Event Detection in Wireless Sensor Networks – Can Fuzzy Values Be Accurate?

Kapitanova, Krasimira; Son, Sang H.; Kang, Kyoung-Don

doi:10.1007/978-3-642-17994-5_12

Cited by 21 publications

(14 citation statements)

References 31 publications

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“…KPCA maps the original inputs into a high dimensional feature space using a kernel method [6]. Mathematically, we transform the current features into a high-dimensional space and the calculate eigenvectors in this space.…”

Section: Related Workmentioning

confidence: 99%

Improved KPCA for outlier detection in Wireless Sensor Networks

Ghorbel¹,

Abid²,

Snoussi³

2014

2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

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In Wireless Sensor Networks (WSNs), the accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. Over the last years, Principal component analysis (PCA) has shown to be a good unsupervised feature extraction. But, this method only focuses on second orders statistics. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. Kernel PCA (KPCA) mapping the data onto another feature space and using nonlinear function. So, we propose an improved KPCA method based on Mahalanobis kernel as a preprocessing step to extract relevant feature for classification and to prevent from the abnormal events. All computation are done in the original space, thus saving computing time using Mahalanobis Kernel (MKPCA). Then the classification was done on real hyperspectral Intel Berkeley data from urban area. Results were positively compared to a version of a standard KPCA specially designed to be use with wireless sensor networks (WSNs).

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Section: Related Workmentioning

confidence: 99%

Improved KPCA for outlier detection in Wireless Sensor Networks

Ghorbel¹,

Abid²,

Snoussi³

2014

2014 1st International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

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“…The model of fuzzy logic control consists of a fuzzifier, fuzzy rules, fuzzy inference engine, and a defuzzifier [12]. The crisp data is converted into fuzzy sets by the fuzzifier.…”

Section: Fuzzy Modelmentioning

confidence: 99%

Cluster Head Election with Hexagonal Node Deployment Technique in Wireless Sensor Networks

Sharma¹,

Tomar

Berry³

et al. 2016

IJFGCN

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“…So number of principle components that need to be estimated is also much larger. The KPCA method has exhibited superior performance compared to linear PC analysis method in processing nonlinear systems [8], [9]. The detail introduction of the basic KPCA can be viewed in [10].…”

Section: Related Workmentioning

confidence: 99%

A Novel Outlier Detection Model Based on One Class Principal Component Classifier in Wireless Sensor Networks

Ghorbel

Abid

Snoussi

2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

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Wireless sensor networks (WSNs) are important platforms for collecting environmental data and monitoring phenomena. So, outlier detection process is a necessary step in building sensor network systems to assure data quality for perfect decision making. Over the last few years Kernel Principal Component Analysis (KPCA) is considered as a natural nonlinear generalization of PCA, which extracts nonlinear structure from the data. Wireless sensor networks had been deployed in the real world to collect large amounts of raw sensed data. Then, the key challenge is to extract high level knowledge from such raw data. So, the accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. However, KPCA based reconstruction error (RE) has found several applications in outlier detection but is not perfect to detect outlier. Within this setting, we propose Kernel Principal Component Analysis based Mahalanobis kernel as a new outlier detection method using mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate outlier points from normal pattern of data distribution. The use of KPCA based mahalanobis kernel on real word data obtained from three real datasets are reported showing that the proposed method performs better in finding outliers in wireless sensor networks when compared to the original RE based variant and the One-Class SVM detection approach.

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Event Detection in Wireless Sensor Networks – Can Fuzzy Values Be Accurate?

Cited by 21 publications

References 31 publications

Improved KPCA for outlier detection in Wireless Sensor Networks

Improved KPCA for outlier detection in Wireless Sensor Networks

Cluster Head Election with Hexagonal Node Deployment Technique in Wireless Sensor Networks

A Novel Outlier Detection Model Based on One Class Principal Component Classifier in Wireless Sensor Networks

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