Reasearch on the data levels fusion of mine safe monitoring the perception of Internet of Things

Abstract：With respect to the complexity and uncertainty in coal mine safety monitoring, Internet of Things （IoT） per- ception was used in the safety monitoring system. Distributed Star-shaped Wireless Sensor Network （DSWSN） was con- structed in perception layer of IoT and the data levels fusion algorithm for perceiving coal mine safety in application layer of IoT was studied in depth. Dynamic amplitude limiting filtering algorithm, which was combined with confidence distance measure and data timestamp was used to pretreat data for elimination of any blunder errors. Optimal weighted estimation algorithm was applied to complete data level fusion, without requiring any priori knowledge of sensor＇ s measurement data. According to self-correlation and cross-correlation estimations of sensor variances, the fusion values with minimum mean square errors and meeting unbiassedness requirements were obtained. The simulation results show that the algorithm is characterized with rational weight distribution, stable absolute error fluctuations, sound dynamic response characteristics, fast convergence speed and the ability to effectively filter out interference data. Such results have demonstrated its rationality and strong robustness and can satisfy safety monitoring requirements.