Estimating algorithm for missing values based on attribute correlation in wireless sensor network

doi:10.11772/j.issn.1001-9081.2015.12.3341

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (12): 3341-3343.DOI: 10.11772/j.issn.1001-9081.2015.12.3341

• Network and communications • Previous Articles Next Articles

Estimating algorithm for missing values based on attribute correlation in wireless sensor network

XU Ke, LEI Jianjun

School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2015-05-04 Revised:2015-07-16 Online:2015-12-10 Published:2015-12-10

基于属性相关性的无线传感网络缺失值估计方法

许可, 雷建军

重庆邮电大学计算机科学与技术学院, 重庆 400065

通讯作者: 许可(1982-),男,重庆人,讲师,硕士,主要研究方向:无线传感器网络、嵌入式系统
作者简介:雷建军(1976-),男,湖北武汉人,副教授,博士,主要研究方向:无线传感器网络、无线互联网。
基金资助:
人力资源和社会保障部留学人员科技活动择优资助项目(F201404);重庆市基础与前沿研究项目(cstc2013jcyjA40023);教育部留学回国人员科研启动基金资助项目(F201503);重庆邮电大学青年科学研究项目(A2012-90)。

Abstract

Abstract: The missing of the sensing data is inevitable due to the inherent characteristic of Wireless Sensor Network (WSN),which affects various applications significantly. To solve the problem, an estimation algorithm for missing values based on attribute correlation of the sensing data was proposed. The multiple regression model was adopted to estimate missing values of attribute-correlated sensing data. Meanwhile, a data interleaved transmitting strategy was proposed to improve the robustness of the algorithm. The simulation results show that the proposed algorithm can estimate the missing values and is more accurate and reliable than some algorithms based on temporal and spatial correlation such as Linear interpolation Model (LM) algorithm and the traditional Nearest Neighbor Interpolation (NNI) algorithm.

Key words: Wireless Sensor Network (WSN), attribute correlation, missing value, data interleaving, robustness

摘要： 针对无线传感器网络(WSN)中感知数据易缺失问题,提出了一种基于感知数据属性相关性的缺失值估计方法。该方法采用多元线性回归模型,对属性相关的感知数据的缺失值进行估计;同时,为提高算法估计的鲁棒性,提出了基于感知数据属性的数据交织传送策略。仿真结果表明,所提出的估计方法能有效估计无线传感器网络中的缺失值,相比基于时空相关性的线性插值模型(LM)算法和传统的最近邻插值(NNI)算法具有更高的精度和稳定性。

关键词: 无线传感器网络, 属性相关性, 缺失值, 数据交织, 鲁棒性

CLC Number:

TP393.03

XU Ke, LEI Jianjun. Estimating algorithm for missing values based on attribute correlation in wireless sensor network[J]. Journal of Computer Applications, 2015, 35(12): 3341-3343.

许可, 雷建军. 基于属性相关性的无线传感网络缺失值估计方法[J]. 计算机应用, 2015, 35(12): 3341-3343.

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Estimating algorithm for missing values based on attribute correlation in wireless sensor network

基于属性相关性的无线传感网络缺失值估计方法

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