Surface subsidence caused by underground coal mining poses significant risks to nearby communities, public infrastructure, agricultural land, forest areas, and the surrounding environment. In India, subsidence monitoring is still largely dependent on conventional field observations, periodic surveys, and post facto damage assessments, which often fail to provide timely warning before critical ground failure occurs. There is a strong need for an indigenous, low cost, intelligent, and real-time mo
The problem envisages to build an AI-enabled smart mine subsidence monitoring and early warning system based on a localized wireless surface mesh sensor network deployed above underground mine panels.
Surface subsidence caused by underground coal mining poses significant risks to nearby communities, public infrastructure, agricultural land, forest areas, and the surrounding environment In India, subsidence monitoring is still largely dependent on conventional field observations, periodic surveys, and post facto damage assessments, which often fail to provide timely warning... There is a strong need for an indigenous, low cost, intelligent, and real-time mo
The problem envisages to build an AI-enabled smart mine subsidence monitoring and early warning system based on a localized wireless surface mesh sensor network deployed above underground mine panels. The proposed solution involves installing a distributed network of low cost smart sensor nodes across the surface over the underground mining area. Each node may be equipped with sensors such as: • tilt/inclination sensors, • vibration sensors, • displacement/stretch sensors, • crack detection sensors, • optional low cost positioning modules. These nodes will communicate through a wireless mesh communication network (such as LoRa/Zigbee/Wi-Fi mesh), enabling continuous real-time monitoring of micro ground movements over the mine panel. The system should continuously detect: • abnormal