Adaptive approach for data cleansing in wireless sensor networks

doi:10.11772/j.issn.1001-9081.2014.08.2145

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (8): 2145-2147.DOI: 10.11772/j.issn.1001-9081.2014.08.2145

Adaptive approach for data cleansing in wireless sensor networks

XIA Ying,BI Haiyang,LEI Jianjun,PEI Haiying

Research Center of Spatial Information System, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2014-04-29 Revised:2014-05-07 Online:2014-08-10 Published:2014-08-01
Contact: XIA Ying

面向无线传感器网络的自适应数据清洗方法

夏英,毕海洋,雷建军,裵海英

重庆邮电大学空间信息系统研究中心，重庆400065

通讯作者: 夏英
作者简介:夏英(1972-),女,重庆人,教授,博士,CCF会员,主要研究方向：数据库与数据挖掘、移动定位与位置服务、云计算与云服务；毕海洋(1989-),男,河北玉田人,硕士研究生,主要研究方向：数据挖掘、空间信息处理；雷建军(1976-),男,重庆人,副教授,博士,主要研究方向：无线传感器网络；裵海英(1948-),男,韩国人,教授,主要研究方向：大数据分析与处理、空间数据库、地理信息系统、多媒体数据库。
基金资助:
重庆市自然科学基金项目;重庆市基础与前沿研究项目;重庆邮电大学青年科学研究项目

Abstract

Abstract:

Since the data gathered in Wireless Sensor Network (WSN) are inaccurate and unreliable, a flexible space model based on the spatial correlation of sensor data was defined, and an adaptive neighbor-space approach for data cleansing (ANSA) was proposed. The approach adjusted neighbor-space dynamically according to sensor data fluctuation and calculated the weighted average of neighbors' measurements to clean local raw data. The experimental results show that, the sensor data error after cleansing by the proposed approach is less than 0.5, and compared to the classic Weighted Moving Average (WMA), it is more accurate and the energy consumption is reduced by about 36%.

摘要：

针对无线传感器网络(WSN)数据不精确和不可靠的问题，根据感知数据的空间相关性定义了弹性空间模型，并在此基础上提出一种自适应近邻空间清洗方法(ANSA)。该方法根据感知数据波动动态调整近邻空间大小，并通过计算近邻节点测量数据的加权平均对本地数据清洗。实验结果表明，感知数据清洗后误差控制在0.5以内，与经典的加权移动平均(WMA)方法相比，所提方法的精确度更高,同时能量损耗减少约36%。

CLC Number:

XIA Ying BI Haiyang LEI Jianjun PEI Haiying. Adaptive approach for data cleansing in wireless sensor networks[J]. Journal of Computer Applications, 2014, 34(8): 2145-2147.

夏英毕海洋雷建军裵海英. 面向无线传感器网络的自适应数据清洗方法[J]. 计算机应用, 2014, 34(8): 2145-2147.

References

［1］MARTINCIC F, SCHWIEBERT L. Distributed event detection in sensor networks ［C］// ICSNC'06: Proceedings of the 2006 IEEE International Conference on Systems and Networks Communications. Piscataway: IEEE Press, 2006: 43.
［2］AKYILDIZ I F, VURAN M C, AKAN O B. On exploiting spatial and temporal correlation in wireless sensor networks ［C］// WiOpt'04: Proceedings of the 2004 Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks. Cambridge: University of Cambridge, 2004: 71-80.
［3］JEFFERY S R, ALONSO G, FRANKLIN M J, et al. Declarative support for sensor data cleaning ［C］// Proceedings of the 4th International Conference on Pervasive Computing. Berlin: Springer, 2006: 83-100.
［4］SHENG B, LI Q, MAO W, et al. Outlier detection in sensor networks ［C］// Proceedings of the 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing. New York: ACM Press, 2007: 219-228.
［5］ZHUANG Y, CHEN L, WANG X S, et al. A weighted moving average-based approach for cleaning sensor data ［C］// ICDCS'07: Proceedings of the 27th IEEE International Conference on Distributed Computing Systems. Piscataway: IEEE Press, 2007: 38-38.
［6］GUO L, FU H, ZHANG Z. Adaptive method for cleaning sensory data in wireless sensor networks [J]. Computer Engineering and Applications, 2009, 45(13): 150-155.(郭龙江,付惠娟,张中兆.传感器网络感知数据自适应去噪方法［J］.计算机工程与应用,2009,45(13):150-155.)
［7］BRANCH J W, GIANNELLA C, SZYMANSKI B, et al. In-network outlier detection in wireless sensor networks ［J］. Knowledge and Information Systems, 2013, 34(1): 23-54.
［8］ZHANG Y, MERATNIA N, HAVINGA P. Outlier detection techniques for wireless sensor networks: a survey ［J］. IEEE Communications Surveys and Tutorials, 2010, 12(2): 159-170.
［9］FRANKE C, GERTZ M. ORDEN: outlier region detection and exploration in sensor networks ［C］// Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2009: 1075-1078.
［10］JEFFERY S R, GAROFALAKIS M, FRANKLIN M J. Adaptive cleaning for RFID data streams ［C］// Proceedings of the 32nd International Conference on Very Large Data Bases. New York: ACM Press, 2006: 163-174.
［11］ZHANG Z, YANG D, ZHANG T, et al. A study on the method for cleaning and repairing the probe vehicle data ［J］. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(1): 419-427.
［12］FANG L, DOBSON S. In-network sensor data modelling methods for fault detection ［C］// AmI-2013: Proceedings of the Fourth International Joint Conference on Ambient Intelligence. Berlin: Springer, 2013: 176-189.

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Adaptive approach for data cleansing in wireless sensor networks

面向无线传感器网络的自适应数据清洗方法

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