consistent with recent studies on active learning in IoT-based environments [1], where

the combination of physical hardware and digital simulation increases conceptual retention and student

motivation (Figure 3).

Figure 3. Tests performed by students of the Mechatronics Engineering program.

Source: The authors.

5.3. Discussion

The results conﬁrm that ProtoCalib meets the principles of interoperability, accessibility, and scalability

deﬁned during the design stage. Unlike closed or commercial-use platforms, the open-source nature of the

system enables its adaptation to a wide range of courses from sensing to automatic control without requiring

proprietary licenses.

From an educational perspective, the project’s hybrid approach integrates three complementary dimen-

sions:

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i.

ii.

Connected instrumentation: real-time data acquisition and analysis with immediate visual feedback.

Virtual simulation: safe testing prior to physical sensor deployment, reducing the risk of damage and

material consumption.

iii.

Active and collaborative learning: students actively participate in calibration, interpretation, and

system improvement, strengthening experimental engineering competencies.

These results reinforce the potential of ProtoCalib as a replicable ecosystem for institutions with limited

resources, contributing to the reduction of technological gaps in engineering education. Additionally, the

project consolidates a line of applied research in educational mechatronics laboratories, with future prospects

for integrating data analytics and predictive maintenance through machine learning techniques.

6. Conclusions

The development of ProtoCalib demonstrates that the integration of modular hardware and hybrid sim-

ulation environments can signiﬁcantly strengthen the teaching of sensing and instrumentation in engineering

programs. Its architecture, based on open-source platforms (ESP32, MicroPython, and Python/PySide6),

enabled the creation of an accessible and adaptable monitoring and calibration system aligned with the

demands of Industry 4.0 and Education 5.0.

From a technical perspective, the prototype achieved levels of stability and accuracy comparable to

more complex systems, validating the use of lightweight protocols such as MQTT and REST in educational

applications. The system’s modular structure facilitates sensor replacement and expansion toward new

measurement categories without signiﬁcant redesign, highlighting its potential as a scalable platform for

low-cost laboratories.

In the pedagogical domain, ProtoCalib promoted active and contextualized learning by allowing

students to participate in the complete calibration cycle, from data acquisition to visual analysis and result

export. The obtained results showed improvements in conceptual understanding, technical autonomy, and a

reduction in operational errors during experimental practices.

Furthermore, the project consolidated a replicable model of educational innovation by combining

accessibility, open documentation, and IoT connectivity. This approach represents a viable reference for

institutions seeking to modernize hands-on engineering education strategies without relying on costly or

closed infrastructures.

In future phases, the incorporation of machine learning for predictive sensor diagnostics is envisioned,

along with expanded interoperability with cloud platforms and the development of multiplatform versions

integrating interactive web interfaces. In this way, ProtoCalib is positioned as an evolving ecosystem that

links academic training with emerging technological competencies, contributing to the digital transformation

of engineering education.

Author Contributions: Saúl Pérez: Conceptualization, Methodology, Supervision, Writing – review & editing, Project

administration, Resources, Validation. Yamith Romero: Conceptualization, Methodology, Software, Investigation, Formal

analysis, Data curation, Validation, Visualization, Writing – original draft.

All authors have read and agreed to the published version of the manuscript. Please refer to the CRediT taxonomy for the

deﬁnitions of the terms. Authorship is limited to those who have made substantial contributions to the reported work.

Funding: This research received no external funding.

Institutional Review Board Statement: Not applicable, since the present study does not involvehuman personnel or

animals.

Informed Consent Statement: This study is limited to the use of technological resources, so nohuman personnel or

animals are involved.

Conﬂicts of Interest: Under the authorship of this research, it is declared that there is no conﬂict of interest with the

present research.

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References

1. Aldana Gutiérrez, J. A., Alzate Plazas, S. L., Romero Cuero, E., and Campo Muñoz, W. Y. (2018). Desarrollo de

módulos didácticos de electrónica básica bajo los estándares 5 y 6 del cdio. Revista Espacios, 39(2).

2. Baños Manchego, L. F. and Arteta Padilla, D. A. (2022). Actualización de bancos de prueba de laboratorio de control

de la universidad autónoma del caribe. Institutional Digital Repository.

3. Hasan, D. (2024). Iot-based smart education: A systematic review of the state of the art. Accessed via ResearchGate.

4. Jouaneh, M. (2025). Laboratory Exercises in Mechatronics. Cengage Learning, 3 edition.

5. Loor Torres, A. P. and Álvarez Saltos, H. X. (2021). Development of an iot educational test bench using esp32 and

alexa.

6. Massachusetts Institute of Technology (2014). Mechatronics course (2.737): Labs and projects. MIT OpenCourseWare.

7. World Economic Forum (2025). The future of jobs report 2025.

Authors’ Biography

Saúl Antonio Pérez Pérez Full-time professor at the Universidad Autónoma del Caribe

Yamith Romero Aldana Mechatronics Engineering student at the Universidad Autónoma del

Caribe

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