SENSEnuts IoT platform and Bayes decision theorem based mine control system

Abstract

Life can be saved from the hazardous accidents happening in mines due to firedamp, dust explosion, and gassy environment. Existing mine environment monitoring systems are not only costly but also need optimization for quality of services, real-time monitoring, management, and information sharing. Thus, this paper is presenting an automated real-time monitoring, alarming, and information sharing system that will help to avoid such accidents. This system consists of a combined mechanism of SENSEnuts internet of things (IoT) stack platform, cloud server, and data analytics to detect, predict, and monitor real-time danger of mine. This system generates alert and shares information with the concerned official for taking immediate preventive measures. The proposed system has been tested in simulated coal mine environments and results show high accuracy, sensitivity, and accurate prediction of hazard due to CO, CO2, CH4, dust, and smoke.