Affine frequency division multiplexing (AFDM) has been considered as a promising waveform to enable high-reliable connectivity in the internet of vehicles. However, accurate channel estimation is critical and challenging to achieve the expected performance of the AFDM systems in doubly-dispersive channels. In this paper, we propose a sparse Bayesian learning (SBL) framework for AFDM systems and develop a dynamic grid update strategy with two off-grid channel estimation methods, i.e., grid-refinement SBL (GR-SBL) and grid-evolution SBL (GE-SBL) estimators. Specifically, the GR-SBL employs a localized grid refinement method and dynamically updates grid for a high-precision estimation. The GE-SBL estimator approximates the off-grid components via first-order linear approximation and enables gradual grid evolution for estimation accuracy enhancement. Furthermore, we develop a distributed computing scheme to decompose the large-dimensional channel estimation model into multiple manageable small-dimensional sub-models for complexity reduction of GR-SBL and GE-SBL, denoted as distributed GR-SBL (D-GR-SBL) and distributed GE-SBL (D-GE-SBL) estimators, which also support parallel processing to reduce the computational latency. Finally, simulation results demonstrate that the proposed channel estimators outperform existing competitive schemes. The GR-SBL estimator achieves high-precision estimation with fine step sizes at the cost of high complexity, while the GE-SBL estimator provides a better trade-off between performance and complexity. The proposed D-GR-SBL and D-GE-SBL estimators effectively reduce complexity and maintain comparable performance to GR-SBL and GE-SBL estimators, respectively.

Geo-Opportunistic Routing Networks (Geo-OR) within the Internet of Underwater Things (IoUT) are crucial for addressing the unique challenges of underwater communication environments. By leveraging geographic location data, these networks can opportunistically select forwarding nodes, a strategy that proves particularly effective in the dynamic and unpredictable nature of IoUT. However, uncertainty in forwarder selection and the inability to verify nodes’ claimed positions remain critical challenges. This paper investigates how uncertainty in next-hop selection arises in Geo-OR when viewed from the perspective of potential adversaries operating in a non-cooperative manner. By integrating an octree-based search strategy with an angle-constrained 3D localization algorithm, we accurately identify and locate “hot” nodes within Geo-OR. Geometric methods are employed to analyze vulnerable relay regions, pinpointing areas especially susceptible to malicious attacks. Based on this analysis, a location spoofing attack is proposed to disrupt legitimate node transmissions and degrade network performance. Simulation results reveal that the inherent uncertainties in Geo-OR expose the network to significant vulnerabilities, severely compromising throughput. This study highlights the critical weaknesses of Geo-OR, offering valuable insights into the most vulnerable regions and contributing to the design of targeted defense strategies to mitigate these risks.

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.

In the complex, time-varying Internet of Underwater Things (IoUT), traditional hierarchical routing struggles to balance reliability, low delay, and energy efficiency due to measurement delays, routing voids and frequent transmission collisions. To address these challenges, we proposes HRT-TCOR, a high-reliability, time-reversal-based cross-layer opportunistic routing protocol that tightly integrates time reversal, reservation-based MAC, and opportunistic forwarding to collaboratively optimize the physical, data link, and network layers. First, lightweight beacons enable void sensing and preselection of candidate-forwarding sets (CFS). Next, a hybrid sender-receiver cooperation strategy forms the CFS, while a two-tier suppression mechanism eliminates redundant transmissions. Finally, pipelined handshakes merge reception acknowledgments with channel reservations to eliminate inter-hop idle gaps and ensure seamless end-to-end reliable delivery. Simulations show that, under both good and poor channel conditions, HRT-TCOR consistently achieves the highest packet delivery ratio, a lower delay, and competitive energy efficiency compared to other protocols.

