• Manoj.M
• Jabelson J bose
• Benhail .B

Mining continues to be a dangerous industry despite its tremendous contribution, with workers at risk from gas leaks, equipment problems, and rockfalls. The system constantly keeps an eye on worker health, equipment performance, and ambient factors by utilizing a network of strategically positioned sensors. It uses cutting-edge algorithms to detect possible threats instantly, allowing for proactive risk assessment and anomaly identification. This may lower the number of accidents and fatalities by enabling quick actions like alarms, equipment shutdowns, or rescue missions. Interestingly, our methodology includes This strategy has a number of benefits. In the first place, it eliminates the need to transfer substantial amounts of unprocessed sensor data to centralized computers for processing. This process can be resource-intensive and prone to delays, particularly in isolated mining sites with spotty internet. Secondly, edge ML processes data locally.

Real-time data analytics System integration ,safety decision support, Environmental sensors, Acoustic and seismic sensors, wearable sensors

"IOT MINING WORKERS SAFETY SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.b858-b866, April-2024, Available :http://www.jetir.org/papers/JETIR2404198.pdf

"IOT MINING WORKERS SAFETY SYSTEM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppb858-b866, April-2024, Available at : http://www.jetir.org/papers/JETIR2404198.pdf