斯蒂芬森迭代改进DV-Hop的无线传感器节点定位

doi:10.13229/j.cnki.jdxbgxb20210552

摘要/Abstract

摘要：

针对当前DV-Hop算法存在的传感器节点定位误差大、定位效率低、抗干扰能力差等问题，设计了一种基于改进DV-Hop算法的传感器节点定位方法。首先，采用DV-Hop算法对传感器节点的位置进行初步估计；然后，将DV-Hop算法的传感器节点定位结果作为斯蒂芬森迭代模型的初始值，通过斯蒂芬森不断迭代得到最优传感器节点位置；最后，与其他传感器节点定位算法进行了仿真对比测试。结果表明，本文方法传感器节点定位误差小，传感器节点定位的平均耗时明显缩短，定位效率得到了明显提升。

关键词: 传感器节点, 定位误差, DV-Hop算法, 节点定位效率

Abstract:

To overcome the shortcomings of current DV-Hop algorithm， such as low positioning accuracy， low positioning efficiency and poor anti-interference ability， a sensor node positioning mechanism based on improved DV-Hop algorithm was designed to achieve ideal positioning effect. Firstly， the factors of large sensor node location error in DV-Hop algorithm were analyzed， and corresponding improvements were made. Then， DV-Hop algorithm was used to estimate the location of sensor nodes preliminarily， and DV-Hop algorithm was used as the initial value of Steffensen iteration model. The optimal sensor node location was obtained through Steffensen iteration. Finally，the simulation results were compared with other sensor node localization algorithms. The results show that the positioning error of the proposed mechanism is small. The average time of sensor node positioning is significantly shortened， and the efficiency of sensor node positioning is significantly improved.

Key words: sensor node, positioning error, DV-Hop algorithm, node positioning efficiency

中图分类号:

TP391

唐德红,王一多,马新国. 斯蒂芬森迭代改进DV-Hop的无线传感器节点定位[J]. 吉林大学学报(工学版), 2022, 52(12): 3015-3021.

De-hong TANG, WANGYi-duo,Xin-guo MA. Sensor node localization mechanism based on improved DV⁃Hop algorithm[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 3015-3021.

