›› 2011, Vol. 26 ›› Issue (6): 942-953.doi: 10.1007/s11390-011-1191-y

• Computer Network and Internet • Previous Articles     Next Articles

An Efficient Method for Cleaning Dirty-Events over Uncertain Data in WSNs

Mo Chen1,2 (陈默), Student Member, CCF, ACM Ge Yu2, (于戈), Senior Member, CCF, Member, ACM, IEEE, Yu Gu2 (谷峪), Member, CCF, ACM Zi-Xi Jia2 (贾子熙), and Yan-Qiu Wang2 (王艳秋), Student Member, CCF, ACM   

  1. 1. Software College, Northeastern University, Shenyang 110004, China;
    2. College of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2010-04-16 Revised:2011-07-19 Online:2011-11-05 Published:2011-11-05
  • About author:Mo Chen received her B.S. and M.S. degrees in computer science from Northeastern University, China, in 2005 and 2007 respectively. She is currently a Ph.D. candidate in the Department of Computer Soft-ware and Theory, Northeastern Uni-versity. She is now an assistant lec-turer in Software College of North-eastern University. She is a student member of the CCF and member of the ACM. Her re-search interests include wireless sensor network and uncer-tain database.
    Ge Yu received his B.E. degree and M.E. degree in computer sci-ence from Northeastern University of China in 1982 and 1986, respectively, Ph.D. degree in computer science from Kyushu University of Japan in 1996. He has been a professor at Northeastern University since 1996. He is a member of the IEEE, ACM, and a senior member of the CCF. His research interests include database theory and technology, distributed and parallel systems, embedded software, and network information security.
    Yu Gu received his Ph.D. degree from the College of Information Sci-ence and Engineering, Northeastern University of China in 2010. He is an associate professor of Northeast-ern University, China. He is a mem-ber of the CCF and ACM. His major research interests include spatiotem-poral data management, RFID data management and data stream.
    Zi-Xi Jia received his Ph.D. de-gree in pattern recognition and in-telligent system from Northeastern University of China, in 2009. He is a lecturer in the Department of Computer Science and Technology of Northeastern University. His area of research is wireless sensor network.
    Yan-Qiu Wang received her B.S. and M.S. degrees in computer science from Northeastern Univer-sity, China, in 2007 and 2009 respec-tively. She is currently a Ph.D. can-didate at the Department of Com-puter Software and Theory, North-eastern University. She is a student member of the CCF and member of the ACM. Her research interests in-clude spatial temporal query and internet of things.
  • Supported by:

    tural Science Foundation of China under Grant Nos. 61003058, 60933001 and the Fundamental Research Funds for the Central Universities under Grant No. N090104001.

Event detection in wireless sensor networks (WSNs) has attracted much attention due to its importance in many applications. The erroneous abnormal data generated during event detection are prone to lead to false detection results. Therefore, in order to improve the reliability of event detection, we propose a dirty-event cleaning method based on spatio-temporal correlations among sensor data. Unlike traditional fault-tolerant approaches, our method takes into account the inherent uncertainty of sensor measurements and focuses on the type of directional events. A probability- based mapping scheme is introduced, which maps uncertain sensor data into binary data. Moreover, we give formulated definitions of transient dirty-event (TDE) and permanent dirty-event (PDE), which are cleaned by a novel fuzzy method and a collaborative cleaning scheme, respectively. Extensive experimental results show the effectiveness of our dirty-event cleaning method.

[1] Akyildiz I F, Su W, Sankarasubramaniam Y et al. Wirelesssensor networks: A survey. Journal of Computer Networks,2002, 38(4): 393-422.

[2] Lu K J, Qian Y, Rodriguez D et al. Wireless sensor net-works for environmental monitoring applications: A designframework. In Proc. Global Communications Conference,Washington, USA, Nov. 26-30, 2007, pp.1108-1112.

[3] Mainwaring A M, Culler D E, Polaste J et al. Wireless sensornetworks for habitat monitoring. In Proc. the 1st Int. Work-shop on Wireless Sensor Networks and Applications, Atlanta,USA, Sep. 28, 2002, pp.88-97.

[4] Wang M M, Cao J N, Li J et al. Middleware for wirelesssensor networks: A survey. Journal of Computer Science andTechnology, 2008, 23(3): 305-326.

[5] Garetto M, Gribaudo M, Chiasserini C F et al. Sensor deploy-ment and relocation: A unified scheme. Journal of ComputerScience and Technology, 2008, 23(3): 400-412.

[6] Krishnamachari B, Iyengar S S. Distributed Bayesian algo-rithms for fault-tolerant event region detection in wirelesssensor networks. IEEE Transactions on Computers, 2004,53(3): 241-250.

[7] Luo X W, Dong M, Huang Y L. On distributed fault-tolerantdetection in wireless sensor networks. IEEE Transactions onComputers, 2006, 55(1): 58-70.

[8] Ding M, Chen D, Xing K et al. Localized fault-tolerant eventboundary detection in sensor networks. In Proc. IEEE IN-FOCOM2005, Miami, USA, Mar. 13-17, 2005, pp.902-913.

[9] Li C R, Liang C K. A fault-tolerant event boundary detectionalgorithm in sensor networks. In Proc. ICOIN 2007, Estoril,Portugal, Jan. 23-25, 2007, pp.406-414.

[10] Gandhi S, Suri S, Welzl E. Catching elephants with mice:Sparse sampling for monitoring sensor networks. In Proc.SenSys 2007, Sydney, Australia, Nov. 4-9, 2007, pp.261-274.

