Volume 48 Issue 5
May  2022
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Article Navigation >  Journal of Mine Automation  > 2022  >  48(5): 39-45
HU Qingsong, FAN Xinge, LI He. Network hole coverage reconstruction algorithm for post-disaster coal mine Internet of things[J]. Journal of Mine Automation，2022，48(5)：39-45.  doi: 10.13272/j.issn.1671-251x.17885
Citation: HU Qingsong, FAN Xinge, LI He. Network hole coverage reconstruction algorithm for post-disaster coal mine Internet of things[J]. Journal of Mine Automation，2022，48(5)：39-45.  doi: 10.13272/j.issn.1671-251x.17885
shu

Network hole coverage reconstruction algorithm for post-disaster coal mine Internet of things

doi: 10.13272/j.issn.1671-251x.17885
  • 1.

    Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou 221116, China

  • 2.

    School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

  • 3.

    Xuzhou Engineering Research Center of Intelligent Industry Safety and Emergency Collaboration, China University of Mining and Technology, Xuzhou 221116, China

  • 4.

    Xuzhou Combustion Control Research Institute Co., Ltd., Xuzhou 221000, China

  • Received Date: 2022-01-27
  • Rev Recd Date: 2022-05-03
    • Available Online: 2022-05-24
    Abstract
  • Due to the damage of some nodes or obstacles in the post-disaster coal mine Internet of things, network hole would appear to hinder network connectivity. The existing network hole coverage algorithm does not consider the geographical environment factors after the underground disaster, and does not optimize the repaired network. Therefore, the algorithm cannot meet the reconstruction requirements of the post-disaster coal mine Internet of things. In order to solve this problem, this paper proposes a network hole coverage reconstruction algorithm with obstacles, NHCRA-O, for post-disaster coal mine Internet of things. The post-disaster coal mine Internet of things model and the node perception model are established. Delaunay triangulation is used to divide residual nodes and corner points of obstacles in the network. The node perception model is used to judge whether there is a network hole in the area. The centroid position of the Delaunay triangle is calculated. The distance between the centroid and the vertex of the Delaunay triangle is used to determine the virtual repair node position. The virtual repair node and mobile node are visualized, and the priority of both is calculated based on distance factor and energy factor. The pre-pruning operation is used to delete some calculation results to improve the convergence speed of the algorithm. According to the visual judgment result and node priority, the virtual repair node and the mobile node are matched in both directions. Therefore, the final position of the mobile node is determined and the network hole repair is completed. The node priority is calculated by fusing the residual energy factor, node connectivity and directional betweenness. The cluster head node is elected according to the priority, and other member nodes join the cluster nearby to realize network reconstruction. Matlab2016a software is used to simulate the node matching efficiency, network coverage efficiency and network time-to-live of NHCRA-O. The results show that the number of times that NHCRA-O completes the matching of mobile nodes with virtual repair nodes is 31.4% less than that of Gale-Shapley algorithm. The network coverage is higher than the C-V algorithm and the PSO algorithm, and the average moving distance of the mobile nodes is shorter. The network time-to-live reconstructed by NHCRA-O is significantly longer than that reconstructed by SEP algorithm and LEACH algorithm.

     

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