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

作     者：Jing Zhang Qichen Yan Xiaogang Zhu Keping Yu Jing Zhang;Qichen Yan;Xiaogang Zhu;Keping Yu

作者机构：Department of Computer Science and TechnologyXi'an University of Science and TechnologyXi'an710054China Department of Information Science and TechnologyNorthwest UniversityXi’an 710127China School of Public Policy and AdministrationNanchang UniversityNanchangJiangxi330031China Graduate School of Science and EngineeringHosei UniversityTokyo184-8584Japan RIKEN Center for Advanced Intelligence ProjectRIKENTokyo103-0027Japan 

出 版 物：《Digital Communications and Networks》 (数字通信与网络（英文版）)

年 卷 期：2023年第9卷第2期

页      面：296-305页

核心收录：

学科分类：0810[工学-信息与通信工程] 080904[工学-电磁场与微波技术] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 080402[工学-测试计量技术及仪器] 0804[工学-仪器科学与技术] 081001[工学-通信与信息系统] 

基　　金：supported in part by the National Natural Science Foundation of China(Grant No.61902311),in part by the Postdoctoral Research Foundation of China(Grant No.2019M663801) in part by the Scientific Research Project of Shaanxi Provincial Education Department(Grant No.22JK0459) Key R&D Foundation of Shaanxi Province(Grant No.2021SF-479) in part by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)under Grant JP18K18044 and JP21K17736 

主　　题：Crowd sensing Industrial Internet of things Safety monitoring Coal mine 

摘      要：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 ***,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 *** solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal ***,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position ***,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground *** 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,***,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.