Compound faults, which arise from the interaction of multiple simultaneous failures, pose significant risks to the maintenance of industrial machinery in the Industrial Internet of Things (IIoT), leading to complex and unpredictable system failures. Traditional models tend to misclassify emerging compound faults as known categories due to a bias toward seen data. Additionally, the delayed emergence of compound faults relative to single faults hampers prompt sample collection. Furthermore, compound fault datasets typically exhibit a long-tailed distribution. Driven by these challenges, we propose an incremental contrastive learning model based on cross-modal contrastive embedding (ICLCFD) to achieve an incremental fault diagnosis from single faults to compound faults.Firstly, we employ Symmetrized Dot Pattern (SDP) image transformation to convert vibration signals into visual representations. We extract high-dimensional visual features from these SDP images using a Multi-Scale Residual Convolutional Neural Network (MS-ResCNN) and generate low-dimensional semantic features based on vibration signals to obtain richer feature information. Subsequently, a novelty detection mechanism is developed using the predicted number of fault sources as a count-based indicator to identify newly emerging faults. Furthermore, we incorporate a difficulty-aware class rebalancing sampling strategy to prioritize hard-to-diagnose samples during incremental updates, mitigating the excessive impact of head-class samples on the diagnosis results. Experiments on three open-source bearing datasets (CWRU, PU, and XJTU-SY) demonstrate that ICLCFD achieves a state-of-the-art accuracy of 96.58 ± 0.28%, outperforming existing methods by approximately 2%. The results confirm its effectiveness in handling unknown and compound faults in dynamic IoT maintenance pipelines.

Underwater acoustic backscatter communication technology brings a new perspective on addressing the energy dilemmas of Internet of Underwater Things (IoUT). However, time asynchronism in underwater acoustic backscatter devices (UABDs) can significantly degrade the performance of the UABD-based IoUT system. Existing time synchronization algorithms lose practicability leading to high energy consumption in scenarios where charging delays vary. To address these challenges, we propose UAB-Sync, a time synchronization algorithm specifically designed for the system. UAB-Sync introduces a novel three-stage architecture that uses dual constraints of time and energy to dynamically optimize the duration of energy signal, achieving adaptive approximation of optimal results. Besides, a closed-form solution for clock parameter estimation that incorporates Doppler factor estimation and accounts for multi-source measurement errors is developed, ensuring effective synchronous correction. Simulation results demonstrate that UAB-Sync significantly outperforms existing synchronization schemes in terms of both accuracy and energy efficiency for the UABD-based IoUT system.

Unmanned aerial vehicles (UAVs) have emerged as effective platforms for mobile edge computing (MEC), offering flexible and efficient computational support to ground users (GUs). Many practical applications, such as deep neural network inference tasks, generate subtasks with complex dependencies, significantly complicating scheduling and offloading decisions. In this paper, we study the joint optimization of UAV deployment, UAV-GU associations, and dependent task offloading decisions within a multi-UAV-enabled MECsystem, aiming to minimize the end time of the overall tasks. The tasks generated by GUs are modeled using directed acyclic graphs (DAGs), explicitly capturing subtask dependencies and execution orders. To address the resulting complex optimization problem, we first propose a Joint Successive convex approximation and Penalty dual decomposition-based Optimization (JSPO) algorithm to determine the initial UAV deployment and UAV-GU associations. Next, we formulate the dependent task offloading decision process as a Markov decision process (MDP), which is solved by employing deep reinforcement learning (DRL). To effectively exploit the structural information within DAG tasks, we integrate a graph attention network (GAT) to provide enhanced state representations for DRL. JSPO and the DRL framework were executed in turns to gradually improve the performance. Extensive simulation results verify that our proposed framework significantly reduces the end time compared to existing methods, demonstrating its superiority in multi-UAV MEC systems.

Autonomous Underwater Vehicles (AUVs) provide a new prospect for data collection in underwater wireless sensor networks (UWSNs). For dynamic underwater environments, researchers typically apply deep reinforcement learning (DRL) to design multi-AUV collection schemes for UWSNs. However, these methods suffer from the following issues. 1)Overloaded observations. The importance of various observations for an AUV varies over time. Considering all observations equally complicates decision-making for subsequent actions. 2)Dynamic scale of AUVs and sensors. Once the number of AUVs or sensors is changed, traditional static neural networks require retraining, lacking scalability across diverse scenarios. To solve the above issues, we propose a Generalizable Attention-based Data collection scheme (GAMD) for Multi-AUV UWSNs, while enhancing AUVs’ collection efficiency. GAMD incorporates the attention mechanism with multi-agent DRL framework, which enables AUVs to prioritize observations more critical for action decisions. Moreover, we propose an adaptive information processing approach, enabling the AUV policy model to seamlessly adapt to various scenarios without retraining. Additionally, we develop a training paradigm with incremental complexity across different scale scenarios to simplify training process and accelerate convergence. Simulation results demonstrate that GAMD alleviates the training cost compared to the state-of-the-art methods, and simultaneously optimizes collection energy efficiency, collection time, and trajectory distance.

Simulators are essential to troubleshoot and optimize Underwater Acoustic Network (UAN) schemes (network protocols and communication technologies) before real field experiments. However, due to programming differences between the above two contents, most existing simulators concentrate on one while weakening the other, leading to non-generic simulations and biased performance results. Moreover, novel UAN schemes increasingly integrate Artificial Intelligence (AI) techniques, yet existing simulators lack support for necessary AI frameworks, failing to train and evaluate these intelligent methods. On the other hand, these novel schemes consider more UAN characteristics involving more complex parameter configurations, which also challenge simulators in flexibility and fineness. To keep abreast of advances in UANs, we propose the Fourth Generation (FG) network simulator-3 (ns-3)-based simulator Aqua-Sim FG, enhancing the general and intelligent simulation ability. On the basis of retaining previous generations’ functions, we design a new general architecture, which is compatible with various programming languages, including MATLAB, C++, and Python. In this way, Aqua-Sim FG provides a general environment to simulate communication technologies, network protocols, and AI models simultaneously. In addition, we expand six new features from node and communication levels by considering the latest UAN methods’ requirements, which enhances the simulation flexibility and fineness of Aqua-Sim FG. Experimental results show that Aqua-Sim FG can simulate UANs’ performance realistically, reflect intelligent methods’ problems in real-ocean scenarios, and provide more effective troubleshooting and optimization for actual UANs. The basic simulator is available at https://github.com/JLU-smartocean/aqua-sim-fg.

