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

Qiao Gang, Aman Muhammad (), Zahid Ullah Khan, Muhammad Shahbaz Khan, Fawad Ahmed and Jawad Ahmad
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Qiao Gang: Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
Aman Muhammad: Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
Zahid Ullah Khan: Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China
Muhammad Shahbaz Khan: Department of Electrical Engineering, Heavy Industries Taxila Education City (HITEC) University, Taxila 47080, Pakistan
Fawad Ahmed: Department of Cyber Security, Pakistan Navy Engineering College, National University of Science and Technology (NUST), Karachi 75350, Pakistan
Jawad Ahmad: School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK

Sustainability, 2022, vol. 14, issue 15, 1-23

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.

Keywords: localization; magneto inductive communication; underwater wireless sensor networks; machine learning; linear regression; ultrareliable low latency communication (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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