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.

This paper addresses the target localization problem using time-of-arrival (TOA)-based technique under the non-line-of-sight (NLOS) environment. To alleviate the adverse effect of the NLOS error on localization, a total least square framework integrated with a regularization term (RTLS) is utilized, and with which the localization problem can get rid of the ill-posed issue. However, it is challenging to figure out the exact solution for the considered localization problem. In this case, we convert the RTLS problem into a semidefinite program (SDP), and then obtain the solution of the original problem by solving a generalized trust region subproblem (GTRS). The proposed method has a relatively good robustness in localization even under the circumstance that the prior knowledge of the NLOS links or its distribution does not know. The outperformance of the proposed method is demonstrated in the simulations compared with other state-of-the-art techniques.