2024
DOI: 10.3390/s24020430
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Effectiveness of Data Augmentation for Localization in WSNs Using Deep Learning for the Internet of Things

Jehan Esheh,
Sofiene Affes

Abstract: Wireless sensor networks (WSNs) have become widely popular and are extensively used for various sensor communication applications due to their flexibility and cost effectiveness, especially for applications where localization is a main challenge. Furthermore, the Dv-hop algorithm is a range-free localization algorithm commonly used in WSNs. Despite its simplicity and low hardware requirements, it does suffer from limitations in terms of localization accuracy. In this article, we develop an accurate Deep Learni… Show more

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Cited by 5 publications
(1 citation statement)
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“…Furthermore, data augmentation can assist address challenges linked with limited real-world data, improving the generalization ability of the deep learning model to unseen environments. Data augmentation is implemented to enhance the accuracy of datasets prediction models by increasing the sample size through the integration of artificially generated data [33].…”
Section: Data Augmentation In Localizationmentioning
confidence: 99%
“…Furthermore, data augmentation can assist address challenges linked with limited real-world data, improving the generalization ability of the deep learning model to unseen environments. Data augmentation is implemented to enhance the accuracy of datasets prediction models by increasing the sample size through the integration of artificially generated data [33].…”
Section: Data Augmentation In Localizationmentioning
confidence: 99%