Distributed Internet of Underwater Things (D-IoUT) has broad application prospects in marine monitoring, resource development, and security protection. However, the spatio-temporal uncertainty of underwater acoustic networks and the presence of multiple collision domains pose severe challenges to MAC-layer scheduling and interference management. To address these issues, this paper proposes an adaptive scheduling MAC protocol based on channel sensing and reinforcement decision-making (CSRD-ASMAC), aiming to provide an efficient and adaptive transmission scheduling scheme for D-IoUT. The protocol first introduces a hierarchical channel-state classification framework to accurately characterize the channel environment for each slot. Building on pre-divided basic slots, it integrates channel sensing and slot prediction into a reinforcement learning framework and employs a mixed-action exploration strategy to update Q-values, enabling each node to adaptively select the optimal transmission slot. Simulation results show that CSRD-ASMAC effectively improves overall network throughput in multi-collision domain environments.

Underwater acoustic sensor networks (UASNs) have attracted significant attention due to their potential applications in military surveillance and disaster early warning. However, due to the low data rate, high latency, and instability of underwater acoustic communication, UASNs are highly vulnerable to cascading failures triggered by the malfunction of key nodes, which can lead to network collapse. To address this challenge, we establish a cascading failure model tailored for dynamic traffic UASNs and propose a multi-criteria key node prediction (MC-KNP) algorithm based on the novel finding that nodes exhibit varying importance levels under different network traffic conditions. Although experimental results demonstrate that MC-KNP algorithm outperforms others approaches (e.g., degree, betweenness, and load) in accurately predicting key nodes for dynamic traffic UASNs, it suffers from high computational complexity. To address this limitation, we propose a key node prediction framework named KNP-GL, which integrates a graph convolutional network (GCN) to extract features that reflect both the structural roles of nodes and their potential impact on cascading failures, and a long short-term memory (LSTM) module to capture the temporal dynamics of cascading failures. Furthermore, based on the prediction results of KNP-GL framework, we design a mitigation strategy leveraging capacity expansion to improve network resilience against cascading failures. Experimental results show that KNP-GL framework achieves approximately 90% accuracy while reducing execution time from tens of seconds to tens of milliseconds. The proposed mitigation strategy further enhances network robustness, providing both theoretical insights and practical guidance for the development of high-reliability UASNs.

The development of the Internet of Underwater Things (IoUT) is of great significance to marine scientific research, deep-sea exploration and interdisciplinary data fusion. However, the existing medium access control (MAC) protocols usually face serious network congestion and unfair channel resource allocation challenges in large-scale IoUT, resulting in a decline in the overall performance of the system. To address these problems, this paper proposes a dynamic slot management MAC protocol based on POMDP (P-DSM-MAC). The protocol integrates network load estimation, ACB scheme optimization, dynamic slot allocation, and the sensing and multiplexing of idle/collision sub-slots through slot division to achieve efficient network resource management and sub-slot contention collision control. Simulation results show that P-DSM-MAC is significantly superior to existing protocols in key performance indicators such as network throughput, delay, and sub-slot contention collision rate, providing a feasible solution for the intelligent and dynamic optimization of IoUT in the future.

Traditional angle of arrival (AOA) localization in 3-D space typically requires sensors equipped with planar arrays, which incurs additional hardware costs. This limitation restricts its application in systems such as the Internet of Underwater Things (IoUT). Recent studies have shown that localization can also be achieved using sensor networks composed solely of linear arrays. However, most existing methods impose strict constraints on the orientation or placement of sensor arrays. Although some studies have proposed localization solutions free from these two limitations, such methods still exhibit two notable shortcomings: 1) high computational complexity that scales with network size, making them unsuitable for resource-constrained scenarios or large-scale sensor network deployments and 2) poor robustness to sensor position errors, where nodal deviations can significantly degrade localization accuracy. To address these challenges, this study proposes a novel computationally efficient 3-D source localization method based on 1-D AOA measurements from multiple linear arrays. Significantly, we innovatively incorporate a weighted least squares (WLSs) compensation model that effectively enhances the method’s robustness against sensor position errors. Experimental results demonstrate that: 1) while achieving the theoretical optimum localization accuracy as defined by the Cramér-Rao lower bound (CRLB), the proposed method shows significantly lower computational complexity than existing methods, with complexity independent of sensor network scale and 2) in practical scenarios with node position errors, our method outperforms other state-of-the-art methods in localization accuracy.

This paper aims to address the issue of low accuracy in underwater Direction of Arrival (DOA) estimation using Deep Learning (DL) methods, which arises due to the scarcity of underwater data caused by the difficulties in conducting underwater experiments. For multi-snapshot sampled signals, we segment the snapshots and reconstruct the task into a problem of processing few-snapshot data within an expanded dataset. By utilizing the new task, we employ a deep mutual learning (DML) model to enhance the accuracy of the original task's DOA estimates. Experimental results demonstrate that under conditions of small and limited datasets, our approach effectively improves the accuracy of DL-based DOA estimation methods.

