International Journal of Science, Engineering and TechnologyAuthors: Shivani Nandu Shinde, Bangar Abhishek Popat, Bhosale Sachin Bajirao
Abstract: Underground mines are hard places to keep safe. The air can turn dangerous without any smell or warning sign, communication is patchy, and help is usually a long way off. Over the last few years many research groups have tried to close this gap by combining low-cost sensors, wireless links and machine learning. This paper reads four recent studies from 2026 side by side: a LoRa-based gas monitoring node with Random Forest analysis, a wearable wristband and belt hub with edge intelligence, a Random Forest gas classifier with a Python dashboard and cloud alerts, and a large integrated smart-mining case that joins electrification, safety analytics, biodiversity and accounting. We compare their sensing, communication, analytics and alerting layers, and we recompute several figures directly from the numbers the authors report. Our reading is that the hardware ideas are sound and increasingly affordable, but the evidence behind them is thin. Datasets are rarely described, labels are often derived from the same thresholds the model is meant to improve on, and underground field trials are scarce. We close with a layered reference framework and a short list of practical recommendations for future prototypes.
