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• W2091504096 abstract "A major challenge for the management of low-cost sensor networks is how to ensure the integrity of the data collected, and how to detect unusual events. In this paper, we present a distributed algorithm for anomaly detection in wireless sensor networks, which reduces the amount of data that needs to be communicated through the network. Our approach learns an ellipsoidal boundary for normal data at each sensor, and introduces a method to cluster these ellipsoids at a global level in order to model normal behaviour in the network. We demonstrate that our approach can achieve greater accuracy in non-homogeneous sensing environments than existing methods, while achieving low communication and computational overhead in the network." @default.
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• W2091504096 date "2009-12-01" @default.
• W2091504096 modified "2023-09-23" @default.
• W2091504096 title "Anomaly detection by clustering ellipsoids in wireless sensor networks" @default.
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• W2091504096 doi "https://doi.org/10.1109/issnip.2009.5416818" @default.
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