The evolution of the Internet of Underwater Things has positioned Autonomous Underwater Vehicles as critical mobile edge nodes, yet the adverse underwater acoustic communication environment, characterized by high latency and low bandwidth, severely constrains the performance of collaborative sensing. To address these challenges, we propose the Kernelized Intrinsic McAulay-Seidman Bound as an online proxy for network Quality of Service. This metric overcomes the optimal test point selection difficulty and theO(L3)computational complexity inherent in the standard Intrinsic McAulay-Seidman Bound. The K-IMSB framework reformulates the discrete problem into a continuous functional optimization within a Reproducing Kernel Hilbert Space. By leveraging variational methods and a heat kernel onSO(3), we derive a closed-form expression that ultimately involves solving an ill-posed Fredholm integral equation of the first kind. To efficiently solve this, a Recursive Ridge Leverage Score Nystr¨om approximation algorithm is introduced, enabling lightweight, energy-efficient computation on the edge. This algorithm utilizes statistical leverage scores to adaptively identify critical manifold regions, thereby solving the dual challenges of operator discretization and numerical instability. Simulation results for an AUV attitude estimation scenario with Out-of-Sequence Measurements demonstrate that the K-IMSB provides a tight lower bound, and the RLS-Nyström method improves computational efficiency by approximately 21.17%, achieving real-time feasibility for IoUT edge deployment.

Optimizing sensor placement is crucial for enhancing the coverage and data-acquisition efficiency of ocean monitoring systems. Traditional approaches primarily rely on univariate ocean data for sensor placement, failing to capture the multidimensional coupling characteristics of the ocean environment, while the potential of multivariate datasets remains underexplored. To address this limitation, this work proposes an innovative Multivariate Fractal Autoencoder (MFAE) framework that leverages multivariate data to solve the sparse sensor placement problem. The MFAE optimizes sensor placement by dynamically updating multivariate feature weights and extracting latent spatial correlations. Furthermore, by optimizing feature weight initialization and enhancing autoencoder training protocols, we propose an Entropy-weighted Multivariate Fractal Autoencoder (EnMFAE) to establish an accurate nonlinear mapping between low-dimensional sampling spaces and full-state reconstructions. Validation experiments are conducted on temperature and salinity datasets from the North Pacific and Arctic Oceans, and the results demonstrate the superior performance of MFAE and EnMFAE relative to the POD, QR, and random placement baselines. With only 10 selected sensors, the MFAE achieves average reconstruction error reductions of 2.96% (for temperature) and 2.12% (for salinity) in the North Pacific, and 5.78% (for temperature) and 7.71% (for salinity) in the Arctic, respectively, significantly outperforming the compared random placement method with decoder-based reconstruction. MFAE offers a novel paradigm for optimizing sensor networks in complex ocean environments by leveraging multivariate data reconstruction.

Range-based localization technology achieves high accuracy under clear signal paths (Line-of-Sight, LOS). However, its performance deteriorates significantly due to errors in distance estimation when signals encounter obstructions, resulting in Non-Line-Of-Sight (NLOS) propagation. In light of these challenges, we investigate the combined effects of measurement noise and NLOS errors on target localization performance and propose a novel approach using Rotated Quadratic Cone Programming (RotQCP) for target localization in Wireless Sensor Networks (WSNs). By formulating the localization problem as a Maximum Likelihood (ML) estimation and employing relaxation techniques, we demonstrate that RotQCP can effectively address it even in the worst-case scenario. Compared to existing methods, the proposed approach eliminates the requirement for specific NLOS error statistics and delivers robust performance in sparsely and heavily congested NLOS environments. The simulation results demonstrate the efficacy of the proposed method in mitigating NLOS errors and attaining accurate localization. Moreover, the experimental outcomes based on open datasets substantiate the effectiveness of the proposed algorithm and indicate its superiority over existing algorithms. Notably, this research offers a robust and efficient solution for target localization in WSNs, particularly in a real harsh environment characterized by mixed LOS and NLOS propagation conditions.

Underwater Acoustic Internet of Things Networks (UAIoTNs) can furnish excellent technical support and information services for applications involving marine observation and detection, marine disaster prevention and mitigation, and maritime search and rescue, in which accurate positioning information is the fundamental requirement. The combination of high dynamics and complexity of the ocean environment to the high latency and narrowband of underwater acoustic communication are complex challenges in UAIoTNs. Due to these facts, this work investigates the received signal strength (RSS)-based three-dimensional (3D) target localization in UAIoTNs taking into account the absorption effect, uncertain transmission power (UTP), and a time-varying Path Loss Exponent (PLE). Through Taylor’s first-order expansion and certain approximations, we envision the underwater stratified acoustic propagation localization challenge as an Alternating Non-negative Constrained Least Squares (ANCLS) framework. To address the challenges posed by unknown multi-parameters, a robust coarse-to-fine localization algorithm (RCFLA) is proposed. At first, the coarse localization phase utilizes the Active Set Method (ASM), while the subsequent fine localization one employs the improved Broyden-Fletcher-Goldfarb-Sanno (BFGS) trust region method to enhance convergence towards the global optimal solution. The iterative process refines the underwater target location, UTP, and PLE, using the ASM-derived rough solution as the initial estimate. Analysis of computational complexity and derivation of the Cramér-Rao Lower Bound (CRLB) with stratified propagation and absorption effect demonstrates the superiority of RCFLA. Furthermore, Lyapunov’s second stability theorem is used to prove the stability of the RCFLA and presents a complete proof of global convergence. Numerical simulation and experimental results validate the algorithm’s optimal localization accuracy across various scenarios, showing reduced overhead compared to benchmark algorithms.

This article presents a robust 3-D Received Signal Strength Difference (RSSD) localization algorithm under mixed Gaussian noise in Underwater Wireless Sensor Networks (UWSNs) with Non-Line-Of-Sight (NLOS) paths. To mitigate the adverse effects, concurrent to absorption and path losses on accurate underwater localization, an Efficient RSSD-based Iterative Estimator (ERIE) in mixed Gaussian noise and NLOS environments is proposed. First, the corresponding non-convex problem in such environments is formulated, and the direct solution to this problem is not tractable unfortunately. Considering underwater acoustic signal attenuation, an RSSD-based min-max strategy is designed to transform it into a problem minimizing the worst-case loss, combined with the Huber cost function, constitutes a Huber function-based equivalent problem (H-ADMM) solved by Alternating Direction Method of Multipliers (ADMM). A compensation matrix is designed based on the H-ADMM solution to compensate for the bias introduced by the transformation, and the corresponding Cramér-Rao Lower Bound (CRLB) is derived to provide a performance benchmark. Numerical results indicate that the proposed approach achieves a higher localization accuracy than state-of-the-art methods.