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灾后煤矿物联网网络空洞覆盖重构算法

胡青松 范莘舸 李鹤

胡青松,范莘舸,李鹤. 灾后煤矿物联网网络空洞覆盖重构算法[J]. 工矿自动化,2022,48(5):39-45.  doi: 10.13272/j.issn.1671-251x.17885
引用本文: 胡青松,范莘舸,李鹤. 灾后煤矿物联网网络空洞覆盖重构算法[J]. 工矿自动化,2022,48(5):39-45.  doi: 10.13272/j.issn.1671-251x.17885
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

灾后煤矿物联网网络空洞覆盖重构算法

doi: 10.13272/j.issn.1671-251x.17885
基金项目: 国家自然科学基金资助项目(51874299);山东省重大科技创新工程项目(2019JZZY020505);中国矿业大学“工业物联网与应急协同”创新团队资助计划项目(2020ZY002)。
详细信息
    作者简介:

    胡青松(1978-),男,四川岳池人,教授,博士,主要从事目标定位、矿山物联网和救灾通信方面的研究工作,E-mail:hqsong722@163.com

  • 中图分类号: TD655

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

  • 摘要: 灾后煤矿物联网因部分节点损毁或障碍物遮挡,会导致网络空洞问题,阻碍网络连通。现有网络空洞覆盖算法未考虑井下灾后地理环境因素,且未对修复后的网络进行优化,无法满足灾后煤矿物联网重构需求。针对该问题,提出了一种煤矿物联网灾后有障碍物情况下的网络空洞覆盖重构算法−NHCRA−O。建立了灾后煤矿物联网模型和节点感知模型,采用Delaunay三角剖分对网络中残存节点及障碍物角点进行区域划分,通过节点感知模型判断区域内是否存在网络空洞;计算Delaunay三角形质心位置,利用质心和Delaunay三角形顶点之间的距离确定虚拟修复节点位置;对虚拟修复节点和移动节点进行可视化判断,并基于距离因子和能量因子计算二者优先级,通过预剪枝操作删除部分计算结果来提高算法收敛速度,根据可视化判断结果和节点优先级进行虚拟修复节点和移动节点双向匹配,从而确定移动节点最终位置,完成网络空洞修复;融合剩余能量因子、节点连通度和方向介数计算节点优先级,根据优先级选举簇头节点,其他成员节点就近入簇,实现网络重构。采用Matlab2016a软件对NHCRA−O的节点匹配效率、网络覆盖效率和网络生存时间进行仿真研究,结果表明:NHCRA−O完成移动节点与虚拟修复节点匹配的次数较Gale−Shapley算法减少31.4%,网络覆盖率较C−V算法和PSO算法高且移动节点平均移动距离短,NHCRA−O重构的网络生存时间明显高于SEP算法和LEACH算法重构的网络。

     

  • 图  1  煤矿井下局部灾后物联网

    Figure  1.  Post-disaster Internet of things in coal mine underground

    图  2  物联网网络空洞

    Figure  2.  Network hole in Internet of things

    图  3  Delaunay三角形修复模型

    Figure  3.  Delaunay triangle repair model

    图  4  虚拟修复节点与移动节点匹配过程

    Figure  4.  Matching process between virtual repair node and mobile mode

    图  5  网络空洞修复前后节点位置分布

    Figure  5.  Node position distribution before and after network hole repair

    图  6  虚拟修复节点与移动节点匹配效率

    Figure  6.  Matching efficiency of virtual repair node and mobile node

    图  7  网络覆盖效率对比

    Figure  7.  Comparison of network coverage efficiency

    图  8  网络生存时间对比

    Figure  8.  Comparison of network time-to-live

    表  1  网络空洞覆盖重构仿真参数

    Table  1.   Simulation parameters of network hole coverage and reconstruction

    参数参数
    监测区域/(m×m)$ 150\times 100 $最小误警率0.05
    静态节点数20节点感知半径/m10
    移动节点数30节点通信半径/m20
    节点初始能量/J1能量阈值/J0.3
    信号衰减指数4仿真时间/s1 000
    自由空间功率放大
    系数/$(\mathrm{p}\mathrm{J}\cdot \mathrm{b}\mathrm{i}\mathrm{t}^{-1}\cdot { {\rm{m} } }^{-2})$
    10多径衰落空间功率放
    大系数/$( \mathrm{p}\mathrm{J}\cdot \mathrm{b}\mathrm{i}\mathrm{t}^{-1}\cdot { {\rm{m} } }^{-4})$
    0.0013
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-01-27
  • 修回日期:  2022-05-03
  • 网络出版日期:  2022-05-24

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