This study aims to develop a real-time hazardous noise monitoring and alert system using Internet of Things (IoT) technology, provide safety notifications via the LINE application when noise levels exceed 85 A-weighted decibels (dB(A)), and compare the continuous monitoring coverage of the proposed IoT system with traditional spot-measurement methods. The methodology involved the installation of an ESP8266 microcontroller integrated with an industrial-grade RS485 noise sensor (SN-3002-ZS) in the vicinity of a grinding machine within a sugar factory.
A linear transformation compensation algorithm was applied to correct calibration offsets, ensuring measurement integrity. Data samples were continuously collected at 10-second intervals over a 20-day period during the sugarcane milling season, incorporating distance variation tests ranging from 10 to 100 meters. All acquired data were transmitted via a Pocket Wi-Fi network to a cloud-based platform (Google Sheets) for storage and data processing. The results indicated that the system successfully recorded a total of 99,974 data sets. The maximum noise level detected was 91.54 dB(A), with an overall average  of 74.51 dB(A). Ultimately, the developed IoT system effectively addresses the limitations of traditional, discontinuous measurement methods by enabling 24-hour continuous monitoring, thereby providing a practical and robust tool for safety officers to efficiently manage hazardous zones.

Keywords

Internet of Things (IoT), Noise Monitoring System, Sugar Factory, RS485 Sensor, Real-time Alert.