Underwater backscatter is an emerging passive communication technology powered by underwater acoustic energy, which has emerged as a promising solution to the underwater energy problem. However, the backscatter mechanism causes the reflected signal strength to undergo periodic ups and downs due to the phase cancellation effect when the receiving end is in motion. Additionally, during the signal retro-reflective process, the reflected array signals deviate from the optimal beam direction when there is movement at the receiving end, introducing nonlinear attenuation into the signal strength. This paper proposes GKCformer signal strength forecasting method for underwater backscatter systems. It is based on the original sequence and Gram angular field image modal design to add period information. It utilizes Transformer to rearrange and improve channel feature information, and Kolmogorov-Arnold Networks to make the fitting better. Experimental results show that the proposed model outperforms the classical model in mean squared error (MSE) and mean absolute error (MAE) metrics, which demonstrates its better performance in prediction accuracy.

To facilitate the exploration and exploitation of underwater resources, autonomous systems and underwater acoustic networks (UANs) are deployed for tasks unsuitable for direct human intervention and exchange information between devices. To keep the reliability of information exchanged, direct-sequence spread spectrum (DSSS) communication is commonly adopted for this scenario. To simplify the channel equalization process in DSSS communication, a minimum-mean-square-error (MMSE) equalizer is often utilized. However, the characteristics of underwater acoustic channels and acoustic modem, including long delay spreads and limited computational resources, lead to high computational complexity for MMSE equalization, thereby reducing decoding efficiency. To address this challenge, we propose a refined MMSE equalizer, termed the efficient MMSE equalizer (EME). Unlike conventional MMSE methods, the EME approach involves initially despreading the received chip sequence, followed by equalizing on the noisy symbols. By reducing the size of the correlation matrix in the core computational step of MMSE equalization, our method significantly improves computational efficiency. We assess the computational complexity of the proposed EME approach in comparison to conventional MMSE equalization and validate its performance through simulations and experimental studies. The results demonstrate that the EME achieves a bit error rate (BER) performance comparable to that of conventional MMSE equalization under high signal-to-noise ratio (SNR) conditions, while significantly enhancing computational efficiency.

Underwater acoustic (UWA) communication is until now the only effective means for long distance underwater wireless communication, and hence it is the key foundation for Internet of Underwater Things (IoUT). However, in ocean environment, impulsive noise (IN) generated by natural and human factors usually seriously affects the performance of UWA communication. In this article, utilizing the powerful capability of deep learning, a 1-D convolutional neural network based on multiattention mechanism (1DCNN-MAM) for IN mitigation in UWA orthogonal frequency division multiplexing (OFDM) systems is proposed. To enhance the generalization performance of the network, it utilizes minimization of the energy on null subcarriers as an auxiliary task for network training. Furthermore, to adapt to specific environment quickly and reduce the amount of real data required for training, it adopts a network-based deep transfer learning approach for fine-tuning. To verify the performance of the proposed scheme, a sea trial has been carried out along with simulations, and both demonstrate that the proposed scheme can effectively suppress the IN in UWA OFDM systems.

Internet of Underwater Thing (IoUT) stands as promising technology facilitating diverse underwater applications. Nevertheless, IoUT across vast marine regions is challenged by highly diverse and fluctuating channel environments, which results in unreliable point-to-point (PTP) transmissions. Moreover, its multi-hop nature exacerbates severe unreliable end-to-end (ETE) transmissions. Existing methods utilize routing protocols to address the above challenges by independently power control for PTP reliability or multi-path transmission for ETE reliability. However, these methods ignore the interdependencies between power control and multi-path transmission, which fail to guarantee high energy-efficient reliability in resource-constrained and harsh underwater environments. To this end, we propose a joint power Control And Multi-Path routing (CAMP) protocol for IoUTs in varying environments. Specifically, we develop PTP and ETE reliability models by analyzing the interrelation between power control and multi-path routing, incorporating historical, current, and predictive information. A hybrid routing strategy is designed based on the reliability models to accommodate changing environmental conditions, residual energy, and link quality. This strategy initiates multi-path routing at the source and single-path forwarding at relay nodes, combined with power control. Extensive simulations demonstrate that CAMP achieves superior reliability (packet delivery rate) and energy efficiency, while simultaneously improving network performance in terms of latency and throughput.

Autonomous Underwater Vehicle (AUV)-assisted Underwater Acoustic Networks (UANs) are promising for complex ocean applications. In essence, an AUV-assisted UAN is still dominated by fixed nodes, and Time Division Multiple Access (TDMA)-based Medium Access Control (MAC) protocols have undisputed practicability in such fixed nodes-dominated UANs since they are simple and easy to deploy. However, AUV-assisted UANs may exist dynamic bidirectional data streams, while most existing protocols assume UANs have a unidirectional data stream, and their fixed scheduling sequence results in the long end-to-end delay in AUV-assisted UANs. In this paper, we first reveal a phenomenon between the data stream and the scheduling sequence, derived from real-world experiments: their consistent direction decreases the packet waiting delay but increases the slot length, and vice versa. To optimize the end-to-end delay, UANs with dynamic bidirectional data streams expect the MAC protocol to provide a flexible scheduling sequence. To this end, we propose a low-delay Adaptive Scheduling MAC protocol (AS-MAC) based on TDMA for AUV-assisted UANs. In AS-MAC, we analyze the relationship between scheduling sequence and data stream, extracting two significant factors: slot length and packet delay. Afterwards, we design Slot Length Model (SLM) and Packet Delay Model (PDM) to analyze the end-to-end delay of different data streams. Based on these two models, we present a Scheduling Sequence and Slot Length allocation Algorithm (SSSLA) to adaptively provide the minimum end-to-end delay for current bidirectional data streams. Extensive simulation results show that AS-MAC efficiently addresses severe queue congestion of the state-of-the-art protocols and reduces the end-to-end delay of different dynamic streams in various scenarios.

