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基于改良麻雀搜索优化的WSN节点部署策略

刘睿 莫愿斌 荆彩

刘睿, 莫愿斌, 荆彩. 基于改良麻雀搜索优化的WSN节点部署策略[J]. 微电子学与计算机, 2022, 39(4): 65-74. doi: 10.19304/J.ISSN1000-7180.2021.1015
引用本文: 刘睿, 莫愿斌, 荆彩. 基于改良麻雀搜索优化的WSN节点部署策略[J]. 微电子学与计算机, 2022, 39(4): 65-74. doi: 10.19304/J.ISSN1000-7180.2021.1015
LIU Rui, MO Yuanbin, JING Cai. Deployment strategy of wireless sensor network based on reformative sparrow search algorithm[J]. Microelectronics & Computer, 2022, 39(4): 65-74. doi: 10.19304/J.ISSN1000-7180.2021.1015
Citation: LIU Rui, MO Yuanbin, JING Cai. Deployment strategy of wireless sensor network based on reformative sparrow search algorithm[J]. Microelectronics & Computer, 2022, 39(4): 65-74. doi: 10.19304/J.ISSN1000-7180.2021.1015

基于改良麻雀搜索优化的WSN节点部署策略

doi: 10.19304/J.ISSN1000-7180.2021.1015
基金项目: 

国家自然科学基金项目 21466008

广西自然科学基金项目 2019GXNSFAA185017

详细信息
    作者简介:

    刘睿  男,(1996-),硕士研究生.研究方向为智能信息控制

    荆彩  女,(2000-),硕士研究生.研究方向为智能信息控制

    通讯作者:

    莫愿斌(通讯作者)   男,(1969-),博士,教授.研究方向为智能计算.E-mail: 674148582@qq.com

  • 中图分类号: TP393

Deployment strategy of wireless sensor network based on reformative sparrow search algorithm

  • 摘要:

    覆盖问题是无线传感器网络(Wireless Sensor Network, WSN)设计中的首要问题,尽可能优化区域覆盖率是提升网络感知性能的直接手段.鉴于此,提出一种基于改良型麻雀搜索算法(Reformative Sparrow Search Algorithm, RSSA)的节点部署优化方案.首先,在算法搜索阶段,RSSA通过引入正余弦指引机制替换原算法位置更新模式,改善算法的遍历性;其次,利用Lévy随机步长特性为算法加入停滞扰动机制,使RSSA具备更强的抗局部极值能力;同时,采用更为契合实际的概率感知模型检测节点的覆盖状态,在迭代更新过程中对比替换更优节点集,从而获得区域覆盖率的提升.为验证改良算法的寻优效果,使用6组通用的基准函数对RSSA进行性能测试,并与三种不同算法进行对比,结果表明RSSA具有良好的优化性能.最后,将RSSA应用于两组WSN节点部署优化实例.对比不同文献中的覆盖优化算法,使用所提算法RSSA优化节点部署最高可取得99.99%的覆盖率,并且使区域内节点呈现均匀化分布,在保证较高覆盖率要求的同时使用了更少的节点,减少了节点冗余,降低了整体网络系统的部署成本.

     

  • 图 1  改良型麻雀搜索算法流程图

    Figure 1.  Reformative Sparrow Search Algorithm flow

    图 2  基准函数

    Figure 2.  Benchmark functions

    图 3  四种算法在基准函数上的收敛曲线对比

    Figure 3.  Comparison of convergence curves of 4 algorithms obtained on the benchmark functions

    图 4  实例一RSSA优化节点分布

    Figure 4.  RSSA optimized nodes distribution in Experiment 1

    图 5  两种节点数量下的覆盖率优化曲线

    Figure 5.  Coverage rate optimization curve under two node numbers

    图 6  实例二RSSA优化节点分布

    Figure 6.  RSSA optimized nodes distribution in Experiment 2

    图 7  四种节点数量下的覆盖率优化曲线

    Figure 7.  Coverage rate optimization curve under four node numbers

    表  1  详细参数设置

    Table  1.   Detailed parameter settings

    Algorithm Parameter setting
    PSO c1=c2=1.49445, ω=0.729
    SCASL α=0.05, β=0.5, a=2
    SSA ST=0.8, PD=0.2, SD=0.1
    RSSA ST=0.8, PD=0.2, SD=0.1
    下载: 导出CSV

