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 3D Localization Algorithm Based on Voronoi Diagram and Rank Sequence in Wireless Sensor Network

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xi Yang ◽  
Fang Yan ◽  
Jun Liu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Voronoi Diagram ◽  
Optimal Parameter ◽  
Rank Correlation ◽  
Correlation Coefficients ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
3D Space

Accurate nodes’ localization is a key problem in wireless sensor network (WSN for short). This paper discusses and analyzes the effects of Voronoi diagram in 3D location space. Then it proposes Sequence Localization Correction algorithm based on 3D Voronoi diagram (SLC3V), which introduces 3D Voronoi diagram to divide the 3D location space and constructs the rank sequence tables of virtual beacon nodes. SLC3V uses RSSI method between beacon nodes as a reference to correct the measured distance and fixes the location sequence of unknown nodes. Next, it selects optimal parameterNand realizes the weighted location estimate withNvalid virtual beacon nodes by normalization process of rank correlation coefficients. Compared with other sequence location algorithms, simulation experiments show that it can improve the localization accuracy for nodes in complex 3D space with less measurements and computational costs.

scholarly journals Wireless Sensor Network Localization Based On A Mobile Beacon And Tsvm

2014 ◽  
Vol 14 (5) ◽  
pp. 98-107 ◽  
Author(s):  
Jiang Xu ◽  
Huanyan Qian ◽  
Huan Dai ◽  
Jianxin Zhu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Localization Algorithm ◽  
Sensor Network Localization ◽  
Network Localization ◽  
Gps Devices ◽  
Mobile Beacon

Abstract In this paper a new wireless sensor network localization algorithm, based on a mobile beacon and TSVM (Transductive Support Vector Machines) is proposed, which is referred to as MTSVM. The new algorithm takes advantage of a mobile beacon to generate virtual beacon nodes and then utilizes the beacon vector produced by the communication between the nodes to transform the problem of localization into one of classification. TSVM helps to minimize the error of classification of unknown fixed nodes (unlabeled samples). An auxiliary mobile beacon is designed to save the large volumes of expensive sensor nodes with GPS devices. As shown by the simulation test, the algorithm achieves good localization performance.

scholarly journals A Robust Indoor Mobile Localization Algorithm for Wireless Sensor Network in Mixed LOS/NLOS Environments

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Ou Yong Kang ◽  
Cheng Long
Keyword(s):  
Kalman Filter ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Unscented Kalman Filter ◽  
Wireless Sensor ◽  
Selection Strategy ◽  
Time Of Arrival ◽  
Multiple Model ◽  
Localization Algorithm ◽  
Extend Kalman Filter

Wireless sensor network (WSN) is a self-organizing network which is composed of a large number of cheap microsensor nodes deployed in the monitoring area and formed by wireless communication. Since it has the characteristics of rapid deployment and strong resistance to destruction, the WSN positioning technology has a wide application prospect. In WSN positioning, the nonline of sight (NLOS) is a very common phenomenon affecting accuracy. In this paper, we propose a NLOS correction method algorithm base on the time of arrival (TOA) to solve the NLOS problem. We firstly propose a tendency amendment algorithm in order to correct the NLOS error in geometry. Secondly, this paper propose a particle selection strategy to select the standard deviation of the particle swarm as the basis of evolution and combine the genetic evolution algorithm, the particle filter algorithm, and the unscented Kalman filter (UKF) algorithm. At the same time, we apply orthogon theory to the UKF to make it have the ability to deal with the target trajectory mutation. Finally we use maximum likelihood localization (ML) to determine the position of the mobile node (MN). The simulation and experimental results show that the proposed algorithm can perform better than the extend Kalman filter (EKF), Kalman filter (KF), and robust interactive multiple model (RIMM).

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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