ISAC-Enabled Underwater IoT Network Localization: Overcoming Asynchrony, Mobility, and Stratification Issues

In oceanographic and environmental monitoring, achieving precise localization and sensing through Integrated Sensing and Communication (ISAC) within the Internet of Underwater Things (IoUT) networks is paramount. However, ISAC-based IoUT systems present distinctive challenges, including depth-dependent propagation speed, asynchronous clock synchronization, and node mobility. This paper introduces an efficient asynchronous localization method explicitly tailored for ISAC-based IoUT networks, which effectively addresses both the stratification effect and node mobility. Our approach centers on an iterative least squares (LS) algorithm designed to localize Autonomous Underwater Vehicles (AUVs) while carefully considering propagation delay and location estimation. Furthermore, we introduce a mobility model grounded in target sensing mechanisms that rely on AUVs' spatial coordinates and propulsion velocities, thereby enhancing the accuracy of target position estimation. We propose a novel precoding design for sensing using random acoustic signals within IoUT networks. To validate the effectiveness of our method, we conduct comprehensive Monte Carlo simulations and benchmark the results against state-of-the-art techniques. The findings demonstrate a significant reduction in estimation errors, confirming the superior efficiency of our approach compared to existing methods.