Underwater acoustic sensor networks (UASNs) drive toward strong environmental adaptability, intelligence, and multifunctionality. However, due to unique UASN characteristics, such as long propagation delay, dynamic channel quality, and high attenuation, existing studies present untimeliness, inefficiency, and inflexibility in real practice. Digital twin (DT) technology is promising for UASNs to break the above bottlenecks by providing high-fidelity status prediction and exploring optimal schemes. In this article, we propose a Digital Twin-based Network Architecture (DTNA), enhancing UASNs’ environmental adaptability, intelligence, and multifunctionality. By extracting real UASN information from local (node) and global (network) levels, we first design a layered architecture to improve the DT replica fidelity and UASN control flexibility. In local DT, we develop a resource allocation paradigm (RAPD), which rapidly perceives performance variations and iteratively optimizes allocation schemes to improve real-time environmental adaptability of resource allocation algorithms. In global DT, we aggregate decentralized local DT data and propose a collaborative Multi-agent reinforcement learning framework (CMFD) and a task-oriented network slicing (TNSD). CMFD patches scarce real data and provides extensive DT data to accelerate AI model training. TNSD unifies heterogeneous tasks’ demand extraction and efficiently provides comprehensive network status, improving the flexibility of multi-task scheduling algorithms. Finally, practical and simulation experiments verify the high fidelity of DT. Compared with the original UASN architecture, experiment results demonstrate that DTNA can: (i) improve the timeliness and robustness of resource allocation; (ii) greatly reduce the training time of AI algorithms; (iii) more rapidly obtain network status for multi-task scheduling at a low cost.

Underwater Wireless Sensor Networks (UWSNs) represent a promising technology that enables diverse underwater applications through acoustic communication. However, it encounters significant challenges including harsh communication environments, limited energy supply, and restricted signal transmission. This paper aims to provide efficient and reliable communication in underwater networks with limited energy and communication resources by optimizing the scheduling of communication links and adjusting transmission parameters (e.g., transmit power and transmission rate). The efficient and reliable communication multi-objective optimization problem (ERCMOP) is formulated as a decentralized partially observable Markov decision process (Dec-POMDP). ATraffic Load-AwareResourceManagement (TARM) strategy based on deep multi-agent reinforcement learning (MARL) is presented to address this problem. Specifically, a traffic load-aware mechanism that leverages the overhear information from neighboring nodes is designed to mitigate the disparity between partial observations and global states. Moreover, by incorporating a solution space optimization algorithm, the number of candidate solutions for the deep MARL-based decision-making model can be effectively reduced, thereby optimizing the computational complexity. Simulation results demonstrate the adaptability of TARM in various scenarios with different transmission demands and collision probabilities, while also validating the effectiveness of the proposed approach in supporting efficient and reliable communication in underwater networks with limited resources.

Due to its relatively lower propagation loss, acoustic wave is until now the only option for medium and long range underwater wireless communication. However, the low velocity of acoustic wave in water can easily lead to significant Doppler effect. Especially in the case of communication between underwater platforms with high mobility, the Doppler effect could even be time varying during signal transmission. In this paper, an iterative Doppler tracking algorithm based on Kalman filter is proposed for non-coherent frequency hopping frequency shift keying underwater acoustic communication (UWA) systems suffered from varying Doppler effect. Since the duration of a UWA communication signal is usually short, the change in relative motion between transmitter and receiver is not too drastic.Therefore the motion during this period is modeled by constant acceleration model or constant jerk model. Then, a receiver algorithm iteratively estimates and refines the time-varying Doppler is proposed, in which the refinement is carried out based on Kalman filter. Utilizing the filtered Doppler scale factors, this algorithm adaptively performs symbol synchronization and Doppler frequency shift correction in a symbol-by-symbol fashion, which effectively compensates the signal distortion induced by varying Doppler effect. Simulation results demonstrate the effectiveness of the proposed algorithm in different maneuvering scenes.

Underwater acoustic networks (UWANs) play a vital role in the Internet of Underwater Things (IoUT), enabling critical functions, such as communication, data collection, and navigation. Among the applications of the IoUT, localizing a mobile node (MN) via a UWAN is particularly promising. However, the existing localization algorithms are ineffective when faced with an insufficient number of time of arrival (TOA) measurements for an unsynchronized MN due to the presence of sparsely deployed anchor nodes and signal reception issues. Although tracking methods can offer position predictions, their accuracies are compromised over time due to the movement of MNs. To overcome these challenges, we propose a methodology that combines the departure time of a beacon signal (DOB) with limited TOA measurements. This methodology enables an MN to be localized using a TOA-based method, which typically requires fewer measurements than a time difference of arrival (TDOA)-based method. Based on this methodology, we introduce an algorithm called localization with insufficient TOA measurements (LITM), which comprises two subalgorithms: one for estimating and tracking the DOBs and the other for localizing MNs through a closed-form solution. Together, these subalgorithms provide accurate MN position estimates under the constraint of insufficient TOA measurements. To validate the performance of our proposed algorithm, we conduct both simulation studies and sea experiments. The results demonstrate the superior effectiveness and position estimation accuracy of our algorithm compared to those of the existing methods.

Localization is vital and fundamental for underwater acoustic sensor networks (UASNs), as it provides location information for UASNs to achieve various practical underwater tasks. Most existing localization methods assume small-scale scenarios without battery energy constraints, making it inapplicable to large-scale UASNs. In large-scale UASNs, localization suffers from the challenges of excessive energy consumption and large localization error because of harsh underwater conditions like node mobility and huge ranging errors. To this end, we propose an efficient localization scheme with velocity prediction (LSVP) to solve the above challenges for large-scale UASNs. LSVP considers node mobility, ranging errors, and energy balance in a unified framework, which is applicable to realistic and scalable UASNs. Specifically, we first design a Doppler-assisted velocity prediction (DVP) algorithm to decrease energy consumption, which can solve the excessive communications caused by node mobility under ocean currents. Then, a acrlong CIL algorithm is proposed to decrease the localization error, which can reduce location uncertainty and error propagation caused by ranging errors. Extensive simulation results indicate that LSVP can achieve accurate velocity prediction and high precision localization for large-scale UASNs.

