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一种改进的虚拟力重定位覆盖增强算法

周非 郭浩田 杨伊

周非, 郭浩田, 杨伊. 一种改进的虚拟力重定位覆盖增强算法[J]. 电子与信息学报, 2020, 42(9): 2194-2200. doi: 10.11999/JEIT190662
引用本文: 周非, 郭浩田, 杨伊. 一种改进的虚拟力重定位覆盖增强算法[J]. 电子与信息学报, 2020, 42(9): 2194-2200. doi: 10.11999/JEIT190662
Fei ZHOU, Haotian GUO, Yi YANG. An Improved Virtual Force Relocation Coverage Enhancement Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2194-2200. doi: 10.11999/JEIT190662
Citation: Fei ZHOU, Haotian GUO, Yi YANG. An Improved Virtual Force Relocation Coverage Enhancement Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2194-2200. doi: 10.11999/JEIT190662

一种改进的虚拟力重定位覆盖增强算法

doi: 10.11999/JEIT190662
基金项目: 国家自然科学基金(61471077)
详细信息
    作者简介:

    周非:男,1977年生,博士,教授,研究方向为无线定位、信号处理、图像处理等

    郭浩田:男,1994年生,硕士,研究方向为无线传感网络

    杨伊:女,1992年生,硕士,研究方向为无线传感网络

    通讯作者:

    郭浩田 17784456880@163.com

  • 中图分类号: TN915.1; TP391.9

An Improved Virtual Force Relocation Coverage Enhancement Algorithm

Funds: The National Natural Science Foundation of China (61471077)
  • 摘要: 在移动无线传感网络(MWSN)的部署问题中最关键的是如何提供最大的区域覆盖范围。针对现有的覆盖控制算法存在覆盖率不理想、部署效率低、能耗过高的问题,该文提出了一种高效部署策略。第1阶段利用Voronoi图获得整个网络的覆盖孔,检测Voronoi多边形内的未覆盖区域,并提供虚拟力驱动传感器移动,同时采用动态调整策略改变移动步长,从而减少能量损耗;第2阶段提出一种检测机制,利用Delaunay三角网检测传感器之间的局部覆盖孔并进行修复。仿真结果表明,该算法在提高网络覆盖率的同时加快了收敛速度,为部署移动无线传感网络提供了新的解决思路。
  • 图  1  局部覆盖孔

    图  2  基于形心传感器移动示意图

    图  3  基于虚拟力传感器移动示意图

    图  4  基于检测机制漏洞修复示意图

    图  5  传感器运动过程图

    图  6  覆盖率变化图

    图  7  单次蒙特卡洛仿真覆盖率变化曲线

    图  8  不同节点个数,4种算法性能对比图

    表  1  基于Voronoi图的虚拟力重定位算法

     Randomly deploy N sensors in the monitoring area;
     Repeat
     Construct Voronoi polygons based on the position of the
     sensors;
      For each $i{\rm{ (} }1 \le i \le N)$
       For each $j{\rm{ } }(1 \le j \le {\rm{vertex(} }i\rm{)})$// vertex: the number of
       Voronoi polygon vertices;
        If ${{\rm dist(} }i,j{) < }{R_s}$ //case 1: Voronoi polygon vertices are
        all covered;
         Calculate the force of the centroid on the sensor and
         the position of the sensor;
        Else //case 2: Voronoi polygon vertices are not all
        covered;
         Calculate ${F_{\rm{uncov}}}$ and the position of the sensor at the
         next moment;// ${F_{\rm{uncov}}}$:the force of the uncovered grid
         point on the sensor;
         End for
        End for
       Sensor location update;
      End for
     Until termination criterion is met
    下载: 导出CSV

    表  2  基于Delaunay三角的局部覆盖空洞修复算法

     Construct a Delaunay triangulation based on the position of the
     sensors;
     For each $j{\rm{ } }(1 \le j \le {\rm{TRI} }\left( i \right))$// TRI: the number of Delaunay
     triangles
       Calculate empty circle center coordinates and radius;
       If there is a gap between the sensors:
        Calculate the force of the centroid of the empty circle on
        the sensor and the position of the sensor circle on the
        sensor;
        If ${\rm{fitness} }(x(t)) \ge {\rm{fitness} }(x(t - {\rm{1} }))$
         Sensor location update;
         Break;
       Else
         Sensor position unchanged;
       End if
      End if
     End for
    下载: 导出CSV
  • YUE Yinggao and HE Ping. A comprehensive survey on the reliability of mobile wireless sensor networks: Taxonomy, challenges, and future directions[J]. Information Fusion, 2018, 44: 188–204. doi: 10.1016/j.inffus.2018.03.005
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    XU Ying, DING Ou, QU Rong, et al. Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization[J]. Applied Soft Computing, 2018, 68: 268–282. doi: 10.1016/j.asoc.2018.03.053
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    BARTOLINI N, BONGIOVANNI G, PORTA T L, et al. Voronoi-based deployment of mobile sensors in the face of adversaries[C]. 2014 IEEE International Conference on Communications, Sydney, Australia, 2014: 532–537. doi: 10.1109/ICC.2014.6883373.
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出版历程
  • 收稿日期:  2019-08-30
  • 修回日期:  2020-02-27
  • 网络出版日期:  2020-04-15
  • 刊出日期:  2020-09-27

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