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邻近信息约束下的随机异构无线传感器网络节点调度算法

秦宁宁 金磊 许健 徐帆 杨乐

秦宁宁, 金磊, 许健, 徐帆, 杨乐. 邻近信息约束下的随机异构无线传感器网络节点调度算法[J]. 电子与信息学报, 2019, 41(10): 2310-2317. doi: 10.11999/JEIT190094
引用本文: 秦宁宁, 金磊, 许健, 徐帆, 杨乐. 邻近信息约束下的随机异构无线传感器网络节点调度算法[J]. 电子与信息学报, 2019, 41(10): 2310-2317. doi: 10.11999/JEIT190094
Ningning QIN, Lei JIN, Jian XU, Fan XU, Le YANG. Neighbor Information Constrained Node Scheduling in Stochastic Heterogeneous Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2310-2317. doi: 10.11999/JEIT190094
Citation: Ningning QIN, Lei JIN, Jian XU, Fan XU, Le YANG. Neighbor Information Constrained Node Scheduling in Stochastic Heterogeneous Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2310-2317. doi: 10.11999/JEIT190094

邻近信息约束下的随机异构无线传感器网络节点调度算法

doi: 10.11999/JEIT190094
基金项目: 国家自然科学基金(61702228),江苏省自然科学基金(BK20170198),雷达成像与微波光子教育部重点实验室开放基金(NJ20170001-7),江苏省博士后科研资助计划(1601012A),江苏省“六大人才高峰”计划(DZXX-026),中央高校基本科研业务费专项资金(JUSRP1805XNC)
详细信息
    作者简介:

    秦宁宁:女,1980年生,副教授,硕士生导师,研究方向为无线传感器网络及应用

    金磊:男,1993年生,硕士生,研究方向为无线传感器网络覆盖

    许健:男,1992年生,硕士生,研究方向为无线传感器网络覆盖

    徐帆:男,1988年生,讲师,硕士生导师,研究方向为信号处理与应用

    杨乐:男,1979年生,副教授,硕士生导师,研究方向为无线信号统计与应用

    通讯作者:

    秦宁宁 ningning801108@163.com

  • 中图分类号: TN929.5

Neighbor Information Constrained Node Scheduling in Stochastic Heterogeneous Wireless Sensor Networks

Funds: The National Natural Science Foundation of China (61702228), The Natural Science Foundation of Jiangsu Province (BK20170198), The Open Fund of Key Laboratory of Radar Imaging and Microwave Photonics of Ministry of Education (NJ20170001-7), Jiangsu Province Planned Projects for Postdoctoral Research Funds (1601012A), The Eleventh Batch High-level Talents Project of “Six Talent Peaks” in Jiangsu Province(DZXX-026), Fundamental Research Funds for the Central Universities (JUSRP1805XNC)
  • 摘要: 针对高密度部署的随机异构传感器网络内部存在的覆盖冗余问题,该文提出一种随机异构无线传感器网络的节点调度算法(NSSH)。在网络原型拓扑的支撑下构建Delaunary三角剖分,规划出节点进行本地化调度的局部工作子集。通过折中与邻近节点的空外接圆半径,完成对感知半径的独立配置;引入几何线、面概念,利用重叠面积和有效约束圆弧完成对灰、黑色节点的分类识别,使得节点仅依赖本地及邻居信息进行半径调整和冗余休眠。仿真结果表明,NSSH能以低复杂度的代价,近似追平贪婪算法的去冗余性能,并表现出了对网络规模、异构跨度和参数配置的低敏感性。
  • 图  1  节点${s_i}$的有效约束圆弧${\stackrel \frown {{\rm{ar}}{{\rm{c}}_i}}}$

    图  2  节点${s_i}$感知半径${r_i}$调整图

    图  3  节点感知重叠面积求解

    图  4  黑色节点的标识与计算

    图  5  不满足圆心$k$覆盖的节点约束圆弧图

    图  6  目标区域调整效果对比

    图  7  覆盖冗余度随节点个数和半径比变化情况

    图  8  多级异构网络中$\Delta F\;$的变化

    图  9  二级异构网络中$\Delta F\;$的变化

    图  10  同构网络中$\Delta F\;$的变化

    表  1  半径调整算法步骤

     半径调整算法(${T^i},{r_i}$)
     (1) $r_{\rm c}^i = \varnothing $
     (2) for $p = 1:P$
     (3)   calculate the radius $r_{{\rm{cp}}}^i$ of ${T_p}^i$//计算空外接圆半径
     (4)    $r_{\rm c}^i = r_{\rm c}^i \cup r_{ {\rm{cp} } }^i$
     (5) end for
     (6   ${r_i} = \min ({r_i},\max \left( {r_{\rm c}^i} \right))$//若$\max \left( {r_{\rm c}^i} \right) > {r_i}$,则保留原${r_i}$
     (7) return (${r_i}$)
    下载: 导出CSV

