What is it about?
Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). In this paper, we exploit the sensor reading over a period of time for temporal outlier detection and the readings from 2‐hop neighbor nodes of the candidate node for spatial outlier detection.
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Why is it important?
Outlier detection techniques play an important role in enhancing the reliability of data communication in wireless sensor networks (WSNs). Considering the importance of outlier detection in WSNs, many outlier detection techniques have been proposed. Unfortunately, most of these techniques still have some potential limitations, that is, (a) high rate of false positives, (b) high time complexity, and (c) failure to detect outliers online. Moreover, these approaches mainly focus on either temporal outliers or spatial outliers. Therefore, this paper aims to introduce novel algorithms that successfully detect both temporal outliers and spatial outliers. Our contributions are twofold: (i) modifying the Hampel Identifier (HI) algorithm to achieve high accuracy identification rate in temporal outlier detection, (ii) combining the Gaussian process (GP) model and graph‐based outlier detection technique to improve the performance of the algorithm in spatial outlier detection. The results demonstrate that our techniques outperform the state‐of‐the‐art methods in terms of accuracy and work well with various data types.
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This page is a summary of: Temporal and spatial outlier detection in wireless sensor networks, ETRI Journal, July 2019, Wiley,
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