Localization is a vital service in underwater acoustic sensor networks (UASNs). Autonomous underwater vehicles (AUVs), with their mobility and collaborations can provide accurate, extensive, and efficient localization service for large-scale UASNs. During localization, AUVs travel along the predefined paths and broadcast reference messages to aid sensor nodes in estimating locations. However, AUVs-aided localization faces the following two challenges: 1) complex localization path planning for multiple AUVs, which requires consideration of localization accuracy and optimization of travel path simultaneously and 2) harsh underwater localization conditions, such as unsynchronized clocks and stratification effects seriously affect the localization accuracy. To this end, an efficient AUVs-aided localization scheme (EAL) is proposed for large-scale UASNs, which jointly addresses the path planning and localization in an unified framework. Specifically, we propose a graph-based localization path planning mechanism, which considers the impact of path on localization and determines effective travel paths for AUVs. Furthermore, we design an iteration-based asynchronous localization mechanism, which could compensate the stratification effect and achieve accurate localization for the sensor nodes. Extensive simulation results show that the EAL can achieve efficient and high accuracy localization for the sensor nodes with the aid of multiple AUVs.

The automatic modulation classification for surface and underwater sensors in the perception layer is crucial in the Internet of Underwater Things (IoUT), where Deep Learning (DL) is becoming an important tool to improve classification accuracy. This work focuses on the radio environment in the perception layer. The biggest challenge in popular DL-based methods is deploying the algorithm in edge devices with limited computing power. Network pruning has been found to be a critical and effective method for network lightweight and the improvement of resources, thus mitigating potential interference. While not studied in previous work, this paper fills the hole in algorithm deployment’s criterion selection and accuracy loss. Specifically, we develop a Convolutional Neural Network (CNN) based lightweight framework on distinguishing modulated signals from generated datasets (which is named DLocean) in different Signal-to-Noise Ratios (SNR). The performance of the lightweight framework is tested on the edge device. The experiments demonstrate that the proposed model compensates for accuracy and can successfully classify the modulation schemes with 93.4% accuracy at the SNR=5 dB. Our results also show that the proposed framework can improve performance without exceeding the original network complexity on edge device deployment.

A high-performance network deployment strategy supports fundamental network services, such as topology controls, protocol designs, and boundary detections in underwater wireless sensor networks (UWSNs). Existing deployment methods treat nodes within the communication range as connected. However, in addition to internode distance, packet errors and collisions are also significant factors for point-to-point connectivity. Furthermore, when allocating node locations, deployment strategies focus on maximizing coverage, ignoring the tradeoff between coverage and network performance (reliability, latency, and energy efficiency). To this end, an efficient deployment scheme with network performance modeling (EDNPM) is proposed, to provide reliable data transmission in a time-aware and energy-efficient way for UWSNs. Specifically, we first explore sensor locations’ impact on communication and network factors, to improve the point-to-point connectivity and network performance. A network performance evaluation model (NPEM) is established to quantify performance metrics for guiding network deployment. Based on NPEM, network deployment is formulated as a multiobjective optimization problem, and we propose a novel network connection-constraint particle swarm optimization (NCPSO) algorithm to solve this problem. Notably, EDNPM is a unified network deployment framework for various underwater applications. Extensive experiments demonstrate that EDNPM outperforms other deployment algorithms in terms of network performance, and robustness with different network settings.

Single-image super-resolution (SISR) is essential for improving the extraction of useful information from images captured in the real world. Most existing super-resolution methods generally assume that low-resolution (LR) images are generated from high-resolution (HR) images through a known degradation model, such as bicubic downsampling. As a result, these methods do not exhibit favorable performance on real-world images with complex authentic degradations, significantly limiting their practicality, especially in application scenarios where image authenticity is strictly enforced. In this paper, We design a frequency separation network (FSN) to separate low-frequency information and generate high-frequency information, which can reconstruct high-resolution real-world images quickly and accurately. We proposed the various Gaussian filters as the frequency separation (FS) module to gradually separate the frequencies and route them to their respective feature extraction modules. Subsequently, we aggregate all the different frequency features using the adaptive feature fusion (AFF) module to generate the HR image. Therefore, FSN can focus on high-frequency information to restore image details and ensure stable restoration of important information, such as object contours, without generating false texture details. Extensive experiments demonstrated that our FSN achieves consistently superior visual quality and generalization ability with more realistic and natural textures in various scenarios.

This paper investigates the fair-effective communication and robustness in imperfect and energy-constrained underwater acoustic sensor networks (IC-UASNs). Specifically, we investigate the impact of unexpected node malfunctions on the network performance under the time-varying acoustic channels. Each node is expected to satisfy Quality of Service (QoS) requirements. However, achieving individual QoS requirements may interfere with other concurrent communications. Underwater nodes rely excessively on the rationality of other underwater nodes when guided by fully cooperative approaches, making it difficult to seek a trade-off between individual QoS and global fair-effective communications under imperfect conditions. Therefore, this paper presents aSEmi-COoperativePowerAllocation approach (SECOPA) that achieves fair-effective communication and robustness in IC-UASNs. The approach is distributed multi-agent reinforcement learning (MARL)-based, and the objectives are twofold. On the one hand, each intelligent node individually decides the transmission power to simultaneously optimize individual and global performance. On the other hand, advanced training algorithms are developed to provide imperfect environments for training robust models that can adapt to the time-varying acoustic channels and handle unexpected node failures in the network. Numerical results are presented to validate our proposed approach.

Medium Access Control (MAC) protocols are critical for scheduling resources to access multiple users without collisions in Underwater Acoustic Networks (UANs). Due to harsh marine environments and limited communication resources, UANs lack a standard MAC protocol to adapt to various scenarios. The specific UAN scenario suffers from how to analyze multiple basic MAC protocols’ performance boundaries and modify the most potential one. A practical solution is to evaluate MAC protocols’ performance by modeling data loss (collisions and packet errors) and service time. However, existing models provide inaccurate performance results, since they ignore the effects of unique UANs’ characteristics and MAC protocol diversity on data loss and service time. In this paper, we propose a MAC Performance Analysis Framework (MPAF) for UANs to consider both unique UANs’ characteristics and MAC protocols’ diversity. We design Successful Transmission Probability (STP) model and Packet Service Time (PST) model in MPAF to estimate nodal throughput, delay, and energy consumption. STP model analyzes data loss types of different MAC protocols by considering long propagation delay, half-duplex communication, and random backoff to achieve a superior STP result from a view of real underwater communication conditions. Based on STP model, we employ Markov chain to deduce the retransmission number in PST model. In this way, MPAF ensures effectiveness and applicability in real-ocean environments. Extensive simulation results show that MPAF can accurately evaluate different MAC protocols’ performance boundaries, select the most appropriate basic protocol, and provide modified suggestions for a specific UAN scenario.

