An improved adaptive genetic algorithm-based clustering routing method for wireless sensor networks

CHEN Lei; ZHANG Ying

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An improved adaptive genetic algorithm-based clustering routing method for wireless sensor networks

更新时间：2026-09-04

- An improved adaptive genetic algorithm-based clustering routing method for wireless sensor networks
- Vol. 37, Issue 11, Pages: 41-47(2020)
- 作者机构：
  
  上海海事大学，信息工程学院,上海,201306
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2020
- 稿件说明：
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陈磊, 张颖. 基于自适应遗传算法的WSNs分簇路由方法[J]. 微电子学与计算机, 2020, 37(11): 41-47.

CHEN Lei, ZHANG Ying. An improved adaptive genetic algorithm-based clustering routing method for wireless sensor networks[J]. 2020, 37(11): 41-47.
陈磊, 张颖. 基于自适应遗传算法的WSNs分簇路由方法[J]. 微电子学与计算机, 2020, 37(11): 41-47. DOI：

CHEN Lei, ZHANG Ying. An improved adaptive genetic algorithm-based clustering routing method for wireless sensor networks[J]. 2020, 37(11): 41-47. DOI：

摘要

在设计和部署无线传感网络的过程中，由于部署环境多变，节点能量有限，各节点传输数据时负载不均衡.因此，如何充分利用有限的能量来延长网络寿命，提高数据传输效率和算法的实时性成为了亟需解决的问题.本文针对上述问题提出了一种基于改进型自适应遗传算法的无线传感网络分簇路由协议IAGA-C(Improved Adaptive Genetic Algorithm-Clustering).该方法在分簇过程中，综合考虑了各节点与簇头节点间、簇头节点与基站间的距离以及各节点剩余能量三方面因素来进行簇头的选择.此外，为了提高分簇算法的实时性，本文改进了经典遗传算法的交叉和变异机制，在保证算法有效性的同时缩短了分簇过程所消耗的时间和能量.仿真结果表明，相比于其他协议，该方法在网络寿命、数据传输效率以及实时性等方面有较好的表现.

Abstract

In the process of designing and deploying wireless sensor networks (WSNs)

due to the changeable deployment environment and limited energy

the load of each node is unbalanced when transmitting data. Therefore

how to make full use of the limited energy to prolong the lifetime of the network

improve the data transmission efficiency and real-time performance of the algorithm has become an urgent problem to be solved. An improved adaptive-genetic-algorithm based clustering protocol (IAGA-C) for WSNs is proposed in this paper. The cluster heads are selected by integrating the factors of the distance between each node and cluster heads

the distance between cluster heads and base station (BS)

and the remaining energy of each node. Additionally

in order to improve the real-time performance of clustering algorithm

this paper improves the crossover and mutation mechanism of classical genetic algorithm

and reduces the time consumed in clustering process while ensuring the effectiveness of the algorithm. Simulation results show that

compared with another clustering routing protocols

the proposed method has good performance on improving network life

data transmission efficiency and real-time capability.

关键词

Keywords

references

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