基于属性相关性的无线传感网络缺失值估计方法

被引：4

作者：

许可

雷建军

机构：

[1] 重庆邮电大学计算机科学与技术学院

来源：

计算机应用 | 2015年 / 35卷 / 12期

基金：

教育部留学回国人员科研启动基金;

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;

D O I：

暂无

中图分类号：

TP212.9 [传感器的应用]; TN929.5 [移动通信];

学科分类号：

080202 ; 080402 ; 080904 ; 0810 ; 081001 ;

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。

引用

页码：3341 / 3343+3347 +3347

页数：4

共 17 条

[1]

DEMS: A data mining basedtechnique to handle missing data in mobile sensor network applications. Le Gruenwald,Md. Shiblee Sadik,Rahul Shukla, et al. ACMDMSN . 2010

[2]

K-Nearest Neighbor Based Missing Data Estimation Algorithm in Wireless Sensor Networks[J] . Jianzhong Li,Liqiang Pan. &nbspWireless Sensor Network . 2010 (02)

[3]

Particle Swarm Optimization Least Square Support Machine Based Missing Data Imputation Algorithm in Wireless Sensor Network for Nuclear Power Plant’s Environmental Radiation Monitor[J] . Guo Fu Tian,Shi Zhou Zhang,Shu Hui Sun. &nbspAdvanced Materials Research . 2013 (605)

[4]

Monitoring chemical plumes in an environmental sensing chamber with a wireless chemical sensor network[J] . Roderick Shepherd,Stephen Beirne,King Tong Lau,Brian Corcoran,Dermot Diamond. &nbspSensors & Actuators: B. Chemical . 2006 (1)

[5]

Estimation in sensor networks:Agraph approach. Zhang H,Moura J M F,Krogh B H. Proceedings of the4th Inter-national Symposium on Information Processing in Sensor Networks . 2005

[6] A Spatial Correlation Based Adaptive Missing Data Estimation Algorithm in Wireless Sensor Networks [J].

Pan, Liqiang ;

Gao, Huijun ;

Gao, Hong ;

Liu, Yong .

INTERNATIONAL JOURNAL OF WIRELESS INFORMATION NETWORKS, 2014, 21 (04) :280-289

[7] 传感器网络中一种基于时-空相关性的缺失值估计算法 [J].

潘立强 ;

李建中 ;

骆吉洲 .

计算机学报, 2010, 33 (01) :1-11

[8]

LSSVM based missing data imputation in nuclear power plant’’s environmental radiation monitor sensor network. GAO S,TANG Y,QU X. Proceedings of the 15th International Conference on Advanced Computational Intelligence . 2012

[9]

Sensor networks for emergency response: Challenges and opportunities. Lorincz, Konrad,Malan, David J.,Fulford-Jones, Thaddeus R.F.,Nawoj, Alan,Clavel, Antony,Shnayder, Victor,Mainland, Geoffrey,Welsh, Matt,Moulton, Steve. IEEE Pervasive Computing . 2004

[10]

Efficient energy management and data recovery in sensor networks using latent variables based tensor factorization. MILOSEVIC B,YANG J,VERMA N,et al. Proceedings of the 16th ACM International Conference on Modeling,Analysis and Simulation of Wireless and Mobile Systems . 2013

← 1 2 →