Mobile Underwater Acoustic Networks (UANs) leverage Autonomous Underwater Vehicles (AUVs) to enhance flexibility and mobility, playing an essential role in ocean research. Similar to static UANs, the Medium Access Control (MAC) protocol is still critical for mobile UANs to achieve efficient communication. However, mobile UANs are delaysensitive and suffer from low Signal-to-Noise Ratio (SNR), which presents significant challenges for the design of MAC protocols. As a hybrid technology combining spread spectrum and multicarrier modulation, Multi-Carrier Code Division Multiple Access (MC-CDMA) offers simple multi-path channel equalization and flexible multi-user access, aiding the MAC protocol in achieving robust communication in mobile UANs. Along this line, we propose an MC-CDMA-based MAC (MC-MAC) protocol, which considers both characteristics of mobile UANs and MC-CDMA to achieve efficient concurrent communication. Specifically, to adequately utilize the limited underwater communication resources, we design an adaptive node clustering algorithm, classifying nodes based on propagation distance, relative mobile velocity, data size, and data grade. Meanwhile, the algorithm determines non-random initial center nodes and adaptively decides the optimal number of clusters to decrease the computational complexity. Based on the clustering results, we present a manyobjective optimization algorithm, which jointly allocates specific spreading code length, spreading code number, subcarrier range, and transmission power to optimize throughput, delay, and energy consumption in mobile UANs. Extensive simulation results demonstrate that MC-MAC fully leverages the advantages of MC-CDMA, providing efficient concurrent communication with lower energy consumption for mobile UANs compared to stateof-the-art protocols.

Underwater wireless sensor networks (UWSNs) stand as promising technologies facilitating diverse underwater applications. However, the major design issues of the considered system are the severely limited energy supply and unexpected node malfunctions. This paper aims to provide fair, efficient, and reliable (FER) communication to the imperfect and energy-constrained UWSNs (IC-UWSNs). Therefore, we formulate a FER-communication optimization problem (FERCOP) and propose ICRL-JSA to solve the formulated problem. ICRL-JSA is a deep multi-agent reinforcement learning (MARL)-based optimizer for IC-UWSNs through joint link scheduling and power allocation, which automatically learns scheduling algorithms without human intervention. However, conventional RL methods are unable to address the challenges posed by underwater environments and IC-UWSNs. To construct ICRL-JSA, we integrate deep Q-network into IC-UWSNs and propose an advanced training mechanism to deal with complex acoustic channels, limited energy supplies, and unexpected node malfunctions. Simulation results demonstrate the superiority of the proposed ICRL-JSA scheme with an advanced training mechanism compared to various benchmark algorithms.

Performing a high-capacity Medium Access Control (MAC) protocol suffers from low bandwidth and long propagation delay in Underwater Acoustic Networks (UANs). Non-Orthogonal Multiple Access (NOMA) is a promising technology to assist MAC protocols in overcoming the above restrictions and improving UANs’ capacity. It enables multiple users to access the same frequency-time resource based on power or code differences of user classes. However, UANs lack MAC research employing NOMA’s physical advantages. Most existing NOMA-based MAC protocols are designed for terrestrial networks, which are inapplicable to UANs. In classifying users, they ignore the effects of harsh marine environments on acoustic channels and fail to decrease channels’ interference, resulting in conflicting communications. Moreover, such unreasonable classification results further affect resource allocation, leading to low transmission rate and high energy consumption in UANs. In this paper, we propose a Hybrid NOMA-based MAC protocol (HN-MAC) to achieve efficient concurrent communication for UANs. Specifically, HN-MAC combines power-domain and code-domain NOMA to classify users and allocate communication resources. For the user classification, we propose an Adaptive Clustering Algorithm (ACA), which dynamically determines the clusters’ number and classifies users based on channel gain and channel correlation under multipath conditions. In this way, HN-MAC decreases interference among multiple users in various ocean scenarios. During the resource allocation, we formulate a joint allocation problem of transmission power and codebook based on the clustering result to optimize transmission rate and energy consumption. Further, a genetic algorithm is proposed to solve the allocation problem by considering resource constraints. Simulation results show that HN-MAC provides more stable concurrent communications with less resource consumption than the state-of-the-art protocols in various UANs.

Providing qualified and fair communications for underwater wireless sensor networks (UWSNs) has garnered considerable interest in light of scarce resources and dynamic channel conditions. Fairness is critical in various situations, including emergency communications in resource-constrained underwater networks that balance load and energy among nodes to optimize network performance. Existing solutions for fair communications, on the other hand, frequently come at the expense of network capacity. This article focuses on the power-management strategy that jointly optimizes the reuse, fairness, and capacity of UWSNs while also proposing a new metric for fair spatial reuse in networks: the fair reuse index (FRI). We observe that the FRI for UWSNs is strongly reliant on the network density, channel conditions, and application requirements. As a result, UWSNs commonly exhibit load imbalance and struggle to meet required network lifetimes. Toward this end, we propose DMPM, a deep multiagent reinforcement learning-based power-management strategy for increasing the fair reuse of UWSNs. DMPM strives to maximize the network’s fair reuse while allowing for gentle network capacity decrease. In two representative communication scenarios, numerical results demonstrate that DMPM achieves a significantly better tradeoff between network capacity and fair reuse than baseline techniques. We also construct three reward functions for DMPM and discuss how different reward functions affect node behaviors. Delivery delays of different models are discussed. We hope that the work provided in this study will prove to be invaluable in the design and optimization of UWSNs.

