Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches

Yadav, Preeti; Sharma, S. C.

doi:10.1007/s11277-023-10630-x

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Cited by 6 publications

References 156 publications

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Comparative Analysis of Chicken Swarm Optimization and IbI Logics Algorithm for Multiobjective Optimization in k-Coverage and m-Connectivity Problem

Afizudeen,

Pavithra

2024

Applied Soft Computing and Communication Networks

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Comparative Analysis of Chicken Swarm Optimization and IbI Logics Algorithm for Multiobjective Optimization in k-Coverage and m-Connectivity Problem

Afizudeen,

Pavithra

2024

Applied Soft Computing and Communication Networks

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Machine Learning-Based Node Localization in IoT-Assisted WSN: An Initial Framework for Real-Time Applications

Yadav,

Arora,

Sharma

et al. 2024

Lecture Notes in Networks and Systems

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Using fuzzy transforms for neural networks-based wireless localization in outdoor environments

Solmann,

Loffredo,

Tomasiello

2024

Neural Comput & Applic

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As neural network-based localization algorithms are becoming popular, there is a need to shorten the training time and the localization time for sustainability and efficiency purposes. To address such issues, the fuzzy transform (or F-transform for short) is employed here for the first time in a neural network-based localization algorithm. The F-transform is a dimensionality reduction method, which has found several applications over the last decade, but it has not been well explored in the form of a prepending layer to a neural network. In this respect, some properties (including the computational cost) of the F-transformed neural scheme are formally discussed here. The performance of the neural network-based approach with and without F-transform, and with a state-of-the-art reduction technique, i.e. the principal component analysis, is evaluated first on simulated data and then on publicly available real-world data. Different neural network architectures have been tried jointly with the above-mentioned reduction techniques. The numerical experiments show the excellent performance of the proposed fuzzy transform-based approach, which can ensure considerable savings in training time and query response time, without significant losses in accuracy.

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Unveiling the Cutting Edge: A Comprehensive Survey of Localization Techniques in WSN, Leveraging Optimization and Machine Learning Approaches

Cited by 6 publications

References 156 publications

Comparative Analysis of Chicken Swarm Optimization and IbI Logics Algorithm for Multiobjective Optimization in k-Coverage and m-Connectivity Problem

Comparative Analysis of Chicken Swarm Optimization and IbI Logics Algorithm for Multiobjective Optimization in k-Coverage and m-Connectivity Problem

Machine Learning-Based Node Localization in IoT-Assisted WSN: An Initial Framework for Real-Time Applications

Using fuzzy transforms for neural networks-based wireless localization in outdoor environments

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