Research on the monitoring and alarm devices for smoking in student dormitories

Due to the privacy nature of student dormitories, video surveillance cannot be implemented. Smoking inside the dormitory is an important factor causing air pollution and fire hazards. Currently, there is no good solution to this problem. To address this issue, this study focuses on detecting key para-meters such as CO, TVOC, and PM2.5. It uses the KNN algorithm for multi-sensor data fusion to achieve smoking behavior warnings and alarms. The relevant data can be uploaded to the cloud platform using LoRa wireless technology for inspection by management personnel. The data can also provide strong sup-port for fire prevention. The smoking monitoring and alarm system studied in this paper can effectively monitor the air quality inside the dormitory, detect smoking behavior on time, and prevent fires and other safety accidents while protecting students' privacy. This system has high practical value and bro-ad application prospects.