AI-Enabled IoT Smart Air Safety System for Kitchens and Elderly People with Anosmia

Gas leakages in household kitchens pose a consistent threat, especially among elderly people and people with anosmia, a disease that adversely affects their sense of smell. Traditional monitoring mechanisms are limited to basic trigger-based actions and local alerts using buzzers. They lack in intelligent capabilities such as connectivity that would enable them timely and reliable responses. This study focuses on artificial intelligence-enhanced IoT-enabled Smart Air Safety System that continually detects the concentration of gas , temperature and humidity levels using a MQ135 gas sensor and DHT11 sensor is connected to NodeMCU (ESP8266). The detection-based filter algorithm differentiates the status of the environment into three categories: safe, moderate, and danger while it minimizes false alarms that result in sudden bursts of the sensors' outputs. During emergencies, the smart system automatically controls the activation of the exhaust fan using a servo motor, activates buzzer and LED alarms, and pushes notifications through the Blynk IoT platform. A web dashboard integrated within the ESP8266 web server shows live data from the sensors and generates analytics. Results show correct categorization of multiple environmental states, successful false alarm prevention, and less than one-second delay in response time.