《计算机应用研究》|Application Research of Computers

基于旋转门算法的自适应变频数据采集策略

Strategy of self-adaptive frequency conversion data acquisition based on swing door trending algorithm

免费全文下载 (已被下载 次)  
获取PDF全文
作者 曾文序,库少平,郑浩
机构 武汉理工大学 计算机科学与技术学院,武汉 430070
统计 摘要被查看 次,已被下载
文章编号 1001-3695(2018)03-0769-04
DOI 10.3969/j.issn.1001-3695.2018.03.027
摘要 针对无线传感器网络在监测类似室内或大棚等微气候环境数据时,传统的等间隔时间数据采集方法存在数据大量冗余、浪费网络带宽的问题,以及现有自适应变频数据采集策略仍然非常复杂的问题,提出一种基于旋转门算法的自适应变频数据采集策略,该策略根据旋转门算法能否“套住”数据以及能连续“套住”数据的次数,自适应地调整数据采集的间隔时间。仿真和实验结果证明,该策略与传统的等间隔时间数据采集方法相比,可以降低数据采集量76%以上,减少数据传输量90%以上。该策略具有创新性,实现简单,不仅可行,而且高效。
关键词 微气候环境监测;无线传感器网络;自适应变频;数据采集;旋转门算法
基金项目
本文URL http://www.arocmag.com/article/01-2018-03-027.html
英文标题 Strategy of self-adaptive frequency conversion data acquisition based on swing door trending algorithm
作者英文名 Zeng Wenxu, Ku Shaoping, Zheng Hao
机构英文名 SchoolofComputerScience&Technology,WuhanUniversityofTechnology,Wuhan430070,China
英文摘要 For the wireless sensor network monitoring the micro-climate environmental data such as indoor or greenhouses, the traditional equal interval time data acquisition method had a large amount of data redundancy, waste of network bandwidth and the existing self-adaptive frequency conversion data acquisition strategies were still very complex.This paper proposed a new self-adaptive frequency conversion data acquisition strategy based on the swing door trending (SDT) algorithm, which adjusted the interval of data acquisition adaptively according to whether the SDT algorithm could “trap” data and the number of times it could “trap” data consecutively. Simulation and experimental results show that the proposed strategy can lower the volume of data acquisition more than 76% and reduce capacity of data transmission more than 90% compared with the traditional data acquisition method with equal interval time. The strategy is innovative, simple to implement, not only feasible, but also efficient.
英文关键词 micro-climate environmental monitoring; wireless sensor network; self-adaptive frequency conversion; data acquisition; swing door trending algorithm
参考文献 查看稿件参考文献
  [1] Gupta M, Shum L V, Bodanese E, et al. Design and evaluation of an adaptive sampling strategy for a wireless air pollution sensor network[C] //Proc of the 36th IEEE Conference on Local Computer Networks. [S. l. ] :IEEE Press, 2011:1003-1010.
[2] 宋欣, 王翠荣. 基于线性回归的无线传感器网络分布式数据采集优化策略[J] . 计算机学报, 2012, 35(3):568-580.
[3] 王亚沙, 王光兴. 网络性能管理中一种数据采集算法的研究[J] . 计算机研究与发展, 2002, 39(9):1031-1037.
[4] 庞希愚, 姜波, 仝春玲, 等. 一种自适应数据变化规律的数据采集算法[J] . 计算机技术与发展, 2013, 23(2):157-161.
[5] 王超. 无线传感器网络中数据收集方法研究[D] . 北京:北京邮电大学, 2012.
[6] Alippi C, Anastasi G, Francesco M D, et al. Energy management in wireless sensor networks with energy-hungry sensors[J] . IEEE Instrumentation & Measurement Magazine, 2009, 12(2):16-23.
[7] 任丰原, 黄海宁, 林闯. 无线传感器网络[J] . 软件学报, 2003, 14(7):1282-1291.
[8] Padhy P, Dash R K, Martinez K, et al. A utility-based sensing and communication model for a glacial sensor network[C] //Proc of the 5th International Conference on Autonomous Agents and Multi-Agent Systems. 2006:1353-1360.
[9] 王玲, 石为人, 石欣, 等. 基于时间相关性的无线传感器网络数据压缩与优化算法[J] . 计算机应用, 2013, 33(12):3453-3456.
[10] 王妍, 郭敬玉, 邓庆绪, 等. 基于自适应变频的链型传感网络数据采集策略[J] . 电子测量与仪器学报, 2015, 29(11):1594-1602.
[11] 宁海楠. 一种基于SDT算法的新的过程数据压缩算法[J] . 计算机技术与发展, 2010, 20(1):25-28.
[12] 邢锐, 祁奇, 郑滔. 改进的SDT算法[J] . 计算机工程与设计, 2013, 34(2):515-518.
[13] 于松涛, 王晓琨, 赵利强, 等. 基于容差动态调整的旋转门(SDT)改进算法[J] . 北京化工大学学报:自然科学版, 2013, 40(3):109-113.
[14] 曲奕霖, 王文海. 用于过程数据压缩的自控精度SDT算法[J] . 计算机工程, 2010, 36(22):40-42.
[15] 张健, 刘光斌. ISDT算法的数据压缩处理及其性能分析[J] . 火力与指挥控制, 2007, 32(2):80-82.
[16] 杨明霞, 王万良, 邵鹏飞. 基于时间序列的自适应采样机制策略研究[J] . 计算机科学, 2015, 42(7):162-164.
[17] Intel Berkeley Research Lab. Intel lab data[DB/OL] . (2004-06-02)[2016-07-23] . http://www. select. cs. cmu. edu/data/labapp3/index. html.
收稿日期 2016/12/1
修回日期 2017/1/18
页码 769-772
中图分类号 TP393
文献标志码 A