Case Studyedge deploymentair qualityesp32linear regression
IoT Node Predicts Next AQI Value
6.0
A developer built an IoT node using an ESP32 and sensors (PMS7003, MQ135, MQ7, DHT11) to monitor PM2.5, PM10, NO2, CO, temperature and humidity in real time and predict the next AQI using a linear regression model trained on ~2,000 samples. The system streams sensor data over UDP to a local Python app and dashboard; the model achieves mean absolute error 19.704 and the dashboard computes AQI using CPCB breakpoints.
Key Points
- 1Implements multi-pollutant sensing with PMS7003, MQ135, MQ7, DHT11 on ESP32, streaming data via UDP
- 2Uses linear regression on ~2,000 samples with MAE 19.704, showing viable low-compute forecasting
- 3Enables local real-time AQI prediction and dashboarding for edge deployment and low-resource monitoring
Scoring Rationale
Practical open-source edge AQI forecasting with available code and dashboard, limited by simple linear model and ~2,000-sample dataset.
Sources
Public references used for this report.
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