Abstract
Outlier detection in wireless sensor networks (WSNs) is essential to ensure data quality, secure monitoring and reliable detection of interesting and critical events. Principal Components Analysis (PCA) has attracted a great interest in the machine learning field especially in outlier detection in WSNs. An efficient and effective method called Improved Distributed PCA-Based Outlier Detection (IDPCA) has been proposed in this paper. The proposed scheme operates on each sensor node respectively, thus reducing the communication cost and prolonging the lifetime of the network. Through taking advantage of the data spatial correlation of adjacent nodes, the proposal can significantly reduce the false alarm rate and distinguish events and errors in real time. Experiments with both synthetic and the real data collected from the Intel Berkeley Research Laboratory indicate that IDPCA achieves a higher detection rate with a lower false alarm rate, while reducing the communication overhead than previous methods.
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Acknowledgments
This work is sponsored by The National Natural Science Foundation of China for Youth (Grant No. 61602263, No. 61572263), The Natural Science Foundation of Jiangsu Province, China (Grant No. BK20160916, No. BK20151507), The National post-doctoral fund (Grant No. 2017M621798), The NUPTSF (Grant No. NY216020).
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Zheng, W., Yang, L., Wu, M. (2018). An Improved Distributed PCA-Based Outlier Detection in Wireless Sensor Network. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11067. Springer, Cham. https://doi.org/10.1007/978-3-030-00018-9_4
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DOI: https://doi.org/10.1007/978-3-030-00018-9_4
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