GNSS World of China

Volume 47 Issue 4
Sep.  2022
Turn off MathJax
Article Contents
Article Navigation >  GNSS World of China  > 2022  >  47(4): 50-54, 121
TANG Yitao, DENG He. A hop correction-based node localization algorithm in things of network[J]. GNSS World of China, 2022, 47(4): 50-54, 121. doi: 10.12265/j.gnss.2022043
Citation: TANG Yitao, DENG He. A hop correction-based node localization algorithm in things of network[J]. GNSS World of China, 2022, 47(4): 50-54, 121. doi: 10.12265/j.gnss.2022043
shu

A hop correction-based node localization algorithm in things of network

doi: 10.12265/j.gnss.2022043

  • ChangSha Social Work College, Changsha 410004, China

  • Received Date: 2022-03-22
  • Accepted Date: 2022-03-22
    • Available Online: 2022-07-28
    Abstract
  • In view of the large localization error in the traditional DV-Hop algorithm in wireless sensor network, node localization based on lion swarm optimization (NLLSO) algorithm is proposed in this paper. In NLLSO algorithm, the minimum-hop estimation, average hop error correction and position process are optimized to reduce the localization error. Beacon packet is transmitted in multiple communication radii way, and accuracy of estimating minimum-hop count is improved. In addition, the weight is introduced to correct the average hop. Finally, lion swarm optimization algorithm is used to solve the position of the node. The simulation results show that positioning accuracy of NLLSO algorithm is significantly improved compared with traditional DV-Hop algorithm.

     

    • FullText(HTML)
  • loading
    • References(12)
  • [1]
    ACHROUFENE A, AMIRAT Y, CHIBANI A. RSS-based indoor localization using belief function theory[J]. IEEE transaction automation science and engineering, 2019, 16(3): 1163-1180. DOI: 10.1109/TASE.2018.2873800
    [2]
    蓝土庆. 物联网感知节点安全威胁自动检测系统设计[J]. 制造业自动化, 2019, 41(5): 148-151. DOI: 10.3969/j.issn.1009-0134.2019.05.034
    [3]
    宋婷婷, 张达敏, 王依柔, 等. 基于改进鲸鱼优化算法的WSN覆盖优化[J]. 传感技术学报, 2020, 33(3): 415-422. DOI: 10.3969/j.issn.1004-1699.2020.03.016
    [4]
    MUKHOPADHYAY B, SRIRANGARAJAN S, KAR S. Signal strength-based cooperative sensor network localization using convex relaxation[J]. IEEE wireless communication letters, 2020, 9(12): 2207-2211. DOI: 10.1109/LWC.2020.3018679
    [5]
    李海啸, 于东, 胡毅, 等. 改进的无线传感器网络三边质心定位算法[J]. 小型微型计算机系统, 2020, 41(6): 1216-1223. DOI: 10.3969/j.issn.1000-1220.2020.06.017
    [6]
    胡诚, 肖本贤. 基于自适应免疫粒子群优化的DV-Hop定位算法[J]. 传感器与微系统, 2020, 39(1): 121-124. DOI: 10.13873/J.1000-9787(2020)01-0121-04
    [7]
    LIU J L, WANG Z, YAO M W, et al. VN-APIT: virtual nodes-based range-free APIT localization scheme for wsn[J]. Wireless networks, 2016, 22(3): 867-878. DOI: 10.1007/s11276-015-1007-z
    [8]
    张晶, 李煜. 改进的三次修正DV_Hop定位算法[EB/OL]. 小型微型计算机系统. (2021-07-06) [2022-01-20]. https://kns.cnki.net/kcms/detail/21.1106.TP.20210706.1049.010.html.
    [9]
    苟平章, 刘学治, 孙梦源, 等. 基于跳距修正与狮群优化的WSNs三维定位算法[J]. 计算机工程与科学, 2021, 43(8): 1405-1412. DOI: 10.3969/j.issn.1007-130X.2021.08.009
    [10]
    刘超, 李海鹏. DV-Hop定位算法的研究与改进[J]. 火控雷达技术, 2021, 50(1): 20-25. DOI: 10.19472/j.cnki.1008-8652.2021.01.004
    [11]
    程超, 钱志鸿, 付彩欣, 等. 一种基于误差距离加权与跳段算法选择的遗传优化DV-Hop定位算法[J]. 电子与信息学报, 2015, 37(10): 2418-2423. DOI: 10.11999/JEIT141205
    [12]
    刘雨, 肖本贤, 尹柏强. 基于修正RSSI值的四边形加权质心定位算法[J]. 电子测量与仪器学报, 2020, 34(10): 107-114. DOI: 10.13382/j.jemi.B2002920
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (219) PDF downloads(13) Cited by()
    Proportional views
    Related

    Copyright © 2009《 GNSS World of China 》 Editorial Office

    Address：84 Jianshe Dong Lu, Muye District, Xinxiang City, Henan Province, ChinaChina Pos：453000Tel：0373-3712411Fax：0373-3052232Email：qqdwxt@126.com

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return