Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

TL;DR

This paper introduces a hybrid Bayesian multidimensional scaling approach for localizing sensor nodes in underwater environments, integrating optical, magnetic, and acoustic communication methods to improve accuracy and applicability.

Contribution

It presents a novel localization technique that combines multiple underwater communication technologies within a Bayesian framework, addressing the lack of existing solutions for hybrid IoUT networks.

Findings

The proposed method outperforms existing localization techniques in simulations.

Derivation of the hybrid Cramer Rao lower bound (HCRLB) for the scheme.

Simulation results demonstrate improved accuracy and robustness.

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 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 (HCRLB). Simulation results provide a complete comparative analysis of the proposed method with the literature.