[1]李景富,张飞.基于量子粒子群优化PI模型的主动队列网络拥塞控制[J].江西师范大学学报(自然科学版),2015,(03):304-308.
 LI Jingfu,ZHANG Fei.The Network Congestion Control Based on Quantum Particle Swarm Algorithm and Improved PI Active Queue Management Model[J].,2015,(03):304-308.
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基于量子粒子群优化PI模型的主动队列网络拥塞控制()
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《江西师范大学学报》(自然科学版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2015年03期
页码:
304-308
栏目:
出版日期:
2015-05-31

文章信息/Info

Title:
The Network Congestion Control Based on Quantum Particle Swarm Algorithm and Improved PI Active Queue Management Model
作者:
李景富;张飞
1.黄淮学院国际学院,河南 驻马店 463000; 2.黄淮学院信息工程学院,河南 驻马店 463000
Author(s):
LI JingfuZHANG Fei
关键词:
无线传感器网络 拥塞控制 主动队列 量子粒子群
Keywords:
wireless sensor network congestion control active queue management quantum particle swarm
分类号:
TP 391
文献标志码:
A
摘要:
为了解决无线传感器网络拥塞引起的丢包率高和网络吞吐率过低,从而引起网络能量有效性和服务质量QoS降低的问题,提出了一种基于改进PI主动队列管理模型和量子粒子群(Quantum-behaved particle swarm optimization,QPSO)的拥塞控制方法.首先定义了改进的PI主动队列管理模型,然后为了对PI模型进行优化,采用改进的多种群量子粒子群算法对PI主动队列管理模型中的参数优化,并对该算法进行了描述,从而得到优化的PI控制模型.最后定义了多种群量子粒子群算法和PI主动队列模型对网络拥塞进行控制的具体算法.实验结果表明:该方法能有效实现WSN的拥塞控制,与其它方法相比,具有较低的数据丢包率和较大的网络吞吐率.
Abstract:
Aiming at the wireless sensor network having the defect of low throughput and high packet drop rate,a congestion control method based on PI reactive management model and quantum particle swarm algorism was proposed.Firstly,the improved PI reactive management model was defined and equations for the packet drop rate and queue length were given,then in order to optimize the PI model,the quantum particle swarm algorithm was used to obtain the PI parameters,the algorithm is proposed to specify the process of optimizing the parameters,so the optimized model is proposed.Finally,the specific algorism based on PI reactive management model and quantum particle swarm algorism used for congestion control was defined.The simulation experiment shows the method in this paper can realize the WSN congestion control,and compared with the other methods,it has lower data packet dropping rate and larger throughput rate.

参考文献/References:

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备注/Memo

备注/Memo:
河南省科技攻关课题(122102210510);河南省教育厅科技攻关课题(13A520786)
更新日期/Last Update: 1900-01-01