Office Air Quality Monitoring with IoT
Technologies: Raspberry Pi 3B+, Bosch BME280, DFRobot CO2 Sensor, ADS1015, Node-RED, MQTT Mosquitto, MySQL, Python
The Challenge
Indoor air quality has a direct impact on the health, well-being, and productivity of office employees. Elevated CO₂ levels, improper humidity, or temperature fluctuations are factors that reduce concentration and increase absenteeism, and their effects often go unnoticed until a real problem emerges.
The client who approached us wanted full control over the conditions in their office. They needed a tool for continuous measurement of three parameters: temperature, humidity, and carbon dioxide levels. Speed of delivery was equally important, they couldn’t afford to wait months for a ready-made product.
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Approach and Solution
Our priority was to deliver a working prototype as quickly as possible. We decided to use off-the-shelf sensor modules already available in our lab, which allowed us to skip the lengthy component sourcing phase entirely.
For temperature, humidity, and pressure measurements, we used the Bosch BME280 sensor. CO₂ levels were measured with the DFRobot CO2 Sensor, connected to an ADS1015 ADC module by Adafruit. As the central unit, we chose Raspberry Pi 3B+ — a reliable and flexible platform that allowed us to bring the entire environment up and running quickly.
To keep the system architecture straightforward, we ran Node-RED as the integration environment on the Raspberry Pi, alongside the MQTT Mosquitto broker for device communication and a MySQL database for storing measurement history. All other devices simply published data via the MQTT protocol to the central unit.
Sensor data was transmitted in JSON format using a simple Python script. In Node-RED, we defined a flow that received the data, stored it in the database, and displayed it on a real-time dashboard. We also configured an input data preview module, enabling ongoing verification that our data format was being decoded correctly.
Results
The project turned out to be remarkably fast to deliver. Setting up the entire system, getting it running, and collecting the first data took just a few hours.
The client received a complete air quality monitoring tool with access to real-time charts and the ability to export data from any time period. The system is scalable, additional sensors can be added without reworking the architecture.
The project confirmed that rapid prototyping with off-the-shelf modules makes it possible to deliver real value in a fraction of the time that building from scratch would require.
Want to build a similar solution?
Do you have an unusual idea or a technical challenge that cannot be solved with an off-the-shelf product? Get in touch with us. We like projects that do not fit into standard frameworks.
Want to build a similar solution?
Do you have an unusual idea or a technical challenge that cannot be solved with an off-the-shelf product? Get in touch with us. We like projects that do not fit into standard frameworks.
