带混沌映射的三维WSN蜂群优化定位算法

李田来; 刘方爱

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带混沌映射的三维WSN蜂群优化定位算法

更新时间：2026-09-04

- 带混沌映射的三维WSN蜂群优化定位算法
- Artificial bee colony optimization localization algorithm with chaotic map in three-dimensional wireless sensor networks
- 微电子学与计算机 2019年36卷第3期页码：38-42
- 作者机构：
  
  山东师范大学机构信息化工作办公室,山东,济南,250013
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61772321国家自然科学基金 61572301山东省自然科学基金 ZR2016FP07
- DOI：
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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李田来, 刘方爱. 带混沌映射的三维WSN蜂群优化定位算法[J]. 微电子学与计算机, 2019, 36(3): 38-42, 47.

LI Tian-lai, LIU Fang-ai. Artificial bee colony optimization localization algorithm with chaotic map in three-dimensional wireless sensor networks[J]. 2019, 36(3): 38-42.
李田来, 刘方爱. 带混沌映射的三维WSN蜂群优化定位算法[J]. 微电子学与计算机, 2019, 36(3): 38-42, 47. DOI：

LI Tian-lai, LIU Fang-ai. Artificial bee colony optimization localization algorithm with chaotic map in three-dimensional wireless sensor networks[J]. 2019, 36(3): 38-42. DOI：

摘要

传感器节点的定位能力对无线传感器网络的性能有很大的影响.为此

本文针对三维无线传感器网络提出了一种带混沌映射的蜂群优化定位算法(CMABC).首先

采用RSSI模型获取目标节点与信标节点的测量值

以此为基础基于三面定位法计算目标节点初值集; 然后

将混沌映射函数作为参数应用到初始解生成函数和寻优函数中; 最后

采用改进的蜂群优化算法优化目标节点的位置.为了验证CMABC算法性能

进行了仿真实验.仿真结果表明

该算法在定位成功率和精度方面均优于人工蜂群算法、粒子群优化算法等定位算法.

Abstract

The localization ability of sensor nodes has a great impact on the performance of wireless sensor networks. To this

this paper proposes an artificial bee colony optimization localization algorithm (CMABC) with chaotic mapping in three-dimensional wireless sensor networks. First

the RSSI model is used to obtain the measured value of the target node and the beacon node. Based on the three plane location method

the initial set of target nodes is calculated. Then

several chaotic mapping functions are studied and used as initial solutions to generate functions and optimization functions. Finally

the improved artificial bee colony optimization algorithm is used to optimize the position of target nodes. In order to verify the performance of CMABC algorithm

a simulation experiment is carried out. The simulation results show that the algorithm is superior to artificial bee colony algorithm and particle swarm optimization algorithm in positioning success rate and accuracy.

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references

ARORA S, SINGH S. Butterfly algorithm with lèvy flights for global optimization[C]// International Conference on Signal Processing, Computing and Control. Italy, Bologna, IEEE, 2016: 220-224.

LIU Y, CHEN J. A K-means based firefly algorithm for localization in sensor networks[J]. International Journal of Parallel Emergent % Distributed Systems, 2018(12):1-16. DOI: 1

ZHANG X, TANG J. LSSVR three-dimensional WSN nodes location algorithm based on improved RSSI[J]. Video Engineering, 2014: DOI: 10.1109/ICECENG.2011.6058085.

PATWARI N, ASH J N, KYPEROUNTAS S, et al. Locating the nodes: cooperative localization in wireless sensor networks[J]. IEEE Signal Processing Magazine, 2005, 22(4):54-69. DOI: 54

KULKARNI R V, VENAYAGAMOORTHY G K, CHENG M X. Bio-inspired node localization in wireless sensor networks[C]// IEEE International Conference on Systems, Man and Cybernetics. Italy, Bari, IEEE Press, 2009: 205-210.

李彬, 吕晓军, 贾新春, 等.无线传感器网络三维节点的插值规划定位[J].北京邮电大学学报, 2016, 39(5):94-98. DOI: 94

SANKALAP A, SATVIR S. An improved butterfly optimization algorithm with chaos[J]. Advanced Science, 2016, 8(9):711-717. DOI: 711

崔鸿飞, 刘佳, 顾晶晶, 等.基于局部保持典型相关分析的无线传感器网络三维定位算法[J].计算机科学, 2017, 44(9):105-109. DOI: 105

曹建, 乔学工.基于鸡群优化的无线传感器网络三面定位算法[J].计算机应用研究, 2017, 34(8):2483-2485. DOI: 2483

邹斌, 李长庚.一种无线传感器网络移动节点的三维定位算法[J].计算机应用与软件, 2015, 32(1):132-135. DOI: 132

YUN S, LEE J, CHUNG W, et al. A soft computing approach to localization in wireless sensor networks[J]. Expert Systems with Applications, 2009, 36(4):7552-7561. DOI: 7552

李田来, 刘方爱, 王新华.基于分治策略的改进人工蜂群算法[J].控制与决策, 2015, 30(2):316-320. DOI: 316

卞国龙, 黄海松, 葛至峥, 等.无线传感器网络定位技术的优化处理[J].微电子学与计算机, 2017, 34(4):49-54. DOI: 49

张欣慧, 徐晶晶, 许必宵, 等.无线传感器网络三维定位算法研究[J].计算机技术与发展, 2016, 26(12):195-199. DOI: 195

刘伟, 董恩清, 宋洋.无线传感器网络节点三维定位的翻转模糊检测[J].电子学报, 2016, 44(2):374-384. DOI: 374

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