Underwater Acoustic Networks (UANs) play a critical role in marine monitoring and exploration. The integration of mobile nodes can extend the monitoring range and enhance network flexibility and real-time performance. UANs that combine both fixed and mobile nodes typically employ geographic routing protocols and dynamic communication techniques. However, in geographic routing protocols, the uncertainty of the next-hop node leads to several challenges, including access conflicts in the underwater acoustic medium, data collisions, and resource allocation difficulties. Non-Orthogonal Multiple Access (NOMA) technology allows for more users to access the network under limited frequency spectrum, thereby improving network performance. However, due to the complexity of underwater acoustic channels, existing NOMA solutions are difficult to be directly applied in marine environments. Moreover, many existing NOMA techniques rely on centralized control and require real-time global network information, which is challenging to be implemented in UANs. Currently, research on physical-layer and network-layer integrated Medium Access Control (MAC) protocols to address resource allocation and data collision issues remains limited. To address these challenges, this paper proposes the GNC-MAC protocol, compatible with geographic routing protocols and NOMA access techniques. First, we propose a dynamic programming-based grouping algorithm to address the user grouping problem in the uplink of underwater NOMA. Next, to tackle the resource allocation issue, we transform it into a problem of maximizing the sum of the system data transmission rates, and design a distributed dynamic power selection algorithm considering node constraints to improve system throughput. Finally, considering six dynamic factors, including channel quality, energy status, link stability, queue load, node mobility, and interruption probability, we design an adaptive backoff algorithm in the GNC-MAC protocol to optimize next-hop selection in geographic routing protocols and reduce packet collisions. The simulation results indicate that the overall performance of GNC-MAC outperforms existing advanced protocols and offers higher reliability.

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 recently proposed affine frequency division multiplexing (AFDM) modulation has been considered as a promising technology for narrowband doubly-dispersive channels. However, the time-scaling effects, i.e., pulse widening and pulse shortening phenomena, in extreme wideband doubly-dispersive channels have not been considered in the literatures. In this paper, we investigate such wideband transmission and develop an efficient transmission structure with chirp-periodic prefix (CPP) and chirp-periodic suffix (CPS) for AFDM system. We derive the input-output relationship of AFDM system under time-scaled wideband doubly-dispersive channels and demonstrate the sparsity in discrete affine Fourier (DAF) domain equivalent channels. We further optimize the AFDM chirp parameters to accommodate the time-scaling characteristics in wideband doubly-dispersive channels and verify the superiority of the derived chirp parameters by pairwise error probability (PEP) analysis. We also develop an efficient cross domain distributed orthogonal approximate message passing (CD-D-OAMP) algorithm for AFDM symbol detection and analyze its corresponding state evolution. By analyzing the detection complexity of CD-D-OAMP detector and evaluating the error performance of AFDM systems based on simulations, we demonstrate that the AFDM system with our optimized chirp parameters outperforms the existing competitive modulation schemes in time-scaled wideband doubly-dispersive channels. Moreover, our proposed CD-D-OAMP detector can achieve the desirable trade-off between the complexity and performance, while supporting parallel computing to significantly reduce the computational latency.

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.

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.

In underwater acoustic sensor networks (UANs), reliable packet delivery is critical in data collection and monitoring of the oceans. It primarily relies on the design of routing protocols to guarantee network durability and connectivity. However, utilizing routing design to achieve enhanced network longevity and reliable packet forwarding is challenging due to the complex underwater environment, unstable link connectivity, high transmission power, and high propagation latency. Thus, we propose a novel routing strategy called the longevity-oriented and reliable forwarding percolation (LRP) routing protocol. The goal of LRP is to ensure reliability by exploring multipath percolation-based routing and extend network longevity by energy control and residual energy optimization. Network reliability can be estimated using a built-in calculation model, and the source node controls energy and records the residual energy to extend the network lifetime. Utilizing an optimization of the network reliability requirement and residual energy, we develop a routing strategy to select the activated link set and node set for each hop in an energy-saving and reliable way. Moreover, a recursive approach is used to avoid the occurrence of void regions. Simulation results exhibit the effectiveness of the power control and routing strategy and demonstrate its superiority over the benchmarks in terms of network longevity and reliability.

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.

