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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.
Highlights
Wireless sensor networks (WSNs) are composed of a large number of sensor nodes, which are equipped with wireless transceivers, low-power microcontrollers, energy sources and various sensors [1]
To evaluate the performance of the proposed outlier detection algorithm, we carried out several simulation experiments on two WSN data sets, and compared the results of TSVDD with those of FastFood [23], RFF [17] and the traditional kernel_SVDD
The following four metrics can be calculated from the confusion matrix: True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate (FPR), and False Negative Rate (FNR)
Summary
WSNs are composed of a large number of sensor nodes, which are equipped with wireless transceivers, low-power microcontrollers, energy sources and various sensors [1]. Andrea [21] proposed an explicit description of the reproducing kernel Hilbert space (RKHS) induced by the approximated Gaussian kernel They demonstrated that the approximations had indistinguishable performance from the full kernels yet greatly reduce the train/test times of SVMs. The majority of existing outlier detection methods for WSNs does not take into account multidimensional data and assume the sensor data is univariate. For outlier detection in WSNs, it is necessary to maintain a high accuracy at a low feature space dimension To meet this goal, this paper proposes an outlier detection method (TSVDD) using model selection-based SVDD, and this method can analyze multi-dimensional sensor data and improve the accuracy of outlier detection.
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