WSN中基于改进灰狼优化的分簇路由协议

侯鹏; 贺之航; 袁也; 晋士程

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WSN中基于改进灰狼优化的分簇路由协议

更新时间：2026-09-04

- WSN中基于改进灰狼优化的分簇路由协议
- Clustering routing protocol based on improved grey wolf optimization in WSN
- 微电子学与计算机 2021年38卷第5期页码：54-59
- 作者机构：
  
  1. 云南大学信息学院,云南,昆明,650500
  2. 深圳大学机构土木与交通工程学院,广东,深圳,518060
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目 61560092云南大学信息学院研究生科研创新项目 Y2000211
- DOI：
  中图分类号：
- 纸质出版：2021
- 稿件说明：
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侯鹏, 贺之航, 袁也, 晋士程. WSN中基于改进灰狼优化的分簇路由协议[J]. 微电子学与计算机, 2021, 38(5): 54-59.

HOU Peng, HE Zhi-hang, YUAN Ye, et al. Clustering routing protocol based on improved grey wolf optimization in WSN[J]. 2021, 38(5): 54-59.
侯鹏, 贺之航, 袁也, 晋士程. WSN中基于改进灰狼优化的分簇路由协议[J]. 微电子学与计算机, 2021, 38(5): 54-59. DOI：

HOU Peng, HE Zhi-hang, YUAN Ye, et al. Clustering routing protocol based on improved grey wolf optimization in WSN[J]. 2021, 38(5): 54-59. DOI：

摘要

针对无线传感器网络能耗不均衡、网络生存期短的问题，提出一种基于改进灰狼优化的分簇路由算法.在簇构建阶段，首先采用自组织神经网络映射(SOM)对网络节点聚类分簇，然后在簇内使用改进的灰狼优化算法选择最优簇头；簇间路由阶段，综合考虑节点的剩余能量和地理位置，为簇首选择合理的下一跳；簇内通信阶段，引入轮询控制机制，进一步降低网络能耗.仿真结果表明：在不同规模的场景下，所提算法均能够均衡网络能耗、延长网络生存期、提高网络吞吐量.

Abstract

In order to solve the problems of uneven energy consumption and short network lifetime in wireless sensor networks

a clustering routing algorithm based on improved grey wolf optimization is proposed. In the cluster construction phase

the self-organizing map network (SOM) clustering algorithm is used to cluster the network nodes

and then the improved grey wolf optimization algorithm is used to select the optimal cluster head in each cluster. In the inter-cluster routing stage

a reasonable next hop is selected for the cluster head by comprehensively considering the residual energy and geographical location of the nodes. In the intra-cluster communication phase

polling control mechanism is introduced to further reduce network energy consumption. Simulation results show that the proposed algorithm can balance the network energy consumption

prolong the network lifetime and improve the network throughput in different scale scenarios.

关键词

Keywords

references

董发志, 丁洪伟, 杨志军, 等. 基于遗传算法和模糊C均值聚类的WSN分簇路由算法[J]. 计算机应用, 2019, 39(8): 2359-2365. DOI: 10.11772/j.issn.1001-9081.2019010134. DONG F Z, DING H W, YANG Z J, et al. WSN clustering routing algorithm based on genetic algorithm and fuzzy C-means clustering[J]. Journal of Computer Applications, 2019, 39(8): 2359-2365. DOI: 10.11772/j.issn.1001-9081.2019010134.

张晓凤, 王秀英. 灰狼优化算法研究综述[J]. 计算机科学, 2019, 46(3): 30-38. DOI: 10.11896/j.issn.1002-137X.2019.03.004. ZHANG X F, WANG X Y. Comprehensive review of grey wolf optimization algorithm[J]. Computer Science, 2019, 46(3): 30-38. DOI: 10.11896/j.issn.1002-137X.2019.03.004.

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CHAUDHARY V, BHATIA R S, AHLAWAT A K. Community SOM (CSOM): an improved self-organizing map learning technique[J]. International Journal of Fuzzy Systems, 2015, 17(2): 129-132. DOI: 10.1007/s40815-015-0022-7.

ZHAO X Q, ZHU H, ALEKSIC S, et al. Energy-efficient routing protocol for wireless sensor networks based on improved grey wolf optimizer[J]. KSⅡ Transactions on Internet and Information Systems, 2018, 12(6): 2644-2657. DOI: 10.3837/tiis.2018.06.011.

武小年, 张楚芸, 张润莲, 等. WSN中基于改进粒子群优化算法的分簇路由协议[J]. 通信学报, 2019, 40(12): 114-123. DOI: 10.11959/j.issn.1000-436x.2019241. WU X N, ZHANG C Y, ZHANG R L, et al. Clustering routing protocol based on improved PSO algorithm in WSN[J]. Journal on Communications, 2019, 40(12): 114-123. DOI: 10.11959/j.issn.1000-436x.2019241.

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