IoT-Based LoRa-Enabled Safety Monitoring System for Coal Mining Workers

IoT-Based LoRa-Enabled Safety Monitoring System for Coal Mining Workers

연구 분야: Verification

학회: 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA)

The indispensable task of ensuring the safety of workers in coal mining is a crucial issue because of dangerous underground conditions such as toxic gas emissions, high temperatures and humidity, structural vibrations, etc. However, traditional monitoring technologies do not support real-time transmission, long-distance communication, and energy-efficient requirements, which cannot meet the needs of underground mining. In overcoming these shortcomings, this article proposes an IoT-based safety monitoring system equipped with LoRaWAN hardware, able to deliver continuous real-time monitoring of miners' environmental and physiological conditions. This contains three sensors; DHT11 (temperature & humidity), MQ2 (gas detection), SW-420 (vibration monitoring), and pulse sensor (heart rate tracking). These sensors are incorporated into a wearable device connected to a centralized control hub through LoRaWAN, ensuring long-distance, low-power, and anti-interference communication in underground scenarios. The data is uploaded to ThingSpeak, a cloud-based IoT analytics platform and real-time visualization, anomaly detection and historical trend analysis is done on the uploaded data. All sensor information are processed within the control unit and alert users as soon as possible as long as a measured parameter exceeds pre-defined limits. Also, the system can also track location in real time, helping emergency responders act quickly and rescue. Experimental results show that the system detects environmental hazards and worker health and anomalies, with low latency and high accuracy, with respect to the data transmission. This system improves the safety of workers, efficiency in operations, and preparedness for emergencies in the mining space by using the combination of IoT, LoRaWAN, and cloud-based analytics. We plan to build upon these results with AI-based predictive analytics and edge computing for optimum performance and scalability.