Underwater acoustic sensor networks (UANs) have been shown as a promising technology to monitor and explore the oceans. Nevertheless, the routing design for data gathering of UANs considering the acoustic channel communication characteristics and limited energy is a pressing, open issue. To address this challenge, we propose the long-lifetime and reliable percolation routing protocol (LRP) for UANs to ensure the reliability of the network and prolong the network lifetime. The proposed protocol adaptively selects the forwarders to deliver each message. By estimating the reliability of the next-hop and considering the remaining energy of candidates, the proposed protocol takes a recursive approach to avoid trapping in a locally optimal solution. Simulations results validate the feasibility of the proposed protocol and demonstrate its superiority over the existing routing algorithms by prolonging the lifetime of LRP by up to 35%.

Despite the remarkable progress of deep learning on speech recognition and music processing, it is still challenging to classify general audio signals due to the high cost of collection and annotation of the samples. The ability to learn discriminative features from a small dataset makes deep metric learning a promising method for general audio classification. However, because of the difficulty in mining informative sample pairs, it usually suffers from slow convergence or even poor local minima. In this letter, to improve classification performance by exploiting the advantages of both the weight-based loss and the metric-based loss, we proposed a multi-positive metric loss and a framework to joint it with the common softmax loss. The proposed method eliminates the need for sub-loss weighting by measuring the similarity between samples in a consistent probabilistic form. It also enhances the classification performance by improving the estimation of the intra-class and inter-class relationships from multiple positive samples. Finally, we evaluated the proposed method on the ShipsEar dataset and the Ocean Networks Canada dataset, and the results verified its effectiveness.

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.

Accurate localization of an autonomous underwater vehicle (AUV) is essential in many applications. The motion of an AUV during the measurement acquisition period can be significant and the localization performance can suffer considerably if it is neglected. A new time delay model that accounts for the motion is proposed for moving AUV localization. The non-recursive form of the proposed model is next derived. An algebraic explicit positioning solution based on the non-recursive model is developed when the measurement noise and transponder location errors are present. Simulation results illustrate the importance of accounting for AUV motion in localization, and validate the theoretical analysis that the proposed solution can reach the Cramér-Rao lower bound (CRLB) accuracy over the small error region under Gaussian noise.

Bursty data links can benefit directly from the removal of pilot symbol transmission for channel estimation by improving the spectral efficiency. For such networking scenarios including data or paging signals, blind equalization for joint data detection and channel estimation with few or no pilot can improve spectrum efficiency. Though some existing works typically have attempted to take advantage of the sparsity of multipath channels, substantial performance improvement remains elusive. In this work, we develop an iterative Markov chain Monte Carlo algorithm based on Gibbs sampling designed for sparse channels. We incorporate the channel sparsity in the form of an l1type prior probability distribution, and derive the posterior channel distribution via stochastic sampling. Furthermore, we propose transmitter and receiver structures that could resolve unknown phase ambiguity in frequency-selective channels. This algorithm is also generalizable to non-sparse channels.

This paper proposes a low-complexity solution for the data protection problem without newcomer nodes in Internet of Things (IoT) scenarios, i.e., when device losses cannot be replaced by new devices. Application scenarios include environmental monitoring, data collection, and industrial automation. Although the optimal solution and optimization framework have been studied in previous work to minimize the network costs and storage capacity requirements, this paper shows that the optimal solution has a high complexity as the number of devices increases. Given the massive number of IoT devices, we propose a relaxation to the cut capacity constraints that (a) guarantees data recoverability, (b) achieves the minimum network use, and (c) reduces the problem's complexity dramatically. Our numerical results show that the proposed relaxation allows us to change the computational scaling of the problem. More specifically, we show that the time taken to compute the optimal transmission policy with the relaxation for a system with 800 devices is the same as the time it takes the optimal solution to solve the case of 15 devices.

This study investigates the blind data detection in two‐way relay networks (TWRN) that employ amplify‐and‐forward (AF) relay strategy. To blindly detect the data in TWRN in the presence of uncertain time‐frequency offsets and phase noise, the authors develop a new Bayesian‐based approximate Gibbs algorithm based on truncated Taylor series expansion approximation. In addition, the authors exploit available constraint information on parameters of interest. The authors present three receivers based on three different parameter estimation approaches. The authors further discuss the implementation issue and present diagnostic convergence analysis. The authors’ numerical results demonstrate the performance and efficacy of their proposed algorithm.