Performing an effective media access control (MAC) protocol suffers from strong dependencies between Underwater Acoustic Networks’ upper and lower layers: 1) the network layer frequently uses geo-routing protocols, which do not provide the specific next-hop for MAC protocols, resulting in serious data collisions and 2) in such scenarios with the unknown next-hop, fixed orthogonal frequency-division multiplexing (OFDM) resource does not adapt to the changing environment, and degrades network performance (OFDM is a mature modulation technology in the physical layer). However, there is scant research on MAC protocols considering the network layer and the physical layer simultaneously, to solve data collisions and resource allocation. To this end, we present a cross-layer MAC protocol to integrate Geo-routing protocols and OFDM technology (GO-MAC) at the same time. GO-MAC employs a handshake scheme to allocate optimal communication resources and select the next-hop concurrently. First, we formulate the OFDM resource allocation as a joint optimization problem based on transmission mode, subcarrier spacing, guard interval, and transmission power, to decrease transmission delay and energy consumption. Then, a Karush–Kuhn–Tucker conditions-based Heuristic algorithm (KKT-H) is proposed to solve this problem. Finally, we consider node congestion and channel quality to assist geo-routing protocols with the next-hop selection, and decrease packet collisions. Simulation results show that our protocol matches geo-routing protocols and OFDM technology better than the state-of-the-art protocols, providing higher end-to-end reliability with lower costs.

Underwater acoustic communication networks (UACNs) have been widely utilized in recent years because of the growing interest in interactive information in the deep ocean. Compared with traditional radio wireless networks, UACNs are characterized by complex and dynamic 3-D network topology and longer signal propagation delay. Therefore, recent studies on UACNs usually apply dynamic routes in data communication to adapt to complex UACN structure. However, the approaches are hardly adequate for UACN scenarios with high-traffic requirements. This is because dynamic routing methods, such as opportunistic routing, usually require external contention costs to control the routing paths, which leads to decreased network throughput. Accordingly, this article focuses on enabling high-speed acoustic communications for underwater application scenarios with high-traffic requirements, and proposes an underwater data transmission method using the multichannel full-duplex (FD) communication technique. The proposed method applies the underwater orthogonal frequency division multiple access (OFDM) technique, that uses collision-free channels to relay data in multihop routes for simultaneous transmission and reception. Unlike the traditional communication methods of UACNs with dynamic routing, the proposed one uses static routes when transporting data over hop-by-hop paths to achieve stable and uninterrupted FD communication. The approach also includes a directional forwarding-based route request method and an elimination-based channel evaluation proposal, which purpose to reduce the overheads from exploring routes and enable collision-free FD communications. The performance analysis of the proposed method is under simulated conditions of high-traffic UACN scenarios, and the results show that it has superior performance compare to the classical underwater data transmission methods.

The jamming and interference in vehicular ad hoc networks (VANETs) depend on the channel states of vehicles from the ambient radio transmitter, which in turn result from the topologies and radio features. In this paper, we propose an environment-aware reinforcement learning (RL)-based VANET communication scheme against jamming and interference that applies the post decision state algorithm to optimize the power allocation and channel selection without relying on the jamming attack model. This scheme exploits the environment information in the state formulation due to the traffic density and their locations reflect the interference level, as well as the location of transmission vehicle combined with building structure and heights indicate the channel gain and shadowing. The proposed post decision state-based RL method employs the estimated future communication distances of the moving vehicles to accelerate the learning process. We provide the performance bounds of the energy consumption, bit error rate (BER), and utility based on a Nash equilibrium. Simulation results show that the proposed scheme significantly reduces the BER with less energy consumption compared with the benchmark.

Underwater acoustic sensor networks (UASNs) have been widely applied in marine monitoring, military reconnaissance, hydrology surveys, etc. Their location information is an important apriori knowledge when they are carried out underwater. However, the complex underwater environments impose great challenges on location acquisition, especially for autonomous underwater vehicles (AUVs), because of their mobility and finite power. In UASNs, existing location and navigation methods can offer AUVs position information, but they may either need a doppler velocity log (DVL), which is inefficient due to the complex underwater environments, or they may require additional localization infrastructure to deploy underwater, which suffers from large communication latency among AUVs, and costs enormous power. In this article, an efficient velocity estimation and location prediction method (VELP) in UASNs is proposed to avoid the above restrictions. It only utilizes collaborations based on communication among AUVs to achieve higher precision location with lower cost. Specifically, we apply an AUV-assisted velocity estimation algorithm with Doppler shift estimation in the physical layer of UASNs to improve the velocity estimation accuracy instead of the DVL. Meanwhile, we build a belief propagation-neural network-based location prediction model, which decreases the communication requirements and obviates introducing modeling errors. Extensive experimental results show VELP achieves superior performance on both accuracy and efficiency, demonstrating its great advantage in offering AUVs’ location information.

Providing qualified and sustainable communications is one of the key challenges for the Internet of Underwater Things (IoUT) facing constrained energy supplements, nonstationary environments, and severe communication interference. Owing to spatial separation, several nodes can (and are often required to) make transmissions simultaneously to maximize network capacity. However, existing transmission solutions often face the dilemma between maximizing local capacity and global concurrency. We break this dilemma via UDARMF, an underwater distributed and adaptive resource management framework, which maximizes network capacity by supporting an increased number of communications in the network. It is a distributed deep multiagent reinforcement learning framework that uses an observation encoder and a local utility network to coordinate the collaboration among underwater nodes by adaptively tuning its transmit parameters. We designed experiments to compare UDARMF with baselines in network capacity, concurrency, and energy efficiency. Extensive experiments were conducted to find the appropriate hyperparameters to achieve the optimal network performances. We also analyze the performance of UDARMF and baselines over diverse communication and lifetime requirements, communication environment, and energy storage. Simple closed-form approximations of UDARMF are given to reveal that an energy-constrained network’s capacity increases with available energy, following a linear trend on the logarithmic scale. Experimental results demonstrate that compared to other methods, UDARMF achieves a much better tradeoff between network capacity and concurrency, at which the lifetime requirements are satisfied. The proposed framework and the closed-form approximations are likely to become valuable tools in designing and analyzing IoUT.

