Design and Development of a Helmet Drying and Sterilization System Using the Fuzzy Logic Method

This study aims to design and develop an IoT-based helmet drying and sterilization system utilizing the Mamdani fuzzy logic method to maintain helmet hygiene, reduce humidity, and inhibit the growth of harmful microorganisms. The system integrates a DHT22 sensor to measure temperature and humidity, an ESP32 microcontroller for data processing, a fan heater for drying, and a UV-C lamp for sterilization. To handle dynamic environmental conditions, the Mamdani fuzzy logic algorithm adaptively controls the actuator outputs based on sensor inputs. Additionally, the system connects to the Blynk IoT platform via Wi-Fi for real-time remote monitoring through a smartphone application. Testing demonstrated that the DHT22 sensor achieved high accuracy, recording 99.71% for temperature and 97.17% for humidity. The ESP32 efficiently processed the fuzzy logic algorithm with an average latency of 556 µs, while maintaining stable IoT communication up to a 40-meter radius. The fan heater successfully reached the target drying temperature of 45°C, and the UV-C lamp effectively operated within the germicidal spectrum to ensure proper sterilization. Overall, the system dynamically adjusted to varying conditions and successfully completed the drying process in approximately 1 hour and 3 minutes. In conclusion, the proposed system successfully automates helmet maintenance, proving highly effective in drying and sterilizing helmets. The integration of fuzzy logic and IoT technology ensures adaptive performance and reliable monitoring, thereby enhancing user health, safety, and comfort.