An Optimized IoT-Based Coal Mine Safety Monitoring System with Edge-Driven Real-Time Hazard Detection

Abstract: Coal mining remains among the most dangerous occupations in the world, and the hazards underground are unlike those in virtually any other industry (Kowalski-Trakofler et al. 2011). Miners routinely work in the presence of toxic gases that build up silently in pockets of still air, in temperatures amplified by geothermal heat and continuous machinery operation, and in tunnels where radio signals fade and wired infrastructure is fragile at best. Underground conditions can deteriorate from normal to life-threatening in a matter of seconds, which leaves almost no margin for error in how the safety system responds. Despite improvements in wireless technology and cloud platforms, most current monitoring systems still rely on offloading decisions to remote servers and that round-trip delay is difficult to justify when the stakes are human lives.

This paper describes a monitoring system built around a different philosophy. Instead of centralizing the decision-making in the cloud, we move it onto the microcontroller itself, so sensor readings are evaluated and acted on before any network communication takes place. Gas levels and temperature are sampled continuously, and when a reading crosses a safety threshold, the device responds on its own: the buzzer goes off, the relevant LED lights up, and the event gets written to local storage. Cloud transmission is secondary — it happens after the local response, not as a precondition for it. The result is a system that reacts faster, draws less power, and keeps working normally even when connectivity is lost entirely.

Keywords: Internet of Things (IoT); Coal Mine Safety; Edge Computing; Gas Detection; Temperature Monitoring; Event-Driven Systems; Embedded Systems; Real-Time Monitoring; Industrial Safety.