In wireless sensor networks, affected by sensor noise, sensor error, transmission error and other factors, data collected from different sensor nodes in the same sensor field are different, leading to uncertainty of the sensor data. This is a challenge for further data processing methods such as sensor data mining and query. To deal with the data uncertainty problem, an uncertain sensor data prediction method based on multiple variable principle component analysis (MVPCA) is proposed. The uncertain sensor data are first pretreated with MVPCA feature extraction to obtain the intrinsic featrure from the uncertain data. A multiple regression prediction method based on correlation analysis is then applied to the feature data for modeling and prediction. Sensor data of a real wireless sensor network are used to estimate the method. The results show that the proposed method can efficiently predict uncertian sensor data with high accuracy.
YAN Xiao-zhen, XIE Hong, WANG Tong
. Uncertain Sensor Data Prediction for Wireless Sensor Networks[J]. Journal of Applied Sciences, 2012
, 30(6)
: 566
-572
.
DOI: 10.3969/j.issn.0255-8297.2012.06.002
[1] Cullar D, Estrin D, Strvastave M. Overview of sensor networks [J]. IEEE Computer, 2004, 37(8):41-49.
[2] 李建中，高宏. 无线传感器网络的研究进展[J]. 计算机研究与发展，2008, 45(1): 1-15.
Li Jianzhong, Gao Hong. Advance of wireless sensor networks [J]. Journal of Computer Research and Development, 2008, 45(1): 1-15. (in Chinese)
[3] Li Yingshu, Ai Chunyu, Deshmukh W P, Wu Yiwei. Data estimation in sensor networks using physical and statistical methodologies [C]//Proceedingsof the 28th IEEE International Conference on Distributed Computing Systems: Washington, IEEE Computer Society, 2008: 538-545.
[4] Zhang Haotian, Moura J M F, Krough B. Estimation in sensor networks: a graph approach [C]//Proceedings of the 4th International Symposium on Information Processing in Sensor Networks:Los Angeles, California, 2005: 203-209.
[5] Jiang N, Gruenwald L. Estimating missing data in data streams [C]//Proceedings of the 12th International Conference on Database Systems for Advanced Applications. Berlin: Springer, 2007: 981-987.
[6] 邹长忠. 无线传感器网络中基于SVR的节点数据估计算法[J]. 计算机应用，2010, 30(1): 127-136.
Zou Changzhong. Node data prediction based on SVR in wireless sensor network [J]. Journal of Computer Applications, 2010, 30(1): 127-136. (in Chinese)
[7] Yan Xiaozhen, Xie Hong, Luo Qinghua. A data predicting method based on LS-SVR in wireless sensor network [C]//Proceedings of WNIS (International Conference on Wireless Sensor Networks and Information Systems) 2010: 75-78.
[8] 潘立强，李建中. 传感器网络中一种基于多元回归模型的缺失值估计算法[J]. 计算机研究与发展，2009,46(12): 2101-2110.
Pan Liqiang, Li Jianzhong. A multiple regression model based missing values imputation algorithm in wireless sensor network [J]. Journal of Computer Research and Development: 2009, 46(12): 2101-2110.(in Chinese)
[9] 潘立强，李建中，骆吉洲. 传感器网络中一种基于时空相关性的缺失值估计算法[J]. 计算机学报，2010,33(1): 1-11.
Pan Liqiang, Li Jianzhong, Luo Jizhou. A temporal and spatial correlation based missing values imputation algorithm in wireless sensor networks [J]. Chinese Journal of Computers, 2010, 33(1): 1-11. (in Chinese)
[10] Yan Xiaozhen, Xie Hong, Wang Tong. A multiple linear regression data predicting method using correlation analysis for wireless sensor networks [J]. Journal of Computational Information Systems (JCIS),2011, 7(11): 4105-4112.
[11] Cheng R, Kalashnikov D, Prabhakar S. Evaluating probabilistic queries over imprecise data[C]//Proceedings of ACM International Conference on Special Interest Group on Management of Data,2003.
[12] Cheng R, Prabhakar S. Managing uncertainty in sensor databases [C]//Proceedings of ACM International Conference on Special Interest Group on Management of Data Record Issue on Sensor Technology, December 2003.
[13] Cheng R, Xia Y, Prabhakar S, Shah R, Vitter J. Efficient indexing methods for probabilistic threshold queries over uncertain data [C]//Proceedings of Very Large Data Base (VLDB), 2004.
[14] Singh S, Mayeld C, Prabhakar S, Shah R, Hambrusch S E. Indexing uncertain categorical data [C]//Proceedings of the 23rd IEEE International Conference on Data Engineering (ICDE), 2007.
[15] 何丽娟，周鸣争，陶皖，江自兵. 无线传感器网络中不确定数据的估计算法[J]. 计算机工程与应用，2011, 47(28): 100-102.
He Lijuan, Zhou Mingzheng, Tao Wan, Jiang Zibing. Estimation algorithm for uncertain data in wireless senor networks [J]. Computer Engineering and Applications, 2011, 47(28): 100-102. (in Chinese)
[16] Lee H, Choi J S, Elmasri R. Sensor data fusion using D-S theory for activity recognition under uncertainty in home-based care [C]//Proceedings of International Conference on Advanced Information Networking and Applications, 2009: 517-524.
[17] 任秀丽，田洋. 传感器网络中基于预处理证据理论的数据融合[J]. 计算机应用，2011, 31(7): 1992-1994.
Ren Xiuli, Tian Yang. Data fusion based on evidence theory by preprocessing in wireless sensor network [J]. Journal of Computer Applications, 2011,31(7): 1992-1994. (in Chinese)
[18] Wang Cheng, Menenti M, Li Zhaoliang. Modified principal component analysis (MPCA) for feature selction of hyper-spectral imagery [J]. IEEE International,2003(6): 3781-3783.
[19] Aminghafari M, Cheze N, Poggi J M. Multivariate denoising using wavelets and principal component analysis [J]. Computational Statistics & Data Analysis, 2006(50): 2381-2398.
