A new range-free localisation in wireless sensor networks using support vector machine

Wang, Zengfeng; Zhang, Hao; Lu, Tingting; Sun, Yongrong; Liu, Xing

doi:10.1080/00207217.2017.1357198

Cited by 19 publications

(8 citation statements)

References 27 publications

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“…Specific symbols used in Equations ( 81) and ( 82) are described in the paper. 89 Wang et al 90 proposed an LSVM-PCS algorithm for node localization that emerges with SVM and PCS. The approach requires the partition of sensor regions into polar grids.…”

Section: Improves Localization Performancementioning

confidence: 99%

A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

Yadav

Sharma

2022

Int J Communication

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Section: Improves Localization Performancementioning

confidence: 99%

A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

Yadav

Sharma

2022

Int J Communication

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“…Machine learning (ML) has recently been applied to position estimation for sensor nodes [13,14]. WSN localization techniques utilize various ML models, such as k-means or fuzzy c-means [15], random forests [16], artificial neural networks (ANN) [17], fuzzy logic (FL) [18], support vector machines (SVMs) [19], Bayesian models [20], principal component analysis (PCA) [21], and semi-supervised learning [22]. Most ML-based techniques consider localization to be a multi-classification problem.…”

Section: A Motivationmentioning

confidence: 99%

“…However, machine-learning models have recently been included to improve localization accuracy [25]. These integrated techniques include k-means clustering [26], artificial neural networks (ANNs) [27,28], fuzzy logic (FL) [18,[29][30], support vector machines (SVMs) [15,31], Bayesian optimization [20], principle component analysis (PCA) [21], and semi-supervised [32] or deep learning [33].…”

Section: The Related Workmentioning

confidence: 99%

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A Hybrid Approach Using Multistage Collaborative Calibration for Wireless Sensor Network Localization in 3D Environments

Liang

2020

IEEE Access

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Accurate localization is critical in the internet of things (IOT), especially for wireless sensor networks (WSNs). Location estimation can be affected by factors such as node density, topological diversity, and sensor coverage. As such, we propose a hybrid approach using multistage collaborative calibration for wireless sensor network localization, specifically in 3D environments. This technique integrates a Modified version of Light Gradient Boosting Model (MLGB), which is based on a regression scheme, a cooperative methodology, and a fine calibration model for collaborative fusion. These techniques were combined with quadrilateral shrunk centroid (QSC) and distance vector hop algorithms, using a multi-communication radius and an improved frog-leaping algorithm (DVMFL). In the first step, MLGB was used to correct for inhomogeneous localization estimation errors and RSSI data sparsity. As a result, the model is capable of adapting to high topological diversity (i.e., C-shape, H-shape, Sshape, and O-shape).Successive steps further improved prediction accuracy by using a screening cooperative anchor node strategy to increase node density and enhance the QSC-DVMFL fusion framework for fine position estimation. The proposed methodology was assessed in a series of validation, comparing it to other techniques. The results demonstrated a clear effectiveness and adaptability across a variety of factors that typically affect WSN localization. INDEX TERMS Wireless sensor network; Localization; Multistage collaborative calibration technique; Modified version of light GBM; Quadrilateral shrunk centroid;

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“…A transmit matrix was introduced to show the relation between hops and distances and to train the system, while SVM was used to find the unknown nodes in WSNs. Another range-free localization method has been developed using SVM classifier [18]. Recently, kernel-based approach is first proposed in isotropic and anisotropic WSN [19].…”

Section: Introductionmentioning

confidence: 99%

A Kernel-Based Node Localization in Anisotropic Wireless Sensor Network

Chen

et al. 2021

Scientific Programming

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Wireless sensors localization is still the main problem concerning wireless sensor networks (WSN). Unfortunately, range-free node localization of WSN results in a fatal weakness–, low accuracy. In this paper, we introduce kernel regression to node localization of anisotropic WSN, which transfers the problem of localization to the problem of kernel regression. Radial basis kernel-based G-LSVR and polynomial-kernel-based P-LSVR proposed are compared with classical DV-Hop in both isotropic WSN and anisotropic WSN under different proportion beacons, network scales, and disturbances of communication range. G-LSVR presents the best localization accuracy and stability from the simulation results.

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A new range-free localisation in wireless sensor networks using support vector machine

Cited by 19 publications

References 27 publications

A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

A Systematic Review of Localization in WSN: Machine Learning and Optimization‐Based approaches

A Hybrid Approach Using Multistage Collaborative Calibration for Wireless Sensor Network Localization in 3D Environments

A Kernel-Based Node Localization in Anisotropic Wireless Sensor Network

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