Exploiting Linear Support Vector Machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks

Linear Support Vector Machine (LSVM) has proven to be an effective approach for link classification in sensor networks. In this paper, we present a data-driven framework for reliable link classification that models Kernelized Linear Support Vector Machine (KLSVM) to produce stable and consistent res...
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Autor*in:	Lawrence Mwenda Muriira [verfasserIn] Zhiwei Zhao [verfasserIn] Geyong Min [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	linear support vector machine linear kernel correlation high dimensional multi-category data classification wireless sensor network

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 18(2018), 9, p 2840
Übergeordnetes Werk:	volume:18 ; year:2018 ; number:9, p 2840

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s18092840

Katalog-ID:	DOAJ032908504

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language	English
source	In Sensors 18(2018), 9, p 2840 volume:18 year:2018 number:9, p 2840
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topic_facet	linear support vector machine linear kernel correlation high dimensional multi-category data classification wireless sensor network Chemical technology
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authorswithroles_txt_mv	Lawrence Mwenda Muriira @@aut@@ Zhiwei Zhao @@aut@@ Geyong Min @@aut@@
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callnumber-first	T - Technology
author	Lawrence Mwenda Muriira
spellingShingle	Lawrence Mwenda Muriira misc TP1-1185 misc linear support vector machine misc linear kernel misc correlation misc high dimensional multi-category data classification misc wireless sensor network misc Chemical technology Exploiting Linear Support Vector Machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks
authorStr	Lawrence Mwenda Muriira
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topic_title	TP1-1185 Exploiting Linear Support Vector Machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks linear support vector machine linear kernel correlation high dimensional multi-category data classification wireless sensor network
topic	misc TP1-1185 misc linear support vector machine misc linear kernel misc correlation misc high dimensional multi-category data classification misc wireless sensor network misc Chemical technology
topic_unstemmed	misc TP1-1185 misc linear support vector machine misc linear kernel misc correlation misc high dimensional multi-category data classification misc wireless sensor network misc Chemical technology
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title	Exploiting Linear Support Vector Machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks
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title_sort	exploiting linear support vector machine for correlation-based high dimensional data classification in wireless sensor networks
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title_auth	Exploiting Linear Support Vector Machine for Correlation-Based High Dimensional Data Classification in Wireless Sensor Networks
abstract	Linear Support Vector Machine (LSVM) has proven to be an effective approach for link classification in sensor networks. In this paper, we present a data-driven framework for reliable link classification that models Kernelized Linear Support Vector Machine (KLSVM) to produce stable and consistent results. KLSVM is a linear classifying technique that learns the “best” parameter settings. We investigated its application to model and capture two phenomena: High dimensional multi-category classification and Spatiotemporal data correlation in wireless sensor network (WSN). In addition, the technique also detects anomalies within the network. With the optimized selection of the linear kernel hyperparameters, the technique models high-dimensional data classification and the examined packet traces exhibit correlations between link features. Link features with Packet Reception Rate (PRR) greater than 50% show a high degree of negative correlation while the other sensor node observations show a moderate degree of positive correlation. The model gives a good visual intuition of the network behavior. The efficiency of the supervised learning technique is studied over real dataset obtained from a WSN testbed. To achieve that, we examined packet traces from the 802.15.4 network. The technique has a good performance on link quality estimation accuracy and a precise anomaly detection of sensor nodes within the network.
abstractGer	Linear Support Vector Machine (LSVM) has proven to be an effective approach for link classification in sensor networks. In this paper, we present a data-driven framework for reliable link classification that models Kernelized Linear Support Vector Machine (KLSVM) to produce stable and consistent results. KLSVM is a linear classifying technique that learns the “best” parameter settings. We investigated its application to model and capture two phenomena: High dimensional multi-category classification and Spatiotemporal data correlation in wireless sensor network (WSN). In addition, the technique also detects anomalies within the network. With the optimized selection of the linear kernel hyperparameters, the technique models high-dimensional data classification and the examined packet traces exhibit correlations between link features. Link features with Packet Reception Rate (PRR) greater than 50% show a high degree of negative correlation while the other sensor node observations show a moderate degree of positive correlation. The model gives a good visual intuition of the network behavior. The efficiency of the supervised learning technique is studied over real dataset obtained from a WSN testbed. To achieve that, we examined packet traces from the 802.15.4 network. The technique has a good performance on link quality estimation accuracy and a precise anomaly detection of sensor nodes within the network.
abstract_unstemmed	Linear Support Vector Machine (LSVM) has proven to be an effective approach for link classification in sensor networks. In this paper, we present a data-driven framework for reliable link classification that models Kernelized Linear Support Vector Machine (KLSVM) to produce stable and consistent results. KLSVM is a linear classifying technique that learns the “best” parameter settings. We investigated its application to model and capture two phenomena: High dimensional multi-category classification and Spatiotemporal data correlation in wireless sensor network (WSN). In addition, the technique also detects anomalies within the network. With the optimized selection of the linear kernel hyperparameters, the technique models high-dimensional data classification and the examined packet traces exhibit correlations between link features. Link features with Packet Reception Rate (PRR) greater than 50% show a high degree of negative correlation while the other sensor node observations show a moderate degree of positive correlation. The model gives a good visual intuition of the network behavior. The efficiency of the supervised learning technique is studied over real dataset obtained from a WSN testbed. To achieve that, we examined packet traces from the 802.15.4 network. The technique has a good performance on link quality estimation accuracy and a precise anomaly detection of sensor nodes within the network.
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