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Sensors 2018, 18(12), 4328; https://doi.org/10.3390/s18124328

Outlier Detection in Wireless Sensor Networks Using Model Selection-Based Support Vector Data Descriptions

1
,
2,3
and
2,3,*
1
School of Information Science & Engineering, Changzhou University, Changzhou 213164, China
2
School of IoT Enginering, Jiangnan University, Wuxi 214122, China
3
Research Center of IoT Technology Application Engineering (MOE), Wuxi 214122, China
*
Author to whom correspondence should be addressed.
Received: 12 October 2018 / Revised: 18 November 2018 / Accepted: 22 November 2018 / Published: 7 December 2018
(This article belongs to the Section Sensor Networks)
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Abstract

Wireless sensor networks (WSNs) are often deployed in harsh and unattended environments, which may cause the generation of abnormal or low quality data. The inaccurate and unreliable sensor data may increase generation of false alarms and erroneous decisions, so it’s very important to detect outliers in sensor data efficiently and accurately to ensure sound scientific decision-making. In this paper, an outlier detection algorithm (TSVDD) using model selection-based support vector data description (SVDD) is proposed. Firstly, the Toeplitz matrix random feature mapping is used to reduce the time and space complexity of outlier detection. Secondly, a novel model selection strategy is realized to keep the algorithm stable under the low feature dimensions, this strategy can select a relatively optimal decision model and avoid both under-fitting and overfitting phenomena. The simulation results on SensorScope and IBRL datasets demonstrate that, TSVDD achieves higher accuracy and lower time complexity for outlier detection in WSNs compared with existing methods. View Full-Text
Keywords: outlier detection; wireless sensor networks; support vector data description; random feature mapping; model selection outlier detection; wireless sensor networks; support vector data description; random feature mapping; model selection
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Huan, Z.; Wei, C.; Li, G.-H. Outlier Detection in Wireless Sensor Networks Using Model Selection-Based Support Vector Data Descriptions. Sensors 2018, 18, 4328.

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