The coming 6G communication technology introduces the possibility of practice underwater Internet of Things (UIoT) applications with high-speed and reliable underwater communications. Among them, the cooperative coverage path planning (CPP) with autonomous underwater vehicles (AUVs) is a promising approach for enabling deep ocean exploration. The cooperative CPP in underwater is challenged by several marine factors, typically the harsh underwater communication environment, which brings difficulties in sharing the coverage progresses of AUVs and the environmental information such as the seafloor bathymetry, obstacles, etc. Accordingly, this paper proposes a novel CPP method based on 6G enabled cooperative AUVs, named the Pac-AUV. As the name suggests, the method is based on the mechanism of the Ms. Pac-Man game, where AUV-assisted subsea exploration is considered as cooperative Pac-Men sharing the Pac-Dots distributed on the seafloor. The Pac-AUV involves two steps in cooperative CPP. One is a dot-spreading-based mission assignment (DMA), which is discretely performed by each AUV and requires support from reliable underwater communication in sharing the Pac-Dots. The other step is virtual attraction-based coverage path planning (V-CPP), which adopts the virtual attraction force from the Pac-Dots, results in low computational complexities in generating the coverage paths and avoids obstacles. Simulations are performed to demonstrate the performance of the Pac-AUV, and the results prove the advantages of cooperative and balanced CPP executions.

Underwater acoustic sensor networks (UASNs) have been proposed as a promising way in supporting the Underwater-Internet-of-Things (UIoT) applications. However, to guarantee and improve Quality of Service (QoS), they are still facing great challenges especially when it comes to enabling reliable data communication for the UIoT applications and meanwhile protecting the marine ecosystems for sustainable underwater monitoring and exploration; this is because the acoustic signals used by UASNs can do harmful interference to vocalizing marine mammals. Therefore, the article proposed an ecologically friendly data transmission scheme for UASNs, which adopts an interference-aware opportunistic route discovery method and a frequency-division multiplexing (FDM)-based full-duplex communication scheme. First, in the route discovery phase, a virtual-void zone scheme, an FDM-based channel allocation approach and a Bayesian network-based mammal avoidance strategy is introduced to establish interference-free routing paths for enabling reliable and environmentally friendly data transmissions. Then during the data transmission phase, an FDM-based full-duplex communication technique is adopted to enable high-speed data flow from the seabed to the surface. Extensive simulations indicate that the proposed scheme has excellent advantages in terms of QoS while also taking marine mammals into account since the signal interference to both underwater nodes and nearby vocalizing mammals is significantly mitigated.

Escorting a moving object in a subsea environment with cooperative autonomous underwater vehicles (AUVs) is a typical subject in Underwater Internet of Things (UIoT) applications. It involves two issues that should be studied. First, a mobile task assignment method is required to lead the AUVs to the escorting positions; then, a formation control scheme should be utilized to safely escort the moving object to the destination. Accordingly, in this article, a comprehensive target escorting mechanism called the cooperative-control-based underwater target estimating mechanism (CUTE) is proposed, which includes a belief-function-method-based self-organizing map algorithm for task assignment and an artificial potential field-based formation control method. The task assignment method aims to establish smooth routes from the AUVs to the escort position while flexibly avoiding obstacles, and the formation control method aims to improve the monitoring coverage on the escort route by rotating the formation structure while following the moving object. Simulations show that the proposed CUTE method may be very practical in underwater target escorting scenarios.

As one of the most promising networks, the space–air–ground–aqua integrated network (SAGAIN) has the characteristics of wide coverage and large information capacity, which can meet various requests from users in different domains. With the rapid growth of data and information generated by the Internet of Things (IoT), SAGAIN has received much attention in recent years. However, the existing network architectures are not capable of providing personalized network services according to different task types in SAGAIN. Besides, they cannot deal with many problems in SAGAIN well, such as heterogeneous network disconnection, high network delay, intermittent interruption, and unbalanced network load. In this article, in order to solve the abovementioned problems, we propose a novel architecture for SAGAIN named task-oriented intelligent networking architecture (TOINA). First, we apply the edge-cloud computing technology and network domain division in TOINA to realize intelligent networking and reduce the latency. Second, the task-oriented networking method is proposed to provide personalized network services and increase network intelligence. Third, we intend to leverage the information center network (ICN) paradigm to build the SAGAIN and optimize the content naming rules. Furthermore, a preprocessing layer was added in the network protocol stack to perform the heterogeneous network convergence in SAGAIN. In addition, some security technologies related to network architecture are considered in SAGAIN. This article presents the background, rationale, and benefits of the TOINA for SAGAIN. Besides, a specific case is studied to illustrate the network architecture work process further.

For an autonomous underwater vehicle (AUV), navigation is a key functionality. Dead-reckoning (DR) navigation is an important class among all the AUV navigation methods. In DR, the measurement errors of inertial sensors (such as gyroscopes and accelerometers) lead to accumulated errors with time, which affect navigation accuracy significantly. Especially, accumulated errors in fast-changing environments, such as waves near or on the surface, are tough to handle. In this article, we propose a neural-network-based AUV navigation method for fast-changing environments, called NN-DR. NN-DR employs the neural network to predict pitch angles accurately, which is our core contribution. In NN-DR, we smoothly integrate the Kalman filter, neural network, and velocity compensation to reduce accumulated errors. Extensive simulation experiments are conducted to test the correctness and stability of NN-DR, and the results show that NN-DR is very effective in lowering accumulated errors. For instance, at time 300 s, NN-DR achieves superior performance on accuracy for navigation, about 160 times than the state-of-the-art DR methods, demonstrating great advantage on AUV navigation for fast-changing environments.

Time synchronization and localization are basic services in a sensor network system. Although they often depend on each other, they are usually tackled independently. In this work, we investigate the time synchronization and localization problems in underwater sensor networks, where more challenges are introduced because of the unique characteristics of the water environment. These challenges include long propagation delay and transmission delay, low bandwidth, energy constraint, mobility, etc. We propose a joint solution for localization and time synchronization, in which the stratification effect of underwater medium is considered, so that the bias in the range estimates caused by assuming sound waves travel in straight lines in water environments is compensated. By combining time synchronization and localization, the accuracy of both are improved jointly. Additionally, an advanced tracking algorithm interactive multiple model (IMM) is adopted to improve the accuracy of localization in the mobile case. Furthermore, by combining both services, the number of required exchanged messages is significantly reduced, which saves on energy consumption. Simulation results show that both services are improved and benefit from this scheme.

