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Volume 9 Issue 3
Mar.  2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2022  >  9(3): 496-509
R. A. Khalil, N. Saeed, M. I. Babar, T. Jan, and S. Din, “Bayesian multidimensional scaling for location awareness in hybrid-internet of underwater things,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 496–509, Mar. 2022. doi: 10.1109/JAS.2021.1004356
Citation: R. A. Khalil, N. Saeed, M. I. Babar, T. Jan, and S. Din, “Bayesian multidimensional scaling for location awareness in hybrid-internet of underwater things,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 3, pp. 496–509, Mar. 2022. doi: 10.1109/JAS.2021.1004356
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Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

doi: 10.1109/JAS.2021.1004356
  • 1.

    Department of Electrical Engineering, Faculty of Electrical and Computer Engineering, University of Engineering and Technology, Peshawar 25120, Pakistan

  • 2.

    Department of Electrical Engineering, Northern Border University, Arar 73222, Saudi Arabia

  • 3.

    College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar

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    Abstract
  • 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 communication 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.

     

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    Highlights

    • 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
    • The proposed algorithm suggests that each sensor node attempts to search for the overall neighbourhood by utilizing any of the communication technology and estimate the range to the adjacent nodes
    • Some sensor nodes are not lying in the communication range of each other and can utilize the available connectiv- ity information and estimate the missing pairwise ranges
    • The information from each node is communicated to the surface buoy through SOA approach. The initial ranging of every node is carried out on SOA approach that utilizes the characteristic of any available underwater links such as acoustic, optical, MI or hybrid. This approach enhances the estimation of distance among available nodes. The surface buoy utilizes the information provided and calcu- lates the estimated distance matrix in a pairwise manner. It further applies the dimensionality reduction methodology based on subjective manifold interpretations to accurately locate each sensor node
    • Simulations are performed for sparse and dense IoUT networks to see the effectiveness of the proposed scheme. The results show the superior performance of the proposed method with respect to the literature in terms of different system parameters, such as ranging error variance, network density, and the total number of anchors. The simulation results depict that the proposed MIAO scheme achieves a sub-meter level of accuracy in even sparse IoUT networks

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