AI System for Automatic IoT Sensor Calibration
Sensors drift: temperature, gas, pressure, humidity — all require periodic calibration. Manual calibration with thousands of sensors is unrealistic. AI system automatically detects drift and performs soft-calibration without technician visits.
Types of Sensor Drift
Zero drift: constant reading offset. Gain drift: change in sensitivity coefficient. Cross-sensitivity drift: reaction to external factors (temperature affecting humidity sensor).
Auto-Calibration Methods
Cross-Sensor Calibration: Multiple sensors of same type in comparable conditions. Statistical outlier detection. Sensor significantly differing from neighbors — drift candidate. Automatic offset correction.
Temporal Self-Calibration: Sensor measures own baseline in known conditions (night, equipment off) → comparison with expected → correction.
Reference-Based: Periodic comparison with reference measurements (weather station for external conditions, lab standard for industrial).
Physics-Informed ML: Sensor physics model + neural network for drift component prediction. Training on dataset of known drifts.
Architecture
IoT devices → MQTT → Edge aggregator → Drift Detection ML → Calibration Parameters → OTA device config update.
Limitations
Soft (software) calibration corrects offset and gain. Physical sensor degradation (contamination, mechanical wear) can't be fixed — system marks sensor for replacement.