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.

Autonomous aerial vehicles (AAVs) are widely used for ship tracking and detection tasks. However, the real-time detection performance is limited by AAV battery capacity and computing power, resulting in a short operational duration. To address this challenge, this paper proposes a AAV ship detection system that focuses on two key aspects: algorithm improvement and computational resource allocation. Specifically, we introduce a lightweight ship detection method tailored for multi-AAV scenarios in a mobile edge computing environment. The proposed method first designs a multi-disentangled knowledge distillation approach based on an information decoupling framework and utilizes a newly designed teacher network to enhance the lightweight detection model. The teacher network disentangles two key types of entanglements: the relationship between the convolutional filters and target categories, and the relationship between the foreground and background regions in the feature maps. Additionally, a proximal policy optimization (PPO) reinforcement learning algorithm is designed to enable real-time decision-making for AAV motion, detection accuracy, and computational offloading. Finally, we validate the superiority of the proposed knowledge distillation method and demonstrate the robustness and effectiveness of the AAV path planning algorithm in various scenarios through a series of experiments. Compared to the improved student models YOLOv8-N and YOLOv10-N, our method improves [email protected] by 1.2% and 1.1% on the SeaShips7000 and FVessel validation sets. Furthermore, compared to the existing methods K-Means and DBSCAN, our approach achieves reward values approximately 2.0 times and 1.4 times higher, respectively.

Underwater acoustic Internet of Things Networks (UAIoTNs) play a crucial role in oceanographic and environmental monitoring, necessitating precise localization for optimal functionality. However, the underwater setting introduces significant challenges, encompassing the stratification effect arising from underwater heterogeneity, uncertainty in anchor positions due to currents, and variations in the signal transmission environment. These factors collectively impede the accurate estimation of location. Consequently, this paper addresses these challenges by analyzing and deriving a closed-form solution using a time-of-arrival (TOA)-based technique for 3D localization in UAIoTNs. The investigation establishes an underwater stratified propagation model, drawing inspiration from ray tracing theory and Snell’s law. Employing the Cramér-Rao lower bound (CRLB) framework, we explore scenarios both with and without considering perturbed anchors, utilizing the Banachiewicz-Schur theorem. To quantify the impact of the stratification effect and perturbed anchors on CRLB and mean square error (MSE), we further analyze and derive an MSE expression, employing Taylor-series linearization. Building on our analysis of the detrimental effects of stratification and inaccurate anchors, we introduce a multiple-weighted least squares (MWLS) algorithm to alleviate potential performance losses. This approach integrates a matrix operator in the update step, eliminating variable dependencies and resulting in a closed-form solution that circumvents the need for iterative processes. Our simulation results validate our analytical findings and demonstrate the effectiveness of the proposed method, showcasing improved localization accuracy across various scenarios when compared to state-of-the-art approaches.

Sparse sensor placement strategies are applied to reconstruct a region’s full-state data conditioned to a limited number of sensors; particularly, crucial to ocean monitoring systems. In maritime systems, existing sparse sensor placement methods mainly consider the reconstruction error of data or rely on specific requirements. Considering how sensors acquire essential information for monitoring systems, the utilization of entropy from information theory becomes quite interesting. In this article, we show that entropy measurements on different quantities of information are sensitive to indicate the border areas, thus requiring a balance between the number of sensors needed and the amount of information collected by them in coastal areas. Due to such, we propose: 1) a novel sparse sensor placement strategy based on entropy, where the entropy measurements in temporal dimension are utilized for sample selection, so portions of samples selected are utilized for training data, significantly improving the training efficiency without sacrificing accuracy of subsequent data reconstruction. In the proposed strategy, 2) we use orthogonal triangle decomposition from linear algebra where a low-cost sensor is employed as pivot and in terms of spatial dimension, the entropy of each location is adopted as entropy weight to reconstruct full state data. Additionally, 3) the strategy employs a greedy algorithm of weighted column pivoting for the orthogonal triangle decomposition, which is designed to suit yet effectively seek additional information and minimal reconstruction error in each iteration processing step. Experimental results using sea surface temperature (SST) data show that the proposed strategy outperforms existing methods, acquiring more information, ensuring higher efficiency, and reducing costs while minimizing reconstruction errors.

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.

Big Data platforms recently employ resource management systems, such as YARN, Mesos, and Google Borg, to provision computational resources. These systems adopt containerization to share the computing resources in a multi-tenant setting with low performance overhead and interference. However, it may be observed that tenants often interfere with each other on the underlying Big Data File Systems (BDFS), e.g., Hadoop File System, which have been widely deployed as a persistent layer in current data centers. A solution with systematic generality is to containerize BDFS itself to isolate and allocate its IO sources to multiple tenants. To this end, we conduct analysis on the ineffectiveness of proportionally sharing BDFS IO resource via containerization. This ineffectiveness is due to the scheduler of containerization in “pseudo-starvation” status, in which most of IO requests are backlogged in BDFS rather than in containerization scheduler. Without enough backlogged IO requests, existing schedulers might have to maximize device utilization rather than enforce proportional sharing policy. To resolve this ineffectiveness issue, we develop a cross-layer system calledBDFS-Container, which containerizes BDFS at the Linux block IO level. Central to BDFS-Container, we propose and design a proactive IOPS throttling-based mechanism namedIOPS Regulator, which achieves a trade-off between maximizing IO utilization and accurately proportional IO sharing. The evaluation results show that our method can improve proportionally sharing BDFS IO resources by 74.4 percent on average.