    表  2  NSSH算法步骤

     NSSH (${T^i}$,${r_i}$,${\rm{N}}{{\rm{e}}_i}$,${\theta _{{\rm{th}}}}$,${\left| {{\rm{arc}}} \right|_{{\rm{th}}}}$,$k$)
     (1) 初始化:${\rm{s}}{{\rm{t}}_i} = 1$, ${\stackrel \frown {{\rm{ar}}{{\rm{c}}_i}}}=\varnothing$
     (2) ${r_i}$=半径调整算法(${T^i},{r_i}$)
     (3) for $q = 1:Q$
     (4)  ${\rm{if}}({\theta _{i,jq}} > {\theta _{{\rm{th}}}})$) //基于定义5和式(1)判定灰色节点
     (5)   ${\rm{s}}{{\rm{t}}_i} = 0$
     (6)  end if
     (7)  ${\stackrel \frown {{\rm{ar}}{{\rm{c}}_i}}}={\stackrel \frown {{\rm{ar}}{{\rm{c}}_i}}} \cup {\stackrel \frown {{\rm{ar}}{{\rm{c}}_{i\_jq}}}}$ //计算有效约束圆弧
     (8) end for
     (9) calculate $\left| {{\rm{ar}}{{\rm{c}}_i}} \right|$ ${k_i}$ //基于式(2)—式(4)计算有效约束圆弧度
    数,统计${s_i}$的覆盖度${k_i}$
     (10) if ($\left| {{\rm{ar}}{{\rm{c}}_i}} \right| \ge {\left| {{\rm{arc}}} \right|_{{\rm{th}}}}{\rm{\& \& }}{k_i} \ge k$)//基于定义6判定黑色节点
     (11)  ${\rm{s}}{{\rm{t}}_i} = 0$
     (12) end if
     (13) return (${\rm{s}}{{\rm{t}}_i}$)
    下载: 导出CSV

    表  3  5种算法参数

    算法节点数目
    $N = {\rm{100}}$, $N = {\rm{150}}$$N = {\rm{200}}$, $N = {\rm{250}}$$N = {\rm{300}}$, $N = {\rm{350}}$$N = {\rm{400}}$, $N = {\rm{450}}$$N = {\rm{500}}$
    NSSH${\theta _{{\rm{th}}}} = 0.82$ ${\left| {{\rm{arc}}} \right|_{{\rm{th}}}} = 1.96{\text{π}} $ $k{\rm{ = 2}}$
    GGA$\mu {\rm{ = 0}}{\rm{.9}}$
    MCLC$\Delta c < 0.05$
    COAN${r_{{\rm{thr}}}} = 4.4$
    ${\eta _{{\rm{thr}}}} = 1.87{\text{π}} $
    ${m_{{\rm{thr}}}} = 11$
    $k = 1$
    ${r_{{\rm{thr}}}} = 4.6$
    ${\eta _{{\rm{thr}}}} = 1.9{\text{π}} $
    ${m_{{\rm{thr}}}} = 12$
    $k = 1$
    ${r_{{\rm{thr}}}} = 4.8$
    ${\eta _{{\rm{thr}}}} = 1.93{\text{π}}$
    ${m_{t{\rm{hr}}}} = 13$
    $k = 1$
    ${r_{{\rm{thr}}}} = 5.2$
    ${\eta _{{\rm{thr}}}} = 1.96{\text{π}} $
    ${m_{{\rm{thr}}}} = 13$
    $k = 1$
    ${r_{{\rm{thr}}}} = 5.4$
    ${\eta _{{\rm{thr}}}} = 1.98{\text{π}} $
    ${m_{{\rm{thr}}}} = 15$
    $k = 1$
    NDBS${k_2}({r_1}/{r_2}) = 4$
    ${k_2}({r_2}/{r_1}) = 7$
    ${k_3}({r_1}/{r_2}) = 7$
    ${k_3}({r_2}/{r_1}) = 16$
    ${k_2}({r_1}/{r_2}) = 4$
    ${k_2}({r_2}/{r_1}) = 8$
    ${k_3}({r_1}/{r_2}) = 8$
    ${k_3}({r_2}/{r_1}) = 16$
    ${k_2}({r_1}/{r_2}) = 4$
    ${k_2}({r_2}/{r_1}) = 7$
    ${k_3}({r_1}/{r_2}) = 8$
    ${k_3}({r_2}/{r_1}){\rm{ = 15}}$
    ${k_2}({r_1}/{r_2}) = 3$
    ${k_2}({r_2}/{r_1}) = 6$
    ${k_3}({r_1}/{r_2}) = 7$
    ${k_3}({r_2}/{r_1}) = 14$
    ${k_2}({r_1}/{r_2}) = 3$
    ${k_2}({r_2}/{r_1}) = 6$
    ${k_3}({r_1}/{r_2}) = 6$
    ${k_3}({r_2}/{r_1}) = 13$
    注:表中参数$\Delta c$代表MCLC算法覆盖子集的约束条件,${r_{{\rm{thr}}}}$, ${\eta _{{\rm{thr}}}}$, ${m_{{\rm{thr}}}}$仅针对COAN算法,${k_2}({r_1}/{r_2})$, ${k_2}({r_2}/{r_1})$, ${k_3}({r_1}/{r_2})$, ${k_3}({r_2}/{r_1})$仅针对NDBS算法,具体参数解释可查阅文献[8,9]。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-02-17
  • 修回日期:  2019-06-09
  • 网络出版日期:  2019-06-14
  • 刊出日期:  2019-10-01

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