    表  2  基准函数信息

    Table  2.   Benchmark function information

    Benchmark function Formula d Range Opt
    Sphere Model $ {{f}_{1}}\left( x \right)=\sum\limits_{i=1}^{n}{{}}{{x}_{i}}^{2}$ 30 [-100, 100] 0
    Schwefel’s problem 2.22 $ {{f}_{2}}\left( x \right)=\sum\limits_{i=1}^{n}{{}}\left| {{x}_{i}} \right|+\prod\limits_{i=1}^{n}{{}}\left| {{x}_{i}} \right|$ 30 [-10, 10] 0
    Schwefel’s problem 1.2 $ {{f}_{3}}\left( x \right)=\sum\limits_{i=1}^{n}{{}}{{\left( \sum\limits_{j=1}^{i}{{}}{{x}_{j}} \right)}^{2}}$ 30 [-100, 100] 0
    Schwefel’s problem 2.21 $ {{f_4}\left( x \right) = {\rm{ma}}{{\rm{x}}_i}\left\{ {\left| {{x_i}} \right|, 1 \le i \le n} \right\}}$ 30 [-100, 100] 0
    Generalized Schwefel’s problem 2.26 $ {f_5}\left( x \right) = \sum\limits_{i = 1}^n {} - {x_i}{\rm{sin}}\sqrt {\left| {{x_i}} \right|} $ 30 [-500, 500] -418.982n
    Ackley’s Function $ {f_6}\left( x \right) = - 20{\rm{exp}}\left( { - 0.2\sqrt {\frac{1}{n}\sum\limits_{i = 1}^n {} {x_i}^2} } \right) - {\rm{exp}}\left( {\frac{1}{n}\sum\limits_{i = 1}^n {} {\rm{cos}}\left( {2\pi {x_i}} \right)} \right) + 20 + e$ 30 [-32, 32] 0
    下载: 导出CSV

    表  3  基准函数优化结果

    Table  3.   Benchmark function optimization results

    F PSO SCASL SSA RSSA
    Mean Std Mean Std Mean Std Mean Std
    f1 5.08e-10 1.73e-09 2.97e-149 1.76e-149 6.02e-52 3.56e-52 0 0
    f2 1.51e-01 4.93e-01 2.39e-72 2.95e-72 8.71e-28 1.19e-28 3.96e-143 1.48e-144
    f3 1.72e-01 4.43e-01 9.40e-105 5.57e-105 2.52e-50 1.17e-50 2.77e-247 0
    f4 2.66 2.32e-01 1.32e-61 5.26e-61 2.06e-35 8.63e-35 1.30e-148 1.07e-148
    f5 -7.65e+03 787.51 -3.61e+03 377.69 -6.29e+03 682.61 -1.24e+04 667.28
    f6 2.12 1.46 8.88e-16 0 8.88e-16 0 8.88e-16 0
    下载: 导出CSV

    表  4  算法运行时间

    Table  4.   The running time of algorithm

    F 最短运行时间/s 最长运行时间/s 平均运行时间/s
    SSA RSSA SSA RSSA SSA RSSA
    f1 0.226 3 0.073 9 0.295 2 0.104 7 0.258 7 0.090 5
    f2 0.243 1 0.089 6 0.304 4 0.120 2 0.267 4 0.096 7
    f3 0.320 2 0.179 4 0.433 1 0.225 2 0.387 3 0.190 2
    f4 0.246 2 0.086 6 0.308 6 0.106 4 0.267 7 0.097 6
    f5 0.252 3 0.100 6 0.316 2 0.152 9 0.287 3 0.112 8
    f6 0.237 3 0.097 7 0.322 8 0.121 4 0.283 9 0.107 6
    下载: 导出CSV

    表  5  实例一中算法优化覆盖率结果对比

    Table  5.   Comparison of optimized coverage results of each algorithm in Experiment 1

    Algorithm Coverage rate /%
    V=20 V=16
    VFPSO 98.01 N/A
    ESCA 99.56 98.04
    RSSA 99.99 99.07
    下载: 导出CSV

    表  6  实例二中算法优化覆盖率结果对比

    Table  6.   Comparison of optimized coverage results of each algorithm in Experiment 2

    Algorithm Coverage rate /%
    V=50 V=46 V=40 V=36
    EABC 90.86 90.86 N/A N/A
    ESCA 98.58 97.26 91.02 N/A
    RSSA 99.26 97.91 96.32 93.73
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-12
  • 修回日期:  2021-09-07
  • 网络出版日期:  2022-05-12

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