基于自然选择粒子群的时钟同步算法

蒋伊琳; 张芳园

doi:10.3969/j.issn.0258-2724.2017.03.021

基于自然选择粒子群的时钟同步算法

doi: 10.3969/j.issn.0258-2724.2017.03.021

基金项目:

国家自然科学基金资助项目（NSFC-61571146）

详细信息

作者简介:
蒋伊琳(1980—),男,讲师,博士,研究方向为宽带信号检测与估值、无线时间同步等,E-mail:jiangyilin@hrbeu.edu.cn

计量
- 文章访问数: 514
- HTML全文浏览量: 94
- PDF下载量: 127
- 被引次数: 0
出版历程
- 收稿日期: 2016-03-03
- 刊出日期: 2017-06-25

Clock Synchronization Algorithm Based on Particle Swarm Optimization with Natural Selection

摘要

摘要: 为了提高无线传感器网络的时钟同步精度，避免因不可靠数据和网络拓扑结构变化在同步过程中产生的同步误差，建立了基于自然选择粒子群融合算法仿真模型.首先，采用卡尔曼滤波算法，将多个传感器的测量数据进行局部滤波处理，去除测量数据中最底层的冗余信息，提高测量数据的准确性；其次，采用自然选择粒子群算法建立数据融合模型，计算网络的最优融合估计；最后，对自然选择粒子群融合算法模型进行仿真，结果表明：该算法的融合模型不仅能够对网络中的5个节点时钟进行有效融合，而且可以提高节点晶振的时偏和频偏的融合精度，形成一个具有较高稳定度的虚拟时钟，其时偏和频偏的融合精度主要集中范围分别为10-5和10-7，与传统自适应融合算法相比较，它的时偏和频偏融合精度提高了1个数量级.
- 无线传感器网络 /
- 时钟同步 /
- 数据融合 /
- 粒子群算法 /
- 卡尔曼滤波 /
- 自适应加权法
Abstract: In order to improve the clock synchronization accuracy of wireless sensor network (WSN) and avoid the synchronization error caused by the unreliable data and the change of the network topology structure in the synchronization process, a simulation model of particle swarm optimization (PSO) algorithm based on natural selection was established. Firstly, using Kalman filtering algorithm to locally filter the measurement data of multiple sensors, the redundant information in the bottom of the measurement data could be removed so that the accuracy of measurement data is improved. Then, particle swarm optimization algorithm based on natural selection was used to set up a data fusion model and calculate the optimal fused estimation. Finally,the simulation results of the fusion model of PSO algorithm based on natural selection show that the fusion algorithm can effectively realize the fusion of five node clock in the networks, and improve the fusion accuracy of time offset and frequency offset of node crystal oscillating, producing a virtual clock of high stability. The concentration ranges of fusion accuracy of time offset and frequency offset are 10-5 and 10-7, respectively. Compared with traditional adaptive fusion algorithms, the concentration ranges of fusion accuracy of time offset and frequency offset are improved by one order of magnitude.
- wireless sensor network /
- clock synchronization /
- data fusion /
- particle swarm optimization algorithm /
- Kalman filtering /
- adaptive weighted algorithm

HTML全文

参考文献(19)

GANERIWAL S. Timing-sync protocol for sensor networks[C]//International Conference on Embedded Networked Sensor Systems.[S.l.]: ACM, 2003: 138-149.

MARI M, KUSY B, SIMON G G, et al. The flooding time synchronization protocol[C]//International Conference on Embedded Networked Sensor Systems. Baltimore:[s.n.], 2004: 39-49.

ELSON J, ROMER K. Wireless sensor network: a new regime for time synchronization[J]. ACM Sigcomm Computer Communication Review, 2003, 33(1): 149-154.

GIROD L, ELSON J, ESTRIN D. Fine-grained network time synchronization using reference broadcasts[J]. Proceedings of OSDI, 2002, 36(Sup.1): 147-163.

李晓珍.基于IEEE 1588的网络时间同步系统研究[D]. 西安:中国科学院研究生院,2011.

周思捷. 基于IEEE 1588无线网络时间同步技术研究[D]. 上海:上海交通大学,2013.

