Abstract
Anomaly detection in wireless sensor networks plays a vital role to ensure the accuracy and reliability of network data. In view of the complexity of sensor networks, the process of detecting anomalies using raw data is complicated, which also requires abundant storage spaces. This paper proposes a WSN anomaly detection algorithm based on graph signals. Use the graph model of the WSN to construct the covariance matrix, so as to realize the anomaly detection of the sensor network. Also, this paper selects a subset of edges with high correlation to construct a new graph to optimize the topology of the sensor network graph. The performance of the algorithm is tested and analyzed on the Intel_Lab_Data.
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Acknowledgements
The work was supported by the Natural Science Foundation of China (61731006, 61971310)
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Zhu, Q., Zhou, J., Zhao, S., Wang, W. (2022). Graph-Based Anomaly Detection of Wireless Sensor Network. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_18
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DOI: https://doi.org/10.1007/978-981-16-9423-3_18
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