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

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Abstract

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.

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Authors and Affiliations

  1. Software College, Northeastern University, Shenyang, 110004, China

    Mo Chen (Student Member, CCF, ACM)

  2. College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China

    Mo Chen (Student Member, CCF, ACM), Ge Yu (Senior Member, CCF, Member, ACM, IEEE), Yu Gu (Member, CCF, ACM), Zi-Xi Jia & Yan-Qiu Wang (Student Member, CCF, ACM)

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  1. Mo Chen
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  2. Ge Yu
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  3. Yu Gu
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  4. Zi-Xi Jia
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  5. Yan-Qiu Wang
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Corresponding author

Correspondence to Ge Yu.

Additional information

This research was supported by the National Basic Research 973 Program of China under Grant No. 2012CB316201, the National Natural Science Foundation of China under Grant Nos. 61003058, 60933001 and the Fundamental Research Funds for the Central Universities under Grant No. N090104001.

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Chen, M., Yu, G., Gu, Y. et al. An Efficient Method for Cleaning Dirty-Events over Uncertain Data in WSNs. J. Comput. Sci. Technol. 26, 942–953 (2011). https://doi.org/10.1007/s11390-011-1191-y

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  • DOI: https://doi.org/10.1007/s11390-011-1191-y

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