Localization of sensor nodes in the internet of underwater things (IoUT) is of considerable significance due to its various applications, such as navigation, data tagging, and detection of underwater objects. Therefore, in this paper, we propose a hybrid Bayesian multidimensional scaling (BMDS) based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical, magnetic induction, and acoustic technologies. These communi-cation technologies are already used for communication in the underwater environment; however, lacking localization solutions. Optical and magnetic induction communication achieves higher data rates for short communication. On the contrary, acoustic waves provide a low data rate for long-range underwater communication. The proposed method collectively uses optical, magnetic induction, and acoustic communication-based ranging to estimate the underwater sensor nodes' final locations. Moreover, we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound (H-CRLB). Simulation results provide a complete comparative analysis of the proposed method with the literature.

Department of Electrical Engineering,Faculty of Electrical and Computer Engineering,University of Engineering and Technology,Peshawar 25120,Pakistan;Department of Electrical Engineering,Northern Border University,Arar 73222,Saudi Arabia;College of Science and Engineering,Hamad Bin Khalifa University,Doha 34110,Qatar

[1]Ruhul Amin Khalil;Nasir Saeed;Mohammad Inayatullah Babar;Tariqullah Jan;Sadia Din-.Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things)[J].自动化学报（英文版）,2022(03):496-509

BMDS

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