[11] Ould-ahmed-vall E, Riley G F, Heck B S. A geometric-basedapproach to fault-tolerance in distributed detection usingwireless sensor networks. In Proc. IPSN 2006, Nashville,USA, Apr. 19-21, 2006, pp.203-215.

[12] Bahrepour M, Meratnia N, Havinga P J M. Use of AI tech-niques for residential fire detection in wireless sensor networks.In Proc. Workshops of the 5th IFIP Conference on Artifi-cial Intelligence Applications and Innovations, Thessaloniki,Greece, Apr. 23-25, 2009, pp.311-321.

[13] Wilson D, Shepherd L. Chemical and biological sensors forenvironmental monitoring. In Proc. 2008 Int. Symposiumon Circuits and Systems, Seattle, USA, May 18-21, 2008,pp.1990-1993.

[14] Vu C T, Beyah R A, Li Y S. Composite event detection inwireless sensor networks. In Proc. 2007 Int. PerformanceComputing and Communications Conference, New Orleans,USA, Apr. 11-13, 2007, pp.264-271.

[15] Li D, Wong K D, Hu H Y et al. Detection, classification andtracking of targets. IEEE Signal Processing Magazine, 2002,19(2): 17-29.

[16] Palpanas T, Papadopoulos D, Kalogeraki V et al. Distributeddeviation detection in sensor networks. SIGMOD Record,2003, 32(4): 77-82.

[17] Niu R, Varshney P. Target location estimation in wirelesssensor networks using binary data. In Proc. of 38th Ann.Conf. Information Sciences and Systems, New Jersey, USA,Mar. 17-19, 2004.

[18] MichaelidesMP, Panoyiotou C G. SNAP: Fault tolerant eventlocation estimation in sensor networks using binary data.IEEE Transactions on Computers, 2009, 58(9): 1185-1197.

[19] Cheng R, Kalashnikov D, Prabhakar S. Evaluating probabilis-tic queries over imprecise data. In Proc. 2003 ACM SIGMODInt. Conf. Management of Data, San Diego, USA, Jun. 9-12,2003, pp.551-562.

[20] Cheng R, Xia Y, Prabhakar S et al. Efficient indexing meth-ods for probabilistic threshold queries over uncertain data. InProc. the 30th Int. Conf. Very Large Data Bases, Toronto,Canada, Aug. 29-Sept. 3, 2004, pp.876-887.

[21] Tao Y F, Cheng R, Xiao X K et al. Indexing multi-dimensional uncertain data with arbitrary probability den-sity functions. In Proc. the 31st Int. Conf. Very Large DataBases, Trondheim, Norway, Aug. 30-Sept. 2, 2005, pp.922-933.

[22] Cheng R, Chen J C, Xie X K. Cleaning uncertain data withquality guarantees. In Proc. PVLDB 2008, Auckland, NewZealand, Aug. 23-28, 2008, pp.722-735.

[23] Khoussainova N, Balazinska M, Suciu D. Towards correctinginput data errors probabilistically using integrity constraints.In Proc. MobiDE 2006, Chicago, USA, Jun. 25, 2006, pp.43-50.

[24] Xing G L, Tan R, Liu B Y et al. Data fusion improves thecoverage of wireless sensor networks. In Proc. the 15th Int.Conf. Mobile Computing and Networking, Beijing, China,Sep. 20-25, 2009, pp.157-168.

[25] Tan R, Xing G L, Liu B Y et al. Impact of data fusion on real-time detection in sensor networks. In Proc. the 30th IEEEReal-Time Systems Symposium, Washington, USA, Dec. 1-4,2009, pp.323-332.

[26] Tan R, Xing G L, Xu X T et al. Analysis of quality ofsurveillance in fusion-based sensor networks. In Proc. the8th Int. Conf. Pervasive Computing and Communications,Mannheim, Germany, Mar. 29-Apr. 2, 2009, pp.37-42.

[27] Tan R, Xing G L, Liu X et al. Adaptive calibration forfusion-based wireless sensor networks. In Proc. the 29th Int.Conf. Computer Communication, San Diego, USA, Mar. 15-19, 2009, pp.2124-2132.

[28] Cheng R, Prabhakar S. Managing uncertainty in sensordatabases. SIGMOD Record, 2003, 32(4): 41-46.

[29] Cheng R, Kalashnikov D, Prabhakar S. Evaluating proba-bilistic queries over imprecise data. In Proc. 2003 SIGMODInt. Conf. Management, San Diego, USA, Jun. 9-12, 2003,pp.551-562.

[30] Elnahrawy E, Nath B. Cleaning and querying noisy sensors.In Proc. the 2nd ACM Int. Conf. Wireless Sensor Networksand Applications, San Dirgo, USA, Sept. 19, 2003, pp.78-87.

[31] Chen M. Study on in-network data cleaning techniques forevent detection in wireless sensor network. [Master Thesis]Northeastern University, 2008.
No related articles found!
Full text



No Suggested Reading articles found!

ISSN 1000-9000(Print)

CN 11-2296/TP

Editorial Board
Author Guidelines
Journal of Computer Science and Technology
Institute of Computing Technology, Chinese Academy of Sciences
P.O. Box 2704, Beijing 100190 P.R. China
E-mail: jcst@ict.ac.cn
  Copyright ©2015 JCST, All Rights Reserved