Outlier detection algorithm based on variable-width histogram for wireless sensor network

Jiang, Xubao; Li, Guangyao; Lian, Shuo

doi:10.3724/sp.j.1087.2011.00694

Cited by 2 publications

(3 citation statements)

References 2 publications

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“…At present, the anomaly detection methods for wireless sensor networks are mainly divided into statistical method [3][4], clustering method [5][6] and classification based method [7][8][9][10]. For example, researchers proposed an anomaly detection method based on hypothetical mathematical statistical model and kernel density function [3].…”

Section: Introductionmentioning

confidence: 99%

“…For example, researchers proposed an anomaly detection method based on hypothetical mathematical statistical model and kernel density function [3]. In addition, an abnormal data detection algorithm for wireless sensor networks based on variable width histogram is proposed [4]. This algorithm reduce data transmission volume furtherly and saving communication cost By collecting histogram information of data flow distribution in wireless sensor network and dynamically merging histogram interval which can change the width of original interval.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network

Shen¹,

Shen²,

Zhu³

et al. 2022

Journal of Compurters

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<p>Abnormal data detection is an important step to ensure the accuracy and reliability of node data in wireless sensor networks. In this paper, a data classification method based on convolutional neural network is proposed to solve the problem of data anomaly detection in wireless sensor networks. First, Normal data and abnormal data generated after injection fault are normalized and mapped to gray image as input data of the convolutional neural network. Then, based on the classical convolution neural network, three new convolutional neural network models are designed by designing the parameters of the convolutional layer and the fully connected layer. This model solves the problem that the performance of traditional detection algorithm is easily affected by relevant threshold through self-learning data characteristics of convolution layer. The experimental results show that this method has better detection performance and higher reliability.</p> <p> </p>

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network

Shen¹,

Shen²,

Zhu³

et al. 2022

Journal of Compurters

View full text Add to dashboard Cite

show abstract

“…The data mining method involves the abnormal detection based on the clustering and the abnormal detection based on the proximity [2,3]. Statistical methods assume that the data meet a distributed model or a probabilistic model [4]. However in practice, the data distribution is usually unknown.…”

Section: Introductionmentioning

confidence: 99%

An abnormal data detection method based on temporal-spatial correlation in WSNs

Wang¹

2016

Proceedings of the Fourth International Conference on Information Science and Cloud Computing — PoS(ISCC2015)

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In recent years, the abnormal data detection has played a vital role in the environmental monitoring of wireless sensor networks (WSNs). However, several key issues in the abnormal data detection still remain to be solved, such as the real time capability of the detection and the classification of the abnormal data. To meet the classification requirements, this paper proposes an abnormal data detection method based on the temporal-spatial correlation in WSNs. Spatial and temporal correlations of the data in WSNs are analyzed in this paper. In addition, abnormal data detection methods of two dimensions are proposed respectively. By Combining the detection results of these two dimensions, the abnormal data can be found and classified accurately. Simulations show that the method proposed in this paper can guarantee both rationality and accuracy for WSNs.

show abstract

Outlier detection algorithm based on variable-width histogram for wireless sensor network

Cited by 2 publications

References 2 publications

A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network

A Method for Detecting Abnormal Data of Network Nodes Based on Convolutional Neural Network

An abnormal data detection method based on temporal-spatial correlation in WSNs

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