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Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication

Gang, Qiao; Muhammad, Aman; Khan, Zahid Ullah; Khan, Muhammad Shahbaz; Ahmed, Fawad; Ahmad, Jawad

Authors

Qiao Gang

Aman Muhammad

Zahid Ullah Khan

Muhammad Shahbaz Khan

Fawad Ahmed



Abstract

This study aims to realize Sustainable Development Goals (SDGs), i.e., SDG 9: Industry Innovation and Infrastructure and SDG 14: Life below Water, through the improvement of localization estimation accuracy in magneto-inductive underwater wireless sensor networks (MI-UWSNs). The accurate localization of sensor nodes in MI communication can effectively be utilized for industrial IoT applications, e.g., underwater gas and oil pipeline monitoring, and in other important underwater IoT applications, e.g., smart monitoring of sea animals, etc. The most-feasible technology for medium- and short-range communication in IIoT-based UWSNs is MI communication. To improve underwater communication, this paper presents a machine learning-based prediction of localization estimation accuracy of randomly deployed sensor Rx nodes through anchor Tx nodes in the MI-UWSNs. For the training of ML models, extensive simulations have been performed to create two separate datasets for the two configurations of excitation current provided to the Tri-directional (TD) coils, i.e., configuration1-case1_configuration2-case1 (c1c1_c2c1) and configuration1-case2_configuration2-case2 (c1c2_c2c2). Two ML models have been created for each case. The accuracies of both models lie between 95% and 97%. The prediction results have been validated by both the test dataset and verified simulation results. The other important contribution of this paper is the development of a novel assembling technique of a MI-TD coil to achieve an approximate omnidirectional magnetic flux around the communicating coils, which, in turn, will improve the localization accuracy of the Rx nodes in IIoT-based MI-UWSNs.

Citation

Gang, Q., Muhammad, A., Khan, Z. U., Khan, M. S., Ahmed, F., & Ahmad, J. (2022). Machine Learning-Based Prediction of Node Localization Accuracy in IIoT-Based MI-UWSNs and Design of a TD Coil for Omnidirectional Communication. Sustainability, 14(15), Article 9683. https://doi.org/10.3390/su14159683

Journal Article Type Article
Acceptance Date Aug 4, 2022
Online Publication Date Aug 6, 2022
Publication Date 2022
Deposit Date Aug 15, 2022
Publicly Available Date Aug 15, 2022
Journal Sustainability
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 14
Issue 15
Article Number 9683
DOI https://doi.org/10.3390/su14159683
Keywords localization; magneto inductive communication; underwater wireless sensor networks; machine learning; linear regression; ultrareliable low latency communication
Public URL http://researchrepository.napier.ac.uk/Output/2896380

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