基于三维Voronoi图划分的加权混合回归定位算法

李芬芳; 党小超; 郝占军

doi:10.11959/j.issn.2096-3750.2022.00273

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基于三维Voronoi图划分的加权混合回归定位算法

理论与技术 | 更新时间：2024-06-05

- 基于三维Voronoi图划分的加权混合回归定位算法
- Weighted mixed regression localization method based on three-dimensional Voronoi diagram division
- 物联网学报 2022年6卷第2期页码：106-116
- 作者机构：
  
  1. 西北师范大学计算机科学与工程学院，甘肃兰州 730070
  2. 甘肃省物联网工程研究中心，甘肃兰州 730070
- 作者简介：
  
  [ "李芬芳（1990− ），女，西北师范大学讲师，主要研究方向为机器学习、无线网络定位技术和无线感知技术等" ]
  [ "党小超（1963− ），男，西北师范大学教授、硕士生导师，主要研究方向为物联网、传感器网络、无线感知技术等" ]
  [ "郝占军（1979− ），男，博士，西北师范大学教授、硕士生导师，主要研究方向为位置服务、无线定位技术等" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61762079);甘肃省科技重点研发项目;The Key Science and Technology Development Program of Gansu Province(20YF8GA048);甘肃省科技创新基地和人才计划项目;The Science and Technology Innovation Base and Talent Plan Program of Gansu Province(20JR10RA096);西北师范大学青年教师科研能力提升计划项目;The Young Teachers' Scientific Research Ability Improvement Program of Northwest Normal University(NWNU-LKQN2019-28)
- DOI：10.11959/j.issn.2096-3750.2022.00273
  中图分类号： TP391.9
- 纸质出版日期：2022-06-30，
  
  网络出版日期：2022-06，
- 稿件说明：
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李芬芳, 党小超, 郝占军. 基于三维Voronoi图划分的加权混合回归定位算法[J]. 物联网学报, 2022,6(2):106-116.

FENFANG LI, XIAOCHAO DANG, ZHANJUN HAO. Weighted mixed regression localization method based on three-dimensional Voronoi diagram division. [J]. Chinese journal on internet of things, 2022, 6(2): 106-116.
李芬芳, 党小超, 郝占军. 基于三维Voronoi图划分的加权混合回归定位算法[J]. 物联网学报, 2022,6(2):106-116. DOI： 10.11959/j.issn.2096-3750.2022.00273.

FENFANG LI, XIAOCHAO DANG, ZHANJUN HAO. Weighted mixed regression localization method based on three-dimensional Voronoi diagram division. [J]. Chinese journal on internet of things, 2022, 6(2): 106-116. DOI： 10.11959/j.issn.2096-3750.2022.00273.

摘要

随着无线通信技术和感知技术的发展，基于无线传感器网络的各种技术应运而生，这些技术被广泛应用在智慧农业、智慧交通、消防救援等领域。节点定位技术是无线传感器网络的基础技术之一，位置信息是感知数据的一部分，它决定了下一步要采取的具体措施。由于三维空间定位环境的复杂性，将平面上的定位方法应用在三维空间会有一定的局限性。针对以上问题，研究了基于三维空间Voronoi图的加权混合回归定位算法WMR-SKR。该定位算法分为离线训练和在线测试两个阶段。根据网络中的锚节点对定位空间进行三维Voronoi图划分，离线训练阶段将锚节点和 Voronoi cell 顶点的坐标组成的序列作为训练集进行训练。在线测试阶段通过训练好的定位模型对网络中未知节点的坐标进行预测。仿真实验结果表明，所提算法可有效降低三维空间中的节点定位误差，同时有效提高节点定位速度。

Abstract

With the development of the wireless communication technology and sensing technology

various technologies based on wireless sensor networks are applied.These technologies are widely used in the fields of intelligent agriculture

intelligent transportation

fire rescue and so on.Node localization technology is one of the basic technologies of wireless sensor networks.Location information is a part of the sensing data

which determines the specific measures to be taken in the next step.Due to the complexity of the three-dimensional (3D) space localization environment

the application of the plane positioning method in 3D space will have some limitations.Aiming at above problems

the weighted hybrid regression location algorithm WMR-SKR based on a 3D Voronoi diagram was studied.The localization algorithm was divided into two stages: offline training and online testing.The 3D space was divided into Voronoi diagrams according to the anchor nodes in the network.In the offline training stage

the sequence composed of the coordinates of the anchor nodes and Voronoi cell vertices was used as the training set for training.In the online test stage

the coordinates of unknown nodes in the network were predicted through the trained localization model.Simulation results show that the WMR-SKR algorithm can effectively reduce the node localization error and improve the node localization speed in 3D space.

关键词

节点定位Voronoi图划分加权混合回归WMR-SKR

Keywords

node localizationVoronoi diagramweighted mixed regressionWMR-SKR

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