(untitled) (International Journal of Engineering and Computer Science, 2016)
Coal mines are the great source of good for which we are highly dependent. It plays a vital role for the development of growth of nation. Here in this paper we are focusing over the safety prerequisite for the coal mines. As we know that now a day’s Wireless sensor networks (WSN) and the Modern Artificial Intelligence technique are good at security monitoring in coal mine. It is able to rapidly detect diverse parameters, which can reduce human and material losses. The Coal mine pivotal parameters include Dust Density (Dust), Temperature (Temp), Wind Speed (WindS), Gas Density (GasD) and Carbonic Oxide Density (COD). It is the most vital area of modern society which requires a proper safety prerequisite for mining. The data collected by the sensors are sent to the sink node to be processed with information fusion technology. This work presents a strategy for the classification of coal mine status based on sensed data by WSN and the use of unsupervised neural network-the Self-Organizing Map (SOM). The SOM application classifies the coal mine environment into four clusters. An experiment confirms the effectiveness of the proposed approach.