图/表 6

图1

表1

图2

图3

图4

图5

参考文献 18

1	陈海霞, 王连明. 人工蜂群算法优化支持向量机的传感器节点定位[J].吉林大学学报:理学版, 2017, 55(3): 647-651.
	Chen Hai-xia, Wang Lian-ming. Sensor node localization based on artificial bee colony algorithm optimizing support vector machine[J]. Journal of Jilin University (Science Edition), 2017, 55(3): 647-651.
2	胡中栋, 张康, 王振东. 基于马鞍地形的无线传感器网络节点定位算法[J]. 传感技术学报, 2018, 31(11): 1753-1757.
	Hu Zhong-dong, Zhang Kang, Wang Zhen-dong. The node location algorithm for wireless sensor networks based on saddle topography[J]. Chinese Journal of Sensors and Actuators, 2018, 3(11): 1753-1757.
3	荆夏磊, 乔学工. 一种改进的布谷鸟搜索移动信标节点定位方法[J]. 山西大学学报: 自然科学版, 2019, 42(1): 125-132.
	Jing Xia-lei, Qiao Xue-gong. An improved cuckoo search localization method for mobile beacon nodes[J]. Journal of Shanxi University (Natural Science Edition), 2019, 42(1): 125-132.
4	李建坡, 时明, 钟鑫鑫. 自适应蒙特卡罗无线传感器网络移动节点定位算法[J]. 吉林大学学报: 工学版, 2014, 44(4): 1191-1196.
	Li Jian-po, Si Ming, Zhou Xin-xin. Self-adaptive Monte Carlo localization algorithm of mobile nodes in WSN[J]. Journal of Jilin University (Engineering and Technology Edition), 2014, 44(4): 1191-1196.
5	楼国红, 张剑平. 粒子群算法修正测距的无线传感器网络节点定位[J]. 吉林大学学报: 理学版, 2018, 56(3): 650-656.
	Lou Guo-hong, Zhang Jian-ping. Ranging distance modified by particle swarm algorithm for WSN node localization[J]. Journal of Jilin University (Science Edition) 2018, 56(3): 650-656.
6	任鹏飞, 谷灵康. 基于粒子群进化的输电网络WSN节点定位算法[J]. 沈阳工业大学学报, 2018, 40(5):541-546.
	Ren Peng-fei, Gu Ling-kang. WSN node localization algorithm for transmission network based on particle swarm optimization[J]. Journal of Shenyang University of Technology, 2018, 40(5): 541-546.
7	田浩杉, 李翠然, 谢健骊, 等. 基于时序蒙特卡洛的WSN节点定位算法[J]. 传感技术学报, 2016, 29(11): 1724-1731.
	Tian Hao-shan, Li Cui-ran, Xie Jian-li, et al. Node localization algorithm for WSN based on time sequence Monte Carlo[J]. Chinese Journal of Sensors and Actuators, 2016, 29(11): 1724-1731.
8	汪晗, 成昂轩, 王坤, 等. 无线传感器网络分布式迭代定位误差控制算法[J]. 电子与信息学报, 2018, 40(1): 72-78.
	Wang Han, Cheng Ang-xuan, Wang Kun, et al. Error control algorithm of distributed localization in wireless sensor networks[J]. Journal of Electronics & Information Technology, 2018, 40(1): 72-78.
9	温斯琴. 距离误差加权与多通信半径的蝙蝠优化无线网络节点定位算法[J]. 传感技术学报, 2018, 31(6): 941-948.
	Wen Si-qin. Bat-based optimal location algorithm with distance-weighted error and multiple communication radius[J]. Chinese Journal of Sensors and Actuators, 2018, 31(6): 941-948.
10	吴明慧. 基于局部采样模式的无线传感器节点定位[J]. 内蒙古师范大学学报:自然科学汉文版, 2017, 46(1): 36-39.
	Wu Ming-hui. Localization of wireless sensor nodes based on local sampling pattern[J]. Journal of Inner Mongolia Normal University (Natural Science Edition), 2017,46(1): 36-39.
11	肖玮, 涂亚庆, 徐华. 基于运动参数预测的群组移动节点定位算法[J]. 计算机应用研究, 2018, 35(4):1221-1226.
	Xiao Wei, Tu Ya-qing, Xu Hua. Localization algorithm of group-mobility nodes based on mobility parameters prediction[J]. Application Research of Computers, 2018, 35(4): 1221-1226.
12	闫俊伢, 钱宇华, 李华锋, 等. WSN中基于多径距离和神经网络的节点定位[J]. 计算机科学, 2017, 44(8): 71-75.
	Yan Jun-ya, Qian Yu-hua, Li Hua-feng, et al. Node localization based on multipath distance and neural network in WSN[J]. Computer Science, 2017, 44(8): 71-75.
13	杨海, 李兵. 基于核矩阵等距映射的无线传感网络节点定位算法[J]. 华东师范大学学报: 自然科学版,2019, 22(1): 115-123.
	Yang Hai, Li Bing. Node localization of wireless sensor networks based on the kernel matrix ISOMAP algorithm[J]. Journal of East China Normal University (Natural Science), 2019, 22(1): 115-123.
14	尹雨晴, 高守婉, 王小旗,等. 基于随机游走的无线传感器网络节点定位方法[J]. 北京邮电大学学报, 2018, 41(2): 75-80.
	Yin Yu-qing, Gao Shou-wan, Wang Xiao-qi, et al. Node localization with random walk for wireless sensor networks[J]. Journal of Beijing University of Posts and Telecommunications, 2018, 41(2): 75-80.
15	于晓升, 王莹, 孟亚男, 等. 基于IMM-IKF的无线传感器网络非视距节点定位方法[J]. 控制与决策,2018, 33(6): 1069-1074.
	Yu Xiao-sheng, Wang Ying, Meng Ya-nan, et al. Non-line of sight node localization method based on IMM-IKF for wireless sensor networks[J]. Control and Decision, 2018, 33(6): 1069-1074.
16	赵海军, 夏金红, 贺春林. 基于多维标度和局部成本函数最小化的传感器节点定位算法[J]. 云南大学学报: 自然科学版, 2018, 40(4): 645-651.
	Zhao Hai⁃jun, Xia Jin-hong, He Chun-lin. The sensor node localization algorithm based on multidimensional scaling and minimizing local cost function[J]. Journal of Yunnan University (Natural Sciences Edition), 2018, 40(4): 645-651.
17	周子昂, 徐坤, 程全, 等. 人工蜂群优化神经网络的无线传感器节点定位算法[J]. 南京理工大学学报, 2017, 41(4): 466-471.
	Zhou Zi⁃ang, Xu Kun, Cheng Quan, et al. Node localization of wireless sensor network by using artificial bee colony algorithm optimizing neural network[J]. Journal of Nanjing University of Science and Technology, 2017, 41(4): 466-471.
18	任克强, 李娜. 基于误差加权的三维双曲线定位算法[J]. 传感技术学报, 2017, 30(5): 752-757.
	Ren Ke-qiang, Li Na. Three dimensional hyperbolic localization algorithm based on error weighting[J]. Chinese Journal of Sensors and Actuators, 2017,30(5): 752-757.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

编号	参数	取值及说明
1	无线传感器网络大小/（m×m）	200×200
2	未知节点数/个	100
3	锚节点数/个	25
4	节点分布方式	锚节点均匀分布，未知节点随机分布
5	节点通信半径/m	10~30

[1]	李晗,杜鹏,杜颖,李晓会. 基于遗传算法的无线体域网多路径路由选择方法[J]. 吉林大学学报(工学版), 2022, 52(11): 2706-2711.
[2]	李文军,华强,谭立东,孙悦. DV⁃HOP和接收信号强度指示结合的改进算法[J]. 吉林大学学报(工学版), 2019, 49(5): 1689-1695.
[3]	李娟, 刘禹, 钱志鸿, 卢长刚. 基于双通信半径的传感器网络DV-Hop定位算法[J]. 吉林大学学报(工学版), 2014, 44(2): 502-507.
[4]	申铉京, 李成岳, 王硕, 千庆姬. 基于最优锚节点的无线传感器网络节点定位算法[J]. 吉林大学学报(工学版), 2011, 41(增刊1): 208-214.
[5]	肖竹, 宋宝相, 孙宛, 于全, 易克初. 基于UWB定位误差统计特性分析的NLOS鉴别[J]. 吉林大学学报(工学版), 2010, 40(05): 1381-1385.
[6]	刘衍珩, 刘炳日, 孙大洋, 王爱民. WSN中一种DV-Hop定位精度改进算法[J]. 吉林大学学报(工学版), 2010, 40(03): 763-0768.
[7]	王毅，张德运，马新新，梁涛涛 . 无线传感器网络传感器节点动态功耗管理方法[J]. 吉林大学学报(工学版), 2008, 38(04): 880-885.