Execution of analytic frameworks on sample data sets is the current trend in response to increasing data size and demand for real-time analysis. Additionally, high-performance, energy-efficient Solid-State Drive (SSD) arrays are the primary storage subsystem for parallel data analysis systems. To exploit the benefits of SSD arrays when executing sample data set analytics, several key areas must be considered. First, due to logical to physical address translation, random data choice in data sampling jobs can cause unbalanced workloads among SSDs in the array. Second, after the data choice, existing task schedulers in data analysis frameworks can introduce non-negligible resource contentions resulting from the suboptimal Input/Output (I/O). The performance of SSDs is unpredictable because of their varying maintenance costs at runtime, which renders them hard to be managed by the scheduler. With the trend towards sample set data analytics and the use of SSDs, it is increasingly important to ensure balanced workloads and minimize resource contention. Without addressing these areas, sample-set data analytics on SSDs will continue to suffer from performance inefficiencies. In this paper, we propose ApproxSSD to perform on-disk layout-aware data sampling on SSD arrays. This proposed framework leverages data selection and task scheduling to improve the performance of many applications. ApproxSSD decouples I/O from the computation in task execution. This avoids potential I/O contentions and suboptimal workload balances. We have developed an open-source prototype system of ApproxSSD in Scala at Github. Our evaluation shows that ApproxSSD can achieve up to 2.7 times speed up at 10 percent sampling ratio under an example sampling workload when compared to Spark, while simultaneously maintaining high output accuracy.

Single image rain streaks removal is extremely important since rainy condition adversely affects many computer vision systems. Deep learning based methods have great success in image deraining tasks. In this paper, we propose a novel residual-guide feature fusion network, called ResGuideNet, for single image deraining that progressively predicts high-quality reconstruction while using fewer parameters than previous methods. Specifically, we propose a cascaded network and adopt residuals from shallower blocks to guide deeper blocks. We can obtain a coarse-to-fine estimation of negative residual as the blocks go deeper with this strategy. The outputs of different blocks are merged into the final reconstruction. We adopt recursive convolution to build each block and apply supervision to intermediate de-rained results. ResGuideNet is detachable to meet different rainy conditions. For images with light rain streaks and limited computational resource at test time, we can obtain a decent performance even with several building blocks. Experiments validate that ResGuideNet can benefit other low- and high-level vision tasks.

In this paper, we study the problem of sub-dataset analysis over distributed file systems, e.g., the Hadoop file system. Our experiments show that the sub-datasets distribution over HDFS blocks, which is hidden by HDFS, can often cause corresponding analyses to suffer from a seriously imbalanced or inefficient parallel execution. Specifically, the content clustering of sub-datasets results in some computational nodes carrying out much more workload than others; furthermore, it leads to inefficient sampling of sub-datasets, as analysis programs will often read large amounts of irrelevant data. We conduct a comprehensive analysis on how imbalanced computing patterns and inefficient sampling occur. We then propose a storage distribution aware method to optimize sub-dataset analysis over distributed storage systems referred to as DataNet. First, we propose an efficient algorithm to obtain the meta-data of sub-dataset distributions. Second, we design an elastic storage structure called ElasticMap based on the HashMap and BloomFilter techniques to store the meta-data. Third, we employ distribution-aware algorithms for sub-dataset applications to achieve balanced and efficient parallel execution. Our proposed method can benefit different sub-dataset analyses with various computational requirements. Experiments are conducted on PRObEs Marmot 128-node cluster testbed and the results show the performance benefits of DataNet.

The Peer-to-Peer (P2P) applications have become quite popular in the terrestrial domain mainly due to its strong capability of sharing distributed files. However, in the marine environment, where the ship is almost isolated from the world, the P2P file sharing system could be more demanding, but not available so far. By virtues of the rapid development of the wireless telecommunications, especially the INMARSAT Fleet satellite communications, along with the provided Mobile Packet Data Service (MPDS), we propose a Mobile P2P (MP2P) architecture, which could be further utilized to implement a MP2P file-sharing system at sea. To achieve this goal, in this paper, we firstly proposed an architecture concept for Next Generation Maritime Communication Network (NGMCN). Then, on this basis, we design our layered MP2P file-sharing architecture. Finally, we use simulations to evaluate the performance of the proposed architecture and applicability to the marine environment.

Recently, wireless sensor and actor networks (WSANs) have evoked attention because of its extensive application foreground. WSANs include resource-unconstrained nodes, actors, to make decisions and deal with gusty events. As we all know, one of the most important objectives of designing communication schemes for wireless sensor networks (WSNs) is to decrease energy consumption of sensors and prolong networks lifetime. An introduction of powerful actors completely changes the network structure and new protocols at all layers are demanded to adapt to the characteristics of WSANs. In this paper, we will analyze the routing problem of WSANs and explore the routing differences between WSNs and WSANs. Then we will present our novel energy-efficient routing scheme for WSANs which makes use of actors. At last, the scheme will be proved to be feasible and adaptive to WSANs through simulation experiments