An adaptive sliding window for anomaly detection of time series in wireless sensor networks-Reference-Cited by-同舟云学术

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An adaptive sliding window for anomaly detection of time series in wireless sensor networks

Published:2022-01 Issue:1 Volume:28 Page:393-411
ISSN:1022-0038
Container-title:Wireless Networks
language:en
Short-container-title:Wireless Netw

Author:

Wang Zhongmin,Wang Yue^ORCID,Gao Cong,Wang Fengwei,Lin Tingwu,Chen Yanping

Funder

Scientific Research Program Funded by Shaanxi Provincial Education Department

International Science and Technology Cooperation Program of the Science and Technology Department of Shaanxi Province

Communication Soft Science Program of Ministry of Industry and Information Technology

Science and Technology Project in Shaanxi Province of China

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s11276-021-02852-3.pdf

Reference43 articles.

1. Saraswathi, S., Suresh, G. R., & Katiravan, J. (2019). False alarm detection using dynamic threshold in medical wireless sensor networks. Wireless Networks, 27(2), 925–937. https://doi.org/10.1007/s11276-019-02197-y

2. Lee, J. H., Kim, L. H., & Kwon, T. (2015). Flexicast: Energy-efficient software integrity checks to build secure industrial wireless active sensor networks. IEEE Transactions on Industrial Informatics, 12(1), 6–14. https://doi.org/10.1109/TII.2015.2469644

3. Kiani, F. (2018). Animal behavior management by energy-efficient wireless sensor networks. Computers and Electronics in Agriculture, 151, 478–484. https://doi.org/10.1016/j.compag.2018.06.046

4. Ayadi, A., Ghorbel, O., Obeid, A. M., & Abid, M. (2017). Outlier detection approaches for wireless sensor networks: A survey. Computer Networks, 129, 319–333. https://doi.org/10.1016/j.comnet.2017.10.007

5. Peng, S. (2019). Research on abnormal data detection algorithm in wireless sensor networks. Huaqiao University.

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