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• W2509223312 startingPage "101" @default.
• W2509223312 abstract "In this paper, we focus on detecting a special type of anomaly in wireless sensor network (WSN), which appears simultaneously in a collection of neighboring nodes and lasts for a significant period of time. Existing point-based techniques, in this context, are not very effective and efficient. With the proposed distributed segment-based recursive kernel density estimation, a global probability density function can be tracked and its difference between every two periods of time is continuously measured for decision making. Kullback-Leibler (KL) divergence is employed as the measure and, in order to implement distributed in-network estimation at a lower communication cost, several types of approximated KL divergence are proposed. In the meantime, an entropic graph-based algorithm that operates in the manner of centralized computing is realized, in comparison with the proposed KL divergence-based algorithms. Finally, the algorithms are evaluated using a real-world data set, which demonstrates that they are able to achieve a comparable performance at a much lower communication cost." @default.
• W2509223312 created "2016-09-16" @default.
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• W2509223312 date "2017-01-01" @default.
• W2509223312 modified "2023-09-26" @default.
• W2509223312 title "Distributed Segment-Based Anomaly Detection With Kullback–Leibler Divergence in Wireless Sensor Networks" @default.
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• W2509223312 doi "https://doi.org/10.1109/tifs.2016.2603961" @default.
• W2509223312 hasPublicationYear "2017" @default.
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