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

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

秦宁宁^1, ,,
金磊¹,
许健¹,
徐帆²,
杨乐³

1.
江南大学轻工过程先进控制教育部重点实验室无锡 214122
2.
南京航空航天大学雷达成像与微波光子教育部重点实验室南京 210016
3.
坎特伯雷大学电气与计算机工程系克赖斯特彻奇新西兰 8011

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
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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

Neighbor Information Constrained Node Scheduling in Stochastic Heterogeneous Wireless Sensor Networks

Ningning QIN^{1
, ,},
Lei JIN¹,
Jian XU¹,
Fan XU²,
Le YANG³

1.
Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China
2.
Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
3.
Department of Electrical and Computer Engineering, University of Canterbury, Christchurch 8011, New Zealand

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

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。
- 传感器网络 /
- 随机异构 /
- 覆盖 /
- 节点调度 /
- 邻近信息
Abstract: Considering coverage redundancy problem existed in random heterogeneous sensor networks with high density deployment, a Node Scheduling algorithm for Stochastic Heterogeneous wireless sensor networks(NSSH) is proposed. The Delaunary triangulation is constructed based on the network prototype topology to work out a local subset of nodes for localization scheduling. Independent configuration of the perceived radius is achieved by discounting the radius of the circumcircle with the adjacent node. The concept of geometric line and plane is introduced, and the overlapping area and the effective constrained arcs are used to classify and identify the grey and black nodes. So the node only relies on local and neighbor information for radius adjustment and redundant node sleep. The simulation results show that NSSH can approximately match the dropping redundancy of greedy algorithm at the cost of low complexity, and exhibit low sensitivity to network size, heterogeneous span and parameter configuration.
- Sensor network /
- Stochastic heterogeneous /
- Coverage /
- Node scheduling /
- Neighbor information

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图 1 节点${s_i}$的有效约束圆弧${\stackrel \frown {{\rm{ar}}{{\rm{c}}_i}}}$

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图 2 节点${s_i}$感知半径${r_i}$调整图

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图 3 节点感知重叠面积求解

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图 4 黑色节点的标识与计算

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图 5 不满足圆心$k$覆盖的节点约束圆弧图

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图 6 目标区域调整效果对比

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图 7 覆盖冗余度随节点个数和半径比变化情况

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图 8 多级异构网络中$\Delta F\;$的变化

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图 9 二级异构网络中$\Delta F\;$的变化

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图 10 同构网络中$\Delta F\;$的变化

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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 (${r_i}$)

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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 (${\rm{s}}{{\rm{t}}_i}$)

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表 3 5种算法参数

算法	节点数目
算法	$N = {\rm{100}}$, $N = {\rm{150}}$	$N = {\rm{200}}$, $N = {\rm{250}}$	$N = {\rm{300}}$, $N = {\rm{350}}$	$N = {\rm{400}}$, $N = {\rm{450}}$	$N = {\rm{500}}$
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]。

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