BLETSAS A. Evaluation of Kalman filtering for network time keeping[J]. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2006, 52(9): 1452-1460.

董真杰,郑琛瑶,张国龙. 不同精度数据融合的自适应加权平均法研究[J]. 船舶电子工程,2014,34(10): 31-33. DONG Zhenjie, ZHENG Chenyao, ZHANG Guolong. Self-adaption of weighted average research for data fusion with different precision[J]. Ship Electronic Engineering, 2014, 34(10): 31-33.

唐亚鹏. 基于自适应加权数据融合算法的数据处理[J]. 计算机技术与发展,2015,25(4): 53-56. TANG Yapeng. Data processing based on adaptive weighted data fusion algorithm[J]. Computer Technology and Development, 2015, 25(4): 53-56.

ZHANG L Y, LI D, ZHANG L, et al. A weighted fusion algorithm of multi-sensor based on optimized grouping[C]//Intelligent Control and Automation. Dalian: The Sixth World Congress, 2006: 5350-5353.

QU B Y, LIANG J J, SUGANTHAN P N. Niching particle swarm optimization with local search for multi-modal optimization[J]. Information Sciences, 2012, 197: 131-143.

LIANG J J, QIN A K, SUGANTHAN P N. Comprehensive learning particle swarm optimizer for optimizer for global optimization of multimodal function[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(3): 1158-1162.

OLIVEIRA M, BASTOS-FILHO C, MENEZES R. Using network science to assess particle swarm optimizers[J]. Social Network Analysis and Mining, 2015, 5(1): 1-13.

WU Xinjie, CUI Chunyang, HU Sheng, et al. The velocity measurement of two-phase flow based on particle swarm optimization algorithm and nonlinear blind source separation[J]. Chinese Journal of Chemical Engineering, 2012, 20(2): 346-351.

何莉,刘晓东,李松阳,等. 多核环境下并行粒子群算法[J]. 计算机应用,2015,35(9): 2482-2485,2518. HE Li, LIU Xiaodong, LI Songyang, et al. Parallel particle swarm optimization algorithm in multicore computing environment[J]. Journal of Computer Applications, 2015, 35(9): 2482-2485,2518.

吴海,孙永雄,韩伟,等. 多核环境下的粒子群算法[J]. 吉林大学学报,2012,30(5): 549-554. WU Hai, SUN Yongxiong, HAN Wei, et al. PSO algorithm in multi-nuclear environment[J]. Journal of Jilin University, 2012, 30(5): 549-554.

赵广宇. 无线传感器网络中基于预测模型的数据融合算法研究[D]. 吉林:吉林大学,2012.

任俊亮,邢清华,李龙跃,等. 分布式传感器调度模型与自适应概率粒子群优化算法[J]. 电子学报,2015,43(9): 1756-1762. REN Junliang, XING Qinghua, LI Longyue, et al. A model of distributed sensors' scheduling and self-adaptive probability particle swarm optimization algorithm[J]. ACTA Electronica Sinica, 2015, 43(9): 1756-1762.

杨晓燕. 基于改进PSO的多传感器数据自适应加权融合算法[J]. 闽江学院学报,2011,32(5): 67-71. YANG Xiaoyan. Adaptive weighted fusion algorithm of multi-sensor data based on improved particle swarm optimization[J]. Journal of Min Jiang University, 2011, 32(5): 67-71.

施引文献

附加材料(0)

访问统计

点击查看大图

计量

文章访问数: 514
HTML全文浏览量: 94
PDF下载量: 127
被引次数: 0

基于自然选择粒子群的时钟同步算法

doi: 10.3969/j.issn.0258-2724.2017.03.021

作者简介: 蒋伊琳(1980—),男,讲师,博士,研究方向为宽带信号检测与估值、无线时间同步等,E-mail:jiangyilin@hrbeu.edu.cn

计量

出版历程

Clock Synchronization Algorithm Based on Particle Swarm Optimization with Natural Selection

计量

出版历程

目录

作者简介:
蒋伊琳(1980—),男,讲师,博士,研究方向为宽带信号检测与估值、无线时间同步等,E-mail:jiangyilin@hrbeu.edu.cn