scholarly journals A Method for Fault Detection in Wireless Sensor Network Based on Pearson’s Correlation Coefficient and Support Vector Machine Classification

2021 ◽  
Author(s):  
Priyajit Biswas ◽  
Tuhina Samanta
Keyword(s):  
Support Vector Machine ◽  
Wireless Sensor Network ◽  
Fault Detection ◽  
Sensor Network ◽  
Correlation Coefficient ◽  
Wireless Sensor ◽  
Support Vector ◽  
Pearson’S Correlation Coefficient ◽  
Support Vector Machine Classification ◽  
Pearson's Correlation

scholarly journals A Method for Fault Detection in Wireless Sensor Network Based on Pearson's Correlation Coefficient and Support Vector Machine Classification

2021 ◽  
Author(s):  
Priyajit Biswas ◽  
Tuhina Samanta
Keyword(s):  
Support Vector Machine ◽  
Fault Detection ◽  
Correlation Coefficient ◽  
Detection Algorithm ◽  
Support Vector ◽  
Operating Characteristics ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

Abstract Sensor nodes are tiny low-cost devices, prone to various faults. So, it is imperative to detect those faults. This paper presents a sensor measurement fault detection algorithm based on Pearson's correlation coefficient and the Support Vector Machine(SVM) algorithm. As environmental phenomena are spatially and temporally correlated but faults are somewhat uncorrelated, Pearson's correlation coefficient is used to measure correlation. Then we used SVM to classify faulty readings from normal reading. After classification, faulty readings are discarded. We used network simulator NS-2.35 and Matlab for evaluation of our proposed method. We evaluated our fault detection algorithm using performance metrics, namely, Accuracy, Precision, Sensitivity, Specificity, Recall, F1 Score, Geometric Mean(G_mean), Receiver Operating Characteristics (ROC), and Area Under Curve(AUC).

scholarly journals A node localization algorithm based on Voronoi diagram and support vector machine for wireless sensor networks

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199341
Author(s):  
Zhanjun Hao ◽  
Jianwu Dang ◽  
Yan Yan ◽  
Xiaojuan Wang
Keyword(s):  
Support Vector Machine ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Voronoi Diagram ◽  
Wireless Sensor ◽  
Selection Strategy ◽  
Support Vector ◽  
Localization Algorithm ◽  
Target Node ◽  
Localization Scheme

For wireless sensor network, the localization algorithm based on Voronoi diagram has been applied. However, the location accuracy node position in wireless sensor network needs to be optimized by the analysis of the literature, a node location algorithm based on Voronoi diagram and support vector machine is proposed in this article. The basic idea of the algorithm is to first divide the region into several parts using Voronoi diagram and anchor node in the localization region. The range of the initial position of the target node is obtained by locating the target node in each region and then the support vector machine is used to optimize the position of the target node accurately. The localization performance of the localization algorithm is analyzed by simulation and real-world experiments. The experimental results show that the localization algorithm proposed in this article is better than the optimal region selection strategy based on Voronoi diagram-based localization scheme and Weighted Voronoi diagram-based localization scheme localization algorithms in terms of localization accuracy. Therefore, the performance of the localization algorithm proposed in this article is verified.

scholarly journals A Review of Support Vector Machine-based Intrusion Detection System for Wireless Sensor Network with Different Kernel Functions

2021 ◽  
Vol 11 (1) ◽  
pp. 59-67
Author(s):  
Muhammad Amir Hamzah ◽  
Siti Hajar Othman
Keyword(s):  
Support Vector Machine ◽  
Wireless Sensor Network ◽  
Intrusion Detection ◽  
Sensor Network ◽  
Kernel Function ◽  
Intrusion Detection System ◽  
Detection System ◽  
Wireless Sensor ◽  
Experimental Result ◽  
Support Vector

Wireless sensor network (WSN) is among the popular communication technology which capable of self-configured and infrastructure-less wireless networks to monitor physical or environmental conditions. WSN also is the most standard services employed in commercial and industrial applications, because of its technical development in a processor, communication, and low-power usage of embedded computing devices. However, WSN is vulnerable due to the dynamic nature of wireless network. One of the best solutions to mitigate the risk is implementing Intrusion Detection System (IDS) to the network. Numerous researches were done to improve the efficiency of WSN-IDS because attacks in networks has been evolved due to the rapid growth of technology. Support Vector Machine (SVM) is one of the best algorithms for the enhancement of WSN-IDS. Nevertheless, the efficiency of classification in SVM is based on the kernel function used. Since dynamic environment of WSN consist of nonlinear data, linear classification of SVM has limitations in maximizing its margin during the classification. It is important to have the best kernel in classifying nonlinear data as the main goal of SVM to maximize the margin in the feature space during classification. In this research, kernel function of SVM such as Linear, RBF, Polynomial and Sigmoid were used separately in data classification. In addition, a modified version of KDD’99, NSL-KDD was used for the experiment of this research. Performance evaluation was made based on the experimental result obtained. Finally, this research found out that RBF kernel provides the best classification result with 91% accuracy.

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