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Indoor air quality (IAQ) is a critical environmental health factor responsible for millions of cases of respiratory diseases each year. This study presents the design of an Internet of Things (IoT)-based indoor air circulation control system that integrates a BME680 sensor, an ESP32 microcontroller, and the Firebase Realtime Database platform with the Tsukamoto fuzzy logic algorithm as a control mechanism. This research implements a dual-core architecture on the ESP32, separating IoT communication tasks from sensor computation. This approach enables the BSEC algorithm to run consistently without interruption. The system uses two input variables (IAQ values and room temperature) as well as two actuators—a fan and a 12V DC blower—controlled via Pulse Width Modulation (PWM) signals based on 12 fuzzy rules. Testing was conducted in a miniature test chamber measuring 23.5 × 16 × 18 cm with controlled pollutant introduction. The research results show that the BME680 sensor has an average accuracy of 99.3% for temperature and 98.0% for humidity. All 12 fuzzy testing scenarios met the rule base criteria. The system was able to reduce the IAQ value from a very dirty condition (IAQ = 500) to a clean condition (IAQ ≤ 80) in an average time of 281 seconds. Data synchronization between the local LCD display and the Firebase-based mobile application achieved 100% accuracy. These results demonstrate that the Tsukamoto fuzzy method is effective in producing rule-based, stable, and proportional control for indoor air circulation systems.
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