Swarms of autonomous underwater vehicles (AUVs) forming mobile underwater networks often operate in moving currents, which introduce severe turbulence that interferes with coordinated and stealthy navigation of fleet. Therefore, individual AUV must adjust their heading whenever needed to ensure it can reach a pre-determined destination. To achieve accurate navigation, AUVs must maintain precise knowledge of their locations. This paper develops the “Suave” (Swarm underwater autonomous vehicle localization) algorithm to localize swarms of AUVs operating in rough waters. The purpose of Suave is to ensure that all AUVs arrive at their destinations by preserving localization throughout the entire mission. Suave lowers the probability that an AUV swarm is detected by reducing the number of occasions that vehicles must surface to obtain accurate location information from external sources such as satellites. The Suave algorithm also achieves better energy conservation through improved control of localization reference messages. Simulations show Suave significantly improves localization accuracy, lowers energy consumption, and the probability of swarm detection.

Time synchronization plays a critical role in distributed network systems. In this paper, we investigate the time synchronization problem in the context of underwater sensor networks (UWSNs). Although many time-synchronization protocols have been proposed for terrestrial wireless sensor networks, none of them can be directly applied to UWSNs. This is because most of these protocols do not consider long propagation delays and sensor node mobility, which are important attributes in UWSNs. In addition, UWSNs usually have high requirements in energy efficiency. To solve these new challenges, innovative time synchronization solutions are demanded. In this paper, we propose a pairwise, cross-layer, time-synchronization scheme for mobile underwater sensor networks, called DA-Sync. The scheme proposes a framework to estimate the doppler shift caused by mobility, more precisely through accounting the impact of the skew. To refine the relative velocity estimation, and consequently to enhance the synchronization accuracy, the Kalman filter is employed. Further, the clock skew and offset are calibrated by two runs of linear regression. Simulation results show that DA-Sync outperforms the existing synchronization schemes in both accuracy and energy efficiency.

The performance of Underwater Sensor Networks (UWSNs) can be severely affected by the dynamics of underwater environment. A surface sink is usually deployed at a pre-specified location to maximize one or more performance metrics. However, when the network is dynamic, a redeployment of surface sink should be considered to reduce the effect of mobility on the network performance. Redeployment can be done periodically, at times based on a mobility prediction models, or adaptively based on performance degradation. Unnecessary redeployment can result from using the periodic or prediction based redeployment. In this paper we present an adaptive dynamic sink redeployment strategy that enforces redeployment only if a reduction in energy consumption is guaranteed. The redeployment decision is based on routing information collected at the surface sink throughout network operation. We use a location unaware routing protocol “adaptive power controlled routing protocol” as the underlying routing strategy. When the mobility of the network is not severe, nodes tend to use a fixed power level to communicate with neighboring nodes or surface sink. However, if more nodes are switching to use higher power levels for communication and the energy consumption is increased a sink redeployment procedure is started. Surface sink then triggers localization and finds the optimal new location of surface sink to minimize total energy consumption. Simulation results show that adaptive sink redeployment achieves a considerable reduction in energy consumption.

Time synchronization is an important requirement for many services provided by distributed networks. A lot of time synchronization protocols have been proposed for terrestrial Wireless Sensor Networks (WSNs). However, none of them can be directly applied to Underwater Sensor Networks (UWSNs). A synchronization algorithm for UWSNs must consider additional factors such as long propagation delays from the use of acoustic communication and sensor node mobility. These unique challenges make the accuracy of synchronization procedures for UWSNs even more critical. Time synchronization solutions specifically designed for UWSNs are needed to satisfy these new requirements. This paper proposes Mobi-Sync, a novel time synchronization scheme for mobile underwater sensor networks. Mobi-Sync distinguishes itself from previous approaches for terrestrial WSN by considering spatial correlation among the mobility patterns of neighboring UWSNs nodes. This enables Mobi-Sync to accurately estimate the long dynamic propagation delays. Simulation results show that Mobi-Sync outperforms existing schemes in both accuracy and energy efficiency.

In underwater wireless sensor networks (UWSNs), one efficient way to alleviate the burdens of high propagation delay and high error probability during transmission is to deploy surface-level gateways, which utilize radio waves to forward information to a control station. Usually, deployment of the gateways is considered as an optimization problem with the objective to best satisfy certain parameters. In this work, we propose a prediction assisted dynamic surface gateway placement algorithm for mobile underwater sensor networks, called “PADP”, which intends to maximize the coverage within a specific period of time. PADP applies a tracking scheme “IMM” to predict sensor nodes' positions, adopts branch-and-cut to solve the optimization problem, and employs a disjoint-set data structure to handle connectivity. Simulation results show that PADP outperforms the existing static gateway deployment scheme.

Time synchronization and localization are basic services in a sensor network system. Although they often depend on each other, they are usually tackled independently. In this work, we investigate time synchronization and localization problems in underwater sensor networks. We propose a joint solution for localization and time synchronization, in which the stratification effect of underwater medium is considered, so that the bias in the range estimates caused by assuming sound waves travel in straight lines in water environments is compensated. By combining time synchronization and localization, the accuracy of both are improved jointly. Additionally, an advanced tracking algorithm IMM (interactive multiple model) is adopted to improve the accuracy of localization in the mobile case. Furthermore, by combining both services, the number of required exchanged messages is significantly reduced, which saves on energy consumption. Simulation results show that both services are improved and benefit from this scheme.

Time synchronization plays a critical role in distributed network systems. In this paper, we investigate the time synchronization problem in the context of underwater sensor networks (UWSNs). We propose a pairwise, cross-layer, time synchronization scheme for mobile underwater sensor networks, called TSMU. Facilitated by the Kalman Filter, the proposed method greatly improves the dynamic propagation delay estimation by exploring the Doppler effect. Simulation results show that TSMU outperforms existing synchronization schemes in both accuracy and energy efficiency.

Time synchronization is a critical service for distributed networks. In this paper, we investigate this problem in underwater sensor networks (UWSNs). We propose a novel time synchronization scheme, called ``Mobi-Sync''. Mobi-Sync effectively utilizes the spatial correlation of underwater mobile sensor nodes to estimate the long and dynamic propagation delays. Simulation results show that Mobi-Sync outperforms existing schemes in both accuracy and energy efficiency.