Machine Learning Based Techniques for Node Localization in WSN: A Survey

Yadav, Preeti; Kumar, Krishan; Sharma, Subhash C.

doi:10.1109/dicct56244.2023.10110235

Cited by 10 publications

(1 citation statement)

References 27 publications

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“…In earlier studies, researchers utilized machine learning (ML) and deep learning (DL) techniques to enhance localization performance. Various supervised learning algorithms, including the Support Vector Machine (SVM) and Artificial Neural Network (ANN), have been employed for localization purposes [13]. For the implementation of deep learning, there exists the need for extensive training datasets.…”

Section: Introductionmentioning

confidence: 99%

Efficient Node Localization on Sensor Internet of Things Networks Using Deep Learning and Virtual Node Simulation

Kanwar,

Aydin

2024

Electronics

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Localization is a primary concern for wireless sensor networks as numerous applications rely on the precise position of nodes. This paper presents a precise deep learning (DL) approach for DV-Hop localization in the Internet of Things (IoT) using the whale optimization algorithm (WOA) to alleviate shortcomings of traditional DV-Hop. Our method leverages a deep neural network (DNN) to estimate distances between undetermined nodes (non-coordinated nodes) and anchor nodes (coordinated nodes) without imposing excessive costs on IoT infrastructure, while DL techniques require extensive training data for accuracy, we address this challenge by introducing a data augmentation strategy (DAS). The proposed algorithm involves creating virtual anchors strategically around real anchors, thereby generating additional training data and significantly enhancing dataset size, improving the efficacy of DNNs. Simulation findings suggest that the proposed deep learning model on DV-Hop localization outperforms other localization methods, particularly regarding positional accuracy.

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Section: Introductionmentioning

confidence: 99%