Energy Balance Algorithm Based on Particle Swarm Optimization and Minimum Spanning Tree Clustering

LI Kai-jia; YUAN Ling-yun; YU Rui-gang

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Energy Balance Algorithm Based on Particle Swarm Optimization and Minimum Spanning Tree Clustering

更新时间：2026-09-04

- Energy Balance Algorithm Based on Particle Swarm Optimization and Minimum Spanning Tree Clustering
- Vol. 33, Issue 12, Pages: 15-19(2016)
- 作者机构：
  
  1. 昆明学院机构信息技术学院,昆明,650214
  2. 云南师范大学机构信息学院,昆明,650500
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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李凯佳, 袁凌云, 俞锐刚. 基于粒子群优化和最小生成树聚类的能耗均衡算法[J]. 微电子学与计算机, 2016, 33(12): 15-19.

LI Kai-jia, YUAN Ling-yun, YU Rui-gang. Energy Balance Algorithm Based on Particle Swarm Optimization and Minimum Spanning Tree Clustering[J]. 2016, 33(12): 15-19.
李凯佳, 袁凌云, 俞锐刚. 基于粒子群优化和最小生成树聚类的能耗均衡算法[J]. 微电子学与计算机, 2016, 33(12): 15-19. DOI：

LI Kai-jia, YUAN Ling-yun, YU Rui-gang. Energy Balance Algorithm Based on Particle Swarm Optimization and Minimum Spanning Tree Clustering[J]. 2016, 33(12): 15-19. DOI：

摘要

提出了一种无线传感网络的能耗均衡算法.算法基于粒子群优化方法求解适应值

选择最佳簇头以减少簇内节点的传输能耗; 利用最小生成树聚类规则

以剩余能量和距离等因素来选择最优簇头数量

在保证数据传输质量的同时优化了簇头总能耗.仿真结果表明

相比EEMDC算法和DE算法两种能耗均衡算法

本文算法节点平均能量效率分别提高了6.7%和31.76%

网络节点的失效节点数分别降低了22%和27%.

Abstract

A new energy balance algorithm for wireless sensor networks is proposed.This algorithm through adaptive value to choose the best cluster head to reduce the energy consumption of the cluster nodes; The algorithm uses the minimum spanning tree clustering rules

the residual energy and distance and other factors to select the optimal number of cluster heads in order to ensure the quality of data transmission at the same time optimize the cluster head total energy consumption.Simulation results show that compared EEMDC algorithm and DE algorithm two energy balancing algorithm

the algorithm of the node average energy efficiency were increased by 6.7% and31.76%

the network node failure node number were decreased by 22% and 27%.

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references

Hancke G P, Gungor V C. Guest editorial special section on industrial wireless sensor networks[J]. IEEE Transactions on Industrial Informatics, 2014, 10(1): 762-765. DOI: 762

AlShawi I S, Yan L, Pan W, et al. Fuzzy chessboard clustering and artificial bee colony routing method for energy-efficient heterogeneous wireless sensor networks[J]. International Journal of Communication Systems, 2013, 27(12): 3581-3599. DOI: 3581

Mehmood A, Khan S, Shams B, et al. Energy-efficient multi-level and distance-aware clustering mechanism for WSNs[J]. International Journal of Communication Systems, 2013, 28(17): 973-989. DOI: 973

Yu C B, Yu L, Tan J, et al. DV-Hop localization algorithm in WSN based on weighted of correction in hop distance[J]. Applied Mechanics and Materials, 2013, 32(6): 143-148. DOI: 143

Kuila P, Jana P K. A novel differential evolution based clustering algorithm for wireless sensor networks[J]. Applied Soft Computing, 2014, 25(c): 414-425. DOI: 414

谢和平, 周海鹰, 左德承, 等. 无线传感器网络能量优化与建模技术综述[J]. 计算机科学, 2012, 39(10): 15-16. DOI: 15

付帅, 马建峰, 李洪涛. 改进的基于分簇无线传感器网络的数据聚合算法[J]. 吉林大学学报: 工学版, 2014, 44(4): 1118-1125. DOI: 1118

彭铎, 黎锁平, 杨喜娟. 一种能量高效的无线传感网络非均匀分簇路由协议[J]. 传感技术学报, 2014, 27(12): 1687-1691. DOI: 1687

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