Automatic modulation recognition (AMR) in underwater acoustic (UWA) communications is limited by scarce training data and channel impairments such as multipath propagation, Doppler shifts, and ambient noise. To address these challenges, we propose Mel-Diffusion, a diffusion model–based data augmentation framework. The method first converts time-domain signals into Mel spectrograms for preprocessing. The proposed Mel-Diffusion model, which is a DDