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基于刚分簇与鸡群优化的深井无线传感网络定位算法

余修武 周利兴 余齐豪 胡沐芳 张枫

余修武, 周利兴, 余齐豪, 胡沐芳, 张枫. 基于刚分簇与鸡群优化的深井无线传感网络定位算法[J]. 西南交通大学学报, 2019, 54(4): 870-878. doi: 10.3969/j.issn.0258-2724.20180069
引用本文: 余修武, 周利兴, 余齐豪, 胡沐芳, 张枫. 基于刚分簇与鸡群优化的深井无线传感网络定位算法[J]. 西南交通大学学报, 2019, 54(4): 870-878. doi: 10.3969/j.issn.0258-2724.20180069
YU Xiuwu, ZHOU Lixing, YU Qihao, HU Mufang, ZHANG Feng. Localization Algorithm for Mine Wireless Sensor Network Based on Rigid Cluster and Chicken Swarm Optimization[J]. Journal of Southwest Jiaotong University, 2019, 54(4): 870-878. doi: 10.3969/j.issn.0258-2724.20180069
Citation: YU Xiuwu, ZHOU Lixing, YU Qihao, HU Mufang, ZHANG Feng. Localization Algorithm for Mine Wireless Sensor Network Based on Rigid Cluster and Chicken Swarm Optimization[J]. Journal of Southwest Jiaotong University, 2019, 54(4): 870-878. doi: 10.3969/j.issn.0258-2724.20180069

基于刚分簇与鸡群优化的深井无线传感网络定位算法

doi: 10.3969/j.issn.0258-2724.20180069
基金项目: 湖南省重点研发计划资助项目(2018SK2055);金属矿山安全与健康国家重点实验室开放基金资助项目(2016-JSKSSYS-04);中华人民共和国应急管理部安全生产重特大事故防治关键技术科技项目(hunan-0001-2018AQ)
详细信息
    作者简介:

    余修武(1976—),男,教授,博士,研究方向为无线传感器网络、智能安全监控,E-mail:yxw2008xy@163.com

  • 中图分类号: TP393.1

Localization Algorithm for Mine Wireless Sensor Network Based on Rigid Cluster and Chicken Swarm Optimization

  • 摘要: 针对矿井环境因素对无线传感器网络定位的制约,提出一种基于刚性分簇与鸡群优化的无线传感器网络定位算法(RCCSO). 首先,以传感网络中均匀分布的锚点为簇头,基于刚性图理论提出分簇算法对整个网络进行分簇并保证每个簇都是全局刚性的;其次,利用鸡群算法对簇内进行相对定位,求得簇内最优相对位置解集;再次,不同簇以锚点为旋转中心旋转不同角度,并利用鸡群算法求出旋转角度的最优解集,进而求得全局节点最优位置;最后,仿真结果显示,与多维标度MDS-MAP算法及自适应局部区域循环搜索DALSA相比,所提算法在精度上有较明显的提高.

     

  • 图 1  矿井无线传感器网络监测模型

    Figure 1.  Model of mine monitoring by WSN

    图 2  可变形、一般刚性和全局刚性示意

    Figure 2.  Diagram of deformable graph,local rigid graph,and globally rigid graph

    图 3  Bounding-box示意

    Figure 3.  Diagram of bounding-box

    图 4  相对位置与实际位置关系

    Figure 4.  Relation of relative position and real position

    图 5  不同鸡总数下最优适应值随迭代次数变化

    Figure 5.  Best optimization values with the change of iterations in different pop

    图 6  最优适应度值随鸡总数变化趋势

    Figure 6.  Best optimization values with the change of Npop

    图 7  定位误差随锚点比例变化趋势

    Figure 7.  Error of localization with the change of proportion of anchor nodes

    图 8  定位误差随测距误差变化趋势

    Figure 8.  Error of localization with the change of error of distance measured

    表  1  仿真参数

    Table  1.   Parameters of simulation

    符号含义设定值
    L1/m巷道 1 长度200
    L2/m巷道 2 长度100
    W/m宽度5
    N节点总数量60
    m/N锚点比例0.1~0.3
    R/m通信半径25
    IRSS(d0)/dBmd0 = 1 m 处 IRSS 值–45
    ηIRSS 测距参数4
    σ1IRSS 实际偏离标准差0~5
    tmax最大轮次100
    Npop鸡群中鸡的总数量50~3 000
    NR公鸡比例0.2
    NH母鸡比例0.4
    NC小鸡比例0.4
    NM鸡妈妈比例0.4
    εRCCSO 设定参数0.001
    $\phi $RCCSO 设定参数0.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-01-23
  • 修回日期:  2018-08-10
  • 网络出版日期:  2018-09-05
  • 刊出日期:  2019-08-01

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