Research on topology evolution model for WSNs based on weight constraints in mutual selection mechanism

WANG Chuanyun; ZHU Tao; YIN Yan; ZHAO Junhui

doi:10.13873/J.1000-9787(2021)06-0044-04

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Research on topology evolution model for WSNs based on weight constraints in mutual selection mechanism

更新时间：2025-09-11

- Research on topology evolution model for WSNs based on weight constraints in mutual selection mechanism
- Issue 6, Pages: 44-47+51(2021)
- 作者机构：
  
  1. 华东交通大学信息工程学院
  2. 华东交通大学软件学院
  3. 北京交通大学电子与信息工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2021)06-0044-04
  CLC： TN929.5;TP212.9
- Published：2021
- 稿件说明：
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WANG Chuanyun, ZHU Tao, YIN Yan, et al. Research on topology evolution model for WSNs based on weight constraints in mutual selection mechanism[J]. 2021, (6): 44-47+51.
DOI：

WANG Chuanyun, ZHU Tao, YIN Yan, et al. Research on topology evolution model for WSNs based on weight constraints in mutual selection mechanism[J]. 2021, (6): 44-47+51. DOI： 10.13873/J.1000-9787(2021)06-0044-04.

摘要

探讨和分析了无线传感器网络(WSNs)分簇拓扑结构和权重约束演化模型

提出了点权约束释放模型。通过双向选择机制

对节点间建立连接的方式进行了优化;通过设置节点权重最大阈值来限制点权增长

并对部分达到最大阈值的节点解除权重约束效果

新节点可以就近连接被解除权重约束的节点

减少节点的能量损耗。通过MATLAB软件仿真

将所提模型与双向选择模型和权重约束模型进行对比

簇头分布更均匀

点权分布更合理。模型有效降低了WSNs的能量损耗

延长了WSNs中节点的寿命。

Abstract

Clustering topology structure and weight constraint evolution model for WSNs are discussed and analyzed

and a node weight constraint release model is proposed.The proposed model optimizes the method establishing connections between nodes by mutual selection mechanism

limit nodes weight growth by setting node weight maximum threshold

and released the weight constraint effect on the nodes which reach the maximum threshold.And then

a new node is permitted to connect with the nearby node whose weight constraint is released

it is useful for reducing the energy loss of the node.Using MATLAB software simulation

compared the proposed model with the mutual selection model and the weight constraint model

the proposed model is more reasonable than the other two models in the distribution of the cluster head and the node weight.The proposed model reduces the energy consumption of WSNs effectively

which helps to extend the life of nodes in WSNs.

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