IOT BASED SMART HELMET FOR COAL MINE SAFETY MONITORING

This paper has presented the design, implementation, and evaluation of an IoT-based Smart Helmet system for real-time coal mine safety monitoring. The proposed system successfully integrates MQ2 and MQ7 gas sensors for methane and carbon monoxide detection, a DHT11 sensor for temperature and humidity monitoring, and a tilt sensor for accident detection, all controlled by an ESP32 microcontroller and supported by LoRa, GSM, and Wi-Fi communication modules. Experimental results demonstrate that the system achieves reliable and accurate detection of hazardous environmental conditions, with gas detection accuracy exceeding 92%, accident detection accuracy of 98%, and a system response time of less than 500 milliseconds. The LoRa communication module ensures reliable data transmission over distances up to 800 metres in underground tunnel environments, addressing a key limitation of previous wearable safety systems. The dual-layer alert mechanism, combining local buzzer alerts with remote SMS and cloud notifications, ensures that both the worker and supervisors are notified promptly when unsafe conditions are detected. Overall, the proposed IoT-based smart helmet system represents a significant advancement over traditional mine safety monitoring approaches, offering comprehensive, real-time, personalised safety monitoring in a compact and practical wearable form factor. The system has the potential to substantially reduce the incidence of accidents and fatalities in underground coal mining operations. Several directions for future work are identified to further enhance the capabilities and applicability of the proposed system. First, the integration of artificial intelligence and machine learning algorithms for predictive safety analysis and anomaly detection will enable the system to forecast hazardous conditions before they reach critical levels, allowing even earlier intervention. Deep learning models trained on historical sensor data could identify patterns associated with precursor events to gas leaks, temperature spikes, or structural instability. Second, edge computing techniques can be implemented within the ESP32 to reduce dependence on cloud connectivity and enable autonomous local decision-making. This would be particularly beneficial in deep underground sections where communication links may be intermittently unavailable. Third, integration with a real-time indoor positioning system (IPS) would enable the control room to track the precise location of each worker within the mine, facilitating targeted emergency response and evacuation coordination. Fourth, incorporation of additional sensors such as dust concentration sensors, noise level monitors, and oxygen level sensors would provide more comprehensive environmental monitoring. Fifth, development of a dedicated mobile application for supervisors and safety officers would enhance the usability and accessibility of the monitoring platform. Finally, large-scale field deployment and validation in an active coal mine environment will be conducted to further verify system performance under real-world operating conditions and to refine the system based on practical feedback from mine workers and safety professionals.

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