Precision local positioning mechanism in underground mining using IoT-enabled WiFi platform

被引:11
作者
Mohapatra A.G. [1 ]
Keswani B. [2 ]
Nanda S. [3 ]
Ray A. [4 ]
Khanna A. [5 ]
Gupta D. [5 ]
Keswani P. [6 ]
机构
[1] Department of Electronics and Instrumentation Engineering, Silicon Institute of Technology, Bhubaneswar, Odisha
[2] Department of Computer Application, Suresh Gyan Vihar University, Mahal Jagatpura, Jaipur
[3] Department of Computer Science Engineering, Centurion University of Technology and Management, Jatni, Odisha
[4] School of Computer Science, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha
[5] Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi
[6] Department of Computer Science and Engineering, Akashdeep PG College, Jaipur
关键词
GPS; IoT; Localization; RSSI; underground mining; WiFi;
D O I
10.1080/1206212X.2018.1551178
中图分类号
学科分类号
摘要
Internet of Things (IoT) connects many physical devices to many individuals from earth to space. This new concept covers almost all applications such as smart cities, industries, autonomous systems, and underground setups. This article summarizes the application of IoT-driven wireless nodes and IoT decision support platform in underground mining areas where location estimation of the miners is a critical task for the mining administrators. The underground mining accident is one of the most critical environmental disasters where life risk of the miners is a very crucial factor to be addressed. There are very few research work reported to find real-time location of the miner without any Global Positioning System (GPS) and mobile signals. This work demonstrates an efficient local positioning mechanism where low-cost and high battery capacity WiFi nodes are used to find the location of the miner during crude environmental conditions. The whole local positioning system works on the basis of Receiver Signal Strength Indicator (RSSI) value of the miner nodes. This article also demonstrates the useful technique to increase the battery life more than one year. Apart from all the above technological uses, it has been proposed that the localization algorithm can be used as potential technological key for local positioning in underground mines. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.
引用
收藏
页码:266 / 277
页数:11
相关论文
共 54 条
  • [1] Potvin Y., Hudyma M.R., Seismic monitoring in highly mechanized hard rock mines in Canada and Australia, Int Symp Rock Burst Seism Mines, pp. 267-280, (2011)
  • [2] Dong L.J., Wesseloo J., Yves P., Et al., Discrimination of mine seismic events and blasts using the Fisher classifier, Naive Bayesian classifier and logistic regression, Rock Mech Rock Eng, 49, pp. 183-211, (2016)
  • [3] Dong L.J., Wesseloo J., Potvin Y., Et al., Discriminant models of blasts and seismic events in mine seismology, Int J Rock Mech Min Sci, 86, pp. 282-291, (2016)
  • [4] He H., Dou L.M., Cao A.Y., Et al., Mechanisms of mining seismicity under large scale exploitation with multikey strata, Shock Vibr, pp. 1-9, (2015)
  • [5] Zhao Y., Yang T.H., Zhang P.H., Et al., The analysis of rock damage process based on the microseismic monitoring and numerical simulations, Tunnell Undergr Space Technol, 69, pp. 1-17, (2017)
  • [6] Lia Y., Hua Z., Hua Y., Et al., Integration of vision and topological self-localization for intelligentvehicles, Mechatron, 51, pp. 46-58, (2018)
  • [7] Mohapatra A.G., Keswani B., Lenka S.K., ICT specific technological changes in precision agriculture environment, Int J Comput Sci Mobile Appl, 6, pp. 1-16, (2018)
  • [8] Mohapatra A.G., Keswani B., Lenka S.K., Neural network and fuzzy logic based smart DSS model for irrigation notification and control in precision agriculture, Proc Natl Acad Sci, pp. 1-10, (2018)
  • [9] Mohapatra A.G., Keswani B., Lenka S.K., Optimizing Farm Irrigation mechanism using feedforward neural network and structural similarity index, Int J Comput Appl, 4, 7, pp. 135-141, (2017)
  • [10] Mohapatra A.G., Keswani B., Lenka S.K., Soil n-p-k prediction using location and crop specific random forest classification technique in precision agriculture, Int J Adv Res Comput Sci, 8, pp. 1-6, (2017)