  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.
