IoT-Driven Safety And Environmental Monitoring System For Coal Mining Workers
Keywords:
IoT, Coal Mine Safety, Zigbee, Worker Safety, Environmental Monitoring, Gas Detection, Temperature and Humidity Monitoring, Vibration Detection, Pulse Sensor, ThingSpeak, Cloud Computing, Real-Time Monitoring, Wearable Sensors, Emergency Alert System, Underground MiningAbstract
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 Zigbee 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 Zigbee, 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 is 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, Zigbee, and cloud-based analytics. We plan to build upon these results with AI-based predictive analytics and edge computing for optimum performance and scalability.
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