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Localization of sensor nodes in the Internet of Underwater Things (IoUT) isof considerable significance due to its various applications, such asnavigation, data tagging, and detection of underwater objects. Therefore, inthis paper, we propose a hybrid Bayesian multidimensional scaling (BMDS) basedlocalization technique that can work on a fully hybrid IoUT network where thenodes can communicate using either optical, magnetic induction, and acoustictechnologies. These technologies are already used for communication in theunderwater environment; however, lacking localization solutions. Optical andmagnetic induction communication achieves higher data rates for shortcommunication. On the contrary, acoustic waves provide a low data rate forlong-range underwater communication. The proposed method collectively usesoptical, magnetic induction, and acoustic communication-based ranging toestimate the underwater sensor nodes' final locations. Moreover, we alsoanalyze the proposed scheme by deriving the hybrid Cramer Rao lower bound(HCRLB). Simulation results provide a complete comparative analysis of theproposed method with the literature.

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