基于时间序列预测模型的簇型数据收集机制

doi:10.11772/j.issn.1001-9081.2014.10.2766

计算机应用 ›› 2014, Vol. 34 ›› Issue (10): 2766-2770.DOI: 10.11772/j.issn.1001-9081.2014.10.2766

基于时间序列预测模型的簇型数据收集机制

王正路¹,²,王军¹,程勇²

1. 南京信息工程大学计算机与软件学院，南京 210044；
2. 南京信息工程大学网络信息中心，南京 210044

收稿日期:2014-03-25 修回日期:2014-06-27 出版日期:2014-10-01 发布日期:2014-10-30
通讯作者: 王正路
作者简介:王军(1970-)，男，安徽铜陵人，教授，CCF会员，主要研究方向：无线传感器网络路由协议、物理信息融合；
王正路(1988-)，男，江苏徐州人，硕士，主要研究方向：无线传感器网络路由协议；
程勇(1980-)，男，重庆江津人，讲师，博士，CCF会员，主要研究方向：无线传感器网络路由协议。
基金资助:
国家自然科学基金资助项目;公益性行业(气象)科研专项;江苏省普通高校自然科学研究资助项目

Clustered data collection framework based on time series prediction model

WANG Zhenglu¹,²,WANG Jun¹,CHENG Yong²

1. College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China;
2. Network Information Center, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China

Received:2014-03-25 Revised:2014-06-27 Online:2014-10-01 Published:2014-10-30
Contact: WANG Zhenglu

摘要/Abstract

摘要：

由于温度、光照等物理属性的时空连续性，密集部署的传感器网络中节点感知的数据往往具有很高的时空相关性。这种数据相关性产生的数据冗余会带来通信负担，也会缩短网络的生命周期。提出一种基于预测模型的簇型数据收集机制 (CDCF)，探索数据相关性，减少无线传感器网络的通信量。该机制包括一种基于曲线拟合最小二乘法的时间序列预测模型和简单有效的误差控制方法。在数据收集过程中，簇型结构考虑到了数据间的空间相关性,时间序列预测模型探讨数据的时间相关性。实验仿真表明，在较为稳定的网络环境中，相对于收集原始数据，该机制只需10%~20%的通信量就可完成整个网络的数据收集任务；数据误差控制方法可以确保基站恢复数据的误差控制在用户定义的误差范围之内。

Abstract:

Due to the space-time continuity of the physical attributes, such as temperature and illumination, high spatio-temporal correlation exists among the sensed data in the high-density Wireless Sensor Network (WSN). The data redundancy produced by the correlation brings heavy burden to network communication and shortens the networks lifetime. A Clustered Data Collection Framework (CDCF) based on prediction model was proposed to explore the data correlation and reduce the network traffic. The framework included a time series prediction model based on curve fitting least square method and an efficient error control strategy. In the process of data collection, the clustered structure considered the spatial correlation, and the time series prediction model investigated the temporal correlation existing in sensed data. The experimental simulation proves that CDCF used only 10%—20% of the amount of raw data to finish the data collection of the networks in the relatively stable environment, and the error of the data restored in sink is less than the threshold value which defined by user.

中图分类号:

王正路王军程勇. 基于时间序列预测模型的簇型数据收集机制[J]. 计算机应用, 2014, 34(10): 2766-2770.

WANG Zhenglu WANG Jun CHENG Yong. Clustered data collection framework based on time series prediction model[J]. Journal of Computer Applications, 2014, 34(10): 2766-2770.

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基于时间序列预测模型的簇型数据收集机制

Clustered data collection framework based on time series prediction model

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