Abstract
Environmental monitoring is a typical application in wireless sensor networks (WSNs), the outlier detection of the sensor data streams is especially important. We put forward an outlier detection algorithm based on multidimensional kernel density estimation. Based on the hierarchical network model, the algorithm estimates the normal distribution model in the cluster head nodes with the latest data sample. Each distributed node computes the new data to identify the abnormal data by the kernel density estimation model. The proposed algorithm can compute the result online. It only spends little time to adjust the appropriate threshold to reduce its complexity. In addition, We also take the spatial and temporal correlation, multiple attribute correlation of sensor data into account, such that the result of outlier detection is very reliable. Theoretical analysis and simulation experimental results demonstrate that the outlier detection accuracy of the proposed algorithm is more than 98 % when the outlier rate p is within a reasonable range. With the increase of p, the outlier detection accuracy will decline gradually.
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References
Zhang, Y., Hamm, N.A.S., Meratnia, N.: Statistics-based Outlier Detection for Wireless Sensor Networks. J International Journal of Geographical Information Science. 26, 1373–1392 (2012)
Zhang, Y., Meratnia, N., Havinga, P.: Outlier Detection Techniques for Wireless Sensor Networks: A Survey. J. Communications Surveys & Tutorials. 12, 159–170 (2010)
Sheng, B., Li, Q., Mao, W.: Outlier Detection in Sensor Networks. In: 8th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 219–228. ACM press, Canada (2007)
Luo, X., Dong, M., Huang, Y.: On Distributed Fault-tolerant Detection in Wireless sensor networks. J. Computers, IEEE Transactions on 55, 58–70 (2006)
Ding, M., Chen, D., Xing, K.: Localized Fault-tolerant Event Boundary Detection in Sensor Networks. In: 24th Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 902–913. IEEE press, Piscataway (2005)
Chen, J., Kher, S., Somani, A.: Distributed Fault Detection of Wireless Sensor Networks. In: 2006 Workshop on Dependability Issue in Wireless Ad Hoc Networks and Sensor Networks, pp. 65–72. ACM press, New York (2006)
Palpanas, T., Papadopoulos, D., Kalogeraki, V.: Distributed Deviation Detection in Sensor Networks. J. ACM SIGMOD Rec. 32, 77–82 (2003)
Subramaniam, S., Palpanas, T., Papadopoulos, D.: Online outlier detection in sensor data using non-parametric models. In: 32nd International Conference on Very Large Data Bases, pp. 187–198. ACM press, Seoul (2006)
Babcock, B., Datar, M., Motwani, R.: Sampling from a moving window over streaming data. In: 13th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 633–634. ACM press, Germany (2002)
Boyinbode, O., Le, H., Takizawa, M.: A survey on clustering algorithms for wireless sensor networks. Int. J. Space-Based and Situated Comput. 1, 130–136 (2011)
Acknowledgments
This work was supported by the National Natural Science Foundations of China (Grant No. 61174023) and Zhejiang Provincial Natural Science Foundation of China (Grant No. Y1110791).
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Wu, P., Li, G., Zhu, H., Lu, W. (2015). Outlier Detection Method of Environmental Streams Based on Kernel Density Estimation. In: Sun, L., Ma, H., Fang, D., Niu, J., Wang, W. (eds) Advances in Wireless Sensor Networks. CWSN 2014. Communications in Computer and Information Science, vol 501. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46981-1_45
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DOI: https://doi.org/10.1007/978-3-662-46981-1_45
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