Integration of ThingSpeak with MATLAB and Raspberry Pi

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Evaluación

Dificultad
2/5
Tiempo estimado
1-3 horas
Aptitud para principiantes
35/100
Tipo de issue
Documentación
Claridad
Bastante claro
Estado de actividad
Estancado
Stack tecnológico
matlab, raspberry-pi
Área
documentation, embedded-iot

Línea de trabajo

Read the attached PDF guide and the linked ThingSpeak integration example to understand how the Raspberry Pi, SDS011 sensor, ThingSpeak, and MATLAB fit together. The issue shares this material but does not specify a change or what completion would look like, so clarify the desired contribution before starting.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

We have prepared a guide on integrating a Raspberry Pi with ThingSpeak and MATLAB for real-time air quality monitoring using an SDS011 particulate matter sensor.

Integration_of_ThingSpeak_with_MATLAB_and_Rasberry_Pi.pdf

Code available with Example shared by Prof. Khan, but with ThingSpeak integration

We understand that many of you may already be familiar with this approach; we simply wanted to share it with those who might find it useful.

Key Advantage: No LAN Required

One practical benefit of this setup is that it eliminates the need to connect the Raspberry Pi to the same local network as your PC or laptop running MATLAB. By pushing data to ThingSpeak over the internet, the device can be placed anywhere, indoors, outdoors, outside your room, or even outside Parma, as long as it has an internet connection. MATLAB then retrieves the data from the cloud regardless of where either device is located.

👏 Acknowledgment

A sincere thank you to our professors @MarcoRianiUNIPR and @Asadunipr, their chapter "Data Collection with Mobile Sensors" from the book "Statistical Modelling with MATLAB" was invaluable in helping us achieve this.

Authors

We hope this can be helpful to current and future students working on IoT and data collection projects.

Lenguaje dominante
MATLAB
Estrellas
30
Forks
1
Sin PR fusionados en 30 d

Preparar el entorno

Este proyecto no incluye contenedor de desarrollo, Dockerfile ni guía de contribución, así que la configuración corre por tu cuenta: empieza por su README y consulta nuestra guía para la primera contribución para los pasos generales.

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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