Wireless sensor network nodes localization method based on cellular automata bat algorithm

Kailu MENG; Keqiang YUE; Junna SHANG

doi:10.11959/j.issn.1000-0801.2017311

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Wireless sensor network nodes localization method based on cellular automata bat algorithm

Research and Development | 更新时间：2024-06-05

- Wireless sensor network nodes localization method based on cellular automata bat algorithm
- Telecommunications Science Vol. 33, Issue 11, Pages: 56-65(2017)
- 作者机构：
  
  1. 杭州电子科技大学通信工程学院，浙江杭州310018
  2. 杭州电子科技大学电子信息学院，浙江杭州 310018
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2017311
  CLC： TP393
- Published Online：2017-11，
  
  Published：20 November 2017
- 稿件说明：
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Kailu MENG, Keqiang YUE, Junna SHANG. Wireless sensor network nodes localization method based on cellular automata bat algorithm[J]. Telecommunications science, 2017, 33(11): 56-65.
DOI：

Kailu MENG, Keqiang YUE, Junna SHANG. Wireless sensor network nodes localization method based on cellular automata bat algorithm[J]. Telecommunications science, 2017, 33(11): 56-65. DOI： 10.11959/j.issn.1000-0801.2017311.

摘要

为了提高节点定位精度，解决定位误差较大的问题，提出了基于元胞蝙蝠算法的无线传感器网络节点定位算法，以此来获得更高的定位精度。首先将元胞自动机的思想融入蝙蝠算法，采用了改进的元胞限制竞争选择小生境技术和灾变机制，使得该算法在寻优过程中能够跳出局部极值，避免早熟现象，更快地收敛到全局最优解。通过标准测试函数的验证，表明了该改进算法在收敛深度和广度上的优势。之后将元胞蝙蝠算法应用到无线传感器网络节点定位上来提高定位精度。实测实验中，该算法在测试环境下平均定位误差在0.4 m以内，相比于改进PSO算法，获得更好的定位效果。

Abstract

To further enhance the location precision of unknown nodes and solve the node location error in wireless sensor network

a location method based on cellular automata bat algorithm was presented.Mixed the idea of cellular automata and the bat algorithm and drawed into the cellular RCS niche technique and disaster mechanism

the algorithm could jump out of local optimum and increase the convergence speed.In order to verify the feasibility and efficiency

the proposed algorithm was verified through simulation of several benchmark functions.Then the algorithm implemented the CA-BA to node location prediction to increase the precision of the unknown node location.In the measured experiment

the results show that the proposed algorithm has higher accuracy compared to the improved PSO algorithm

which average localization error is less than 0.5m.

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references

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