Detecting weak nonlinear time series is critical in various applications, such as ocean monitoring, port security, coastal operations, and offshore activities. However, traditional methods for detecting such signals often require informative priors, leading to inefficiencies. This study proposes a novel approach that transforms nonlinear time series detection into network characterization through a weighted undirected similarity network construction method. The method integrates symmetric Kullback-Leibler divergence and complex network theory, transforming the node similarity measurement problem into a geometric problem on matrix manifolds. This method constructs a network representation of the time series data by measuring the similarity between data at different time scales. To demonstrate the effectiveness of our proposed approach, we conducted simulations and applied it to actual recorded data collected in the South China Sea. The synthetic data study showed that our method has a significant advantage in detecting weak nonlinear time series from ambient noise. Additionally, our approach successfully distinguished ship signals from marine ambient noise by comparing the network spectral values.

Intelligent transportation system significantly relies on accurate positioning information of land vehicles for both safety and non-safety related applications, such as hard-braking ahead warning and red-light violation warning. However, existing Global Navigation Satellite System (GNSS) based solutions suffer from positioning performance degradation in challenging environments, such as urban canyons and tunnels. In this paper, we focus on the positioning performance enhancement of land vehicles via cooperative positioning under a partial GNSS environment in a Vehicular Ad-hoc NETwork (VANET). The availability of Time-of-Flight (ToF) based inter-vehicle or vehicle-to-infrastructure ranges is verified via 5.9 GHz Dedicated Short-Range Communication (DSRC) vehicle-to-everything communication with RTS/CTS unicast mechanism. An inertial navigation sensor aided, tightly coupled integration approach for land vehicle cooperative positioning using DSRC ToF ranges and carrier frequency offset range-rates is proposed, where a digital map is used to constrain the position estimates. If available, the GNSS pseudorange and Doppler shift under partial GNSS environment can also be incorporated. A Rao–Blackwellized particle filter is utilized to estimate the unknown variables allowing for reduced computational complexity in comparison with the conventional particle filter. The posterior Cramer–Rao lower bound is also derived to give a theoretical performance guideline. Both simulation and experimental results show the validity of our proposed approach.

Accurate location information is critical for many engineering applications (e.g., radar, sonar, autonomous robots, intelligent transportation systems). In traditional source localization algorithms, the perfect knowledge of noisy Time-of-Arrival (ToA) measurements are assumed to be obtained by the fusion center in a sensor network. This assumption is not practical for wireless sensor networks, especially for a resource-limited sensor network with stringent power and communication bandwidth constraints. In this paper, we propose a novel channel-aware source localization method based on quantized asynchronous ToA measurements, where the quantization errors as well as the imperfect communication link between each sensor and the fusion center are considered. The maximum-likelihood (ML) source localization by jointly estimating the signal transmission instant and source location is formulated. An efficient relaxation is provided to transform the non-convex ML optimization problem into a convex problem. The Cramér-Rao lower bounds (CRLBs) for the quantized ToA measurements with the uncertainty of data exchange are derived. Furthermore, a Fisher information based heuristic quantization scheme is proposed to design quantized thresholds for asynchronous ToA measurements. The simulation and experimental results demonstrate that our proposed method can yield an efficient estimate under different scenarios.

Vehicle self-positioning is of significant importance for intelligent transportation applications. However, accurate positioning (e.g., with lane-level accuracy) is very difficult to obtain due to the lack of measurements with high confidence, especially in an environment without full access to a global navigation satellite system (GNSS). In this paper, a novel information fusion algorithm based on a particle filter is proposed to achieve lane-level tracking accuracy under a GNSS-denied environment. We consider the use of both coarse-scale and fine-scale signal measurements for positioning. Time-of-arrival measurements using the radio frequency signals from known transmitters or roadside units, and acceleration or gyroscope measurements from an inertial measurement unit (IMU) allow us to form a coarse estimate of the vehicle position using an extended Kalman filter. Subsequently, fine-scale measurements, including lane-change detection, radar ranging from the known obstacles (e.g., guardrails), and information from a high-resolution digital map, are incorporated to refine the position estimates. A probabilistic model is introduced to characterize the lane changing behaviors, and a multi-hypothesis model is formulated for the radar range measurements to robustly weigh the particles and refine the tracking results. Moreover, a decision fusion mechanism is proposed to achieve a higher reliability in the lane-change detection as compared to each individual detector using IMU and visual (if available) information. The posterior Cramér-Rao lower bound is also derived to provide a theoretical performance guideline. The performance of the proposed tracking framework is verified by simulations and real measured IMU data in a four-lane highway.