Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine

Zhang, Yang; Meratnia, Nirvana; Havinga, Paul J.M.

doi:10.1016/j.adhoc.2012.11.001

Cited by 114 publications

(77 citation statements)

References 16 publications

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“…Sensor data is highly susceptible to various sources of errors such as changing environmental conditions which may produce noise or noise from other sources [10]. These noises can severely affect data transmitted to central base.…”

Section: Outlier Detection In Wireless Sensor Networkmentioning

confidence: 99%

Distributed and Efficient One-Class Outliers Detection Classifier in Wireless Sensors Networks

Ghorbel

Jmal

Abid

et al. 2015

Lecture Notes in Computer Science

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Abstract. In the data mining literature, many outlier detection models can be found. However, these models are not suitable for the energy constrained WSNs because they assumed the whole data is available in a central location for further analysis. In this paper, we propose Distributed and Efficient One-class Outliers Detection Classifier (DEOODC) based on Mahalanobis Kernel used for outlier detection in wireless sensor networks (WSNs). For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. 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. On account of the attractive capability, KPCA-based methods have been extensively investigated, and have showed excellent performance. 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 Intel Berkeley are reported showing that the proposed method performs better in finding outliers in wireless sensor networks when compared to the One-Class SVM detection approach. All computation are done in the original space, thus saving computing time using Mahalanobis Kernel.

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Section: Outlier Detection In Wireless Sensor Networkmentioning

confidence: 99%

Distributed and Efficient One-Class Outliers Detection Classifier in Wireless Sensors Networks

Ghorbel

Jmal

Abid

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…Normally, WSN tracking categorized as an event-driven. With machine learning dependent event monitoring strategy [19]. It is possible to acquire economical event detection and query processing remedies under restricted environment with limited query areas.…”

Section: Issues Of Query Processing and Event Recognitionmentioning

confidence: 99%

“…There are plenty of issues in the design of MAC protocols for WSNs, for instance, power intake, latency, and forecast precision etc., along with fundamental operational characteristic that plethora of sensors cooperates to effectively transfer data. Consequently, the MAC protocols need to be designed correctly to enable economic data transmission as well as coverage of the sensor nodes [19,20]. Lately, a number of machine learning strategies also has been suggested for designing suitable MAC protocols as well as increasing the performance of WSNs.…”

Section: Issues Of Query Processing and Event Recognitionmentioning

confidence: 99%

A Study of Machine Learning in Wireless Sensor Network

Khan¹,

Samad²

2017

ijcna

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-Within this Paper, a concept of machine learning strategies suggested. In this investigation to address the design issues in WSNs is introduced. As can be viewed within this paper, countless endeavors have induced up to now; several layout issues in wireless sensor networks have been remedied employing numerous machine learning strategies. Utilizing machine learning based algorithms in WSNs need to deem numerous constraints, for instance, minimal sources of the network application that really needs distinct events to be tracked as well as other operational and non-operational aspects.

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“…A distributed approach to outlier detection is performed in a principal component analysis (PCA)-based technique proposed by M Ahmadi Livani et al 16 The scheme reduces communication complexity and achieves comparable accuracy in WSNs. Two outlier detection techniques based on distributed and online are presented in Zhang et al 17 These techniques are achieved using a hyper-ellipsoidal one-class support vector machine (SVM) combined with the spatiotemporal correlation between sensor data. The objective of all above schemes is to improve detection accuracy and reduce false alarm.…”

Section: Introductionmentioning

confidence: 99%

A new approach of anomaly detection in wireless sensor networks using support vector data description

Zhu

et al. 2017

International Journal of Distributed Sensor Networks

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Anomaly detection is an important challenge in wireless sensor networks for some applications, which require efficient, accurate, and timely data analysis to facilitate critical decision making and situation awareness. Support vector data description is well applied to anomaly detection using a very attractive kernel method. However, it has a high computational complexity since the standard version of support vector data description needs to solve quadratic programming problem. In this article, an improved method on the basis of support vector data description is proposed, which reduces the computational complexity and is used for anomaly detection in energy-constraint wireless sensor networks. The main idea is to improve the computational complexity from the training stage and the decision-making stage. First, the strategy of training sample reduction is used to cut back the number of samples and then the sequential minimal optimization algorithm based on the second-order approximation is implemented on the sample set to achieve the goal of reducing the training time. Second, through the analysis of the decision function, the pre-image in the original space corresponding to the center of hyper-sphere in kernel feature space can be obtained. The decision complexity is reduced from O(l) to O(1) using the pre-image. Eventually, the experimental results on several benchmark datasets and real wireless sensor networks datasets demonstrate that the proposed method can not only guarantee detection accuracy but also reduce time complexity.

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Distributed online outlier detection in wireless sensor networks using ellipsoidal support vector machine

Cited by 114 publications

References 16 publications

Distributed and Efficient One-Class Outliers Detection Classifier in Wireless Sensors Networks

Distributed and Efficient One-Class Outliers Detection Classifier in Wireless Sensors Networks

A Study of Machine Learning in Wireless Sensor Network

A new approach of anomaly detection in wireless sensor networks using support vector data description

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