Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

The crowd sensing technology can realize the sensing and computing of people, machines, and environment in smart industrial IoT-based coal mine, which provides a solution for safety monitoring through distributed intelligence optimization. However, due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines, the accuracy of human body position prediction and pressure value prediction is not high. To solve these problems, this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine. First, we propose a Particle Swarm Optimization-Elman Neural Network (PE) algorithm for the mobile human position prediction. Second, we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them, our proposed PE algorithm has the lowest average cumulative prediction error, and the trajectory fit rate is improved by 24.1%, 13.9% and 8.7% compared with Kalman filtering, Elman and Kalman plus Elman algorithms, respectively. Meanwhile, compared with single-input ARIMA, RNN, LSTM, and GRU, the RMSE values of our proposed ADI-LSTM are reduced by 36.6%, 52%, 32%, and 13.7%, respectively; and the MAPE values are reduced by 0.0003%, 0.9482%, 1.1844%, and 0.3620%, respectively.© 2015 Published by Elsevier Ltd.