Multivariate Stream Data Reduction in Sensor Network Applications

Seo, Sungbo; Kang, Jaewoo; Ryu, Keun Ho

doi:10.1007/11596042_21

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…The real-world environment consists of multivariate data, thus designing the multivariate dimension reduction is essential to address the power and memory constraint of the WSNs. Few multivariate data reduction models have been proposed in [ 46 , 47 , 48 , 49 , 50 ]. PCA is a popular multivariate data analysis method used to reduce the dimensionality of a set of correlated data observations into a set of uncorrelated variables known as principal components (PCs) [ 8 , 51 ].…”

Section: Related Workmentioning

confidence: 99%

Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Zamry

Zainal

Rassam

et al. 2021

Sensors

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Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead.

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Section: Related Workmentioning

confidence: 99%

Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Zamry

Zainal

Rassam

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Seo et al [ 16 ] carried out evaluations of some techniques for reducing the multivariate data traffic. These techniques are based on wavelet, sampling, hierarchical clustering and Singular Value Decomposition—SVD.…”

Section: Related Workmentioning

confidence: 99%

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

Carvalho

Gomes

Agoulmine

et al. 2011

Sensors

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This paper proposes a method based on multivariate spatial and temporal correlation to improve prediction accuracy in data reduction for Wireless Sensor Networks (WSN). Prediction of data not sent to the sink node is a technique used to save energy in WSNs by reducing the amount of data traffic. However, it may not be very accurate. Simulations were made involving simple linear regression and multiple linear regression functions to assess the performance of the proposed method. The results show a higher correlation between gathered inputs when compared to time, which is an independent variable widely used for prediction and forecasting. Prediction accuracy is lower when simple linear regression is used, whereas multiple linear regression is the most accurate one. In addition to that, our proposal outperforms some current solutions by about 50% in humidity prediction and 21% in light prediction. To the best of our knowledge, we believe that we are probably the first to address prediction based on multivariate correlation for WSN data reduction.

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“…Seo et al [6] carried out evaluations in some techniques for reducing the multivariate data flow. These techniques are based on wavelet, sampling, hierarchical clustering and Singular Value Decomposition -SVD.…”

Section: Related Workmentioning

confidence: 99%

Multiple linear regression to improve prediction accuracy in WSN data reduction

Carvalho

Gomes

Souza

et al. 2011

2011 7th Latin American Network Operations and Management Symposium

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Simple linear regression is usually used for WSN data reduction. The mechanism is concerned about energy consumption, but neglects the prediction accuracy. The prediction error from it is often ignored and inconsistencies are forwarded to the user application. This paper proposes to use a method based on multiple linear regression to improve prediction accuracy. The improvement is achieved by multivariate correlation of readings gathered by sensor nodes in field. Tests show that our solution outperforms some current solutions adopted in the literature.

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Multivariate Stream Data Reduction in Sensor Network Applications

Cited by 9 publications

References 11 publications

Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Improving Prediction Accuracy for WSN Data Reduction by Applying Multivariate Spatio-Temporal Correlation

Multiple linear regression to improve prediction accuracy in WSN data reduction

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