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Using sparse representation to detect anomalies in complex WSNs

Using sparse representation to detect anomalies in complex WSNs

Li, Xiaoming, Xu, Guangquan, Zheng, Xi, Liang, Kaitai, Panaousis, Emmanouil ORCID: 0000-0001-7306-4062, Li, Tao, Wang, Wei and Shen, Chao (2019) Using sparse representation to detect anomalies in complex WSNs. ACM Transactions on Intelligent Systems and Technology, 10 (6):64. ISSN 2157-6904 (Print), 2157-6912 (Online) (doi:

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In recent years, wireless sensor networks (WSNs) have become an active area of research for monitoring physical and environmental conditions. Due to the interdependence of sensors, a functional anomaly in one sensor can cause a functional anomaly in another sensor, which can further lead to the malfunctioning of the entire sensor network. Existing research work has analysed faulty sensor anomalies, but fails to show the effectiveness throughout the entire interdependent network system. In this paper, a dictionary learning algorithm based on a non-negative constraint is developed, and a sparse representation anomaly node detection method for sensor networks is proposed based on the dictionary learning. Through experiment on a specific thermal power plant in China, we verify the robustness of our proposed method in detecting abnormal nodes against four state of the art approaches and proved our method is more robust. Furthermore, the experiments are conducted on the obtained abnormal nodes to prove the interdependence of multi-layer sensor networks and reveal the conditions and causes of a system crash.

Item Type: Article
Uncontrolled Keywords: wireless sensor networks, anomaly detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Internet of Things and Security (ISEC)
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 26 Nov 2020 22:34
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: GREAT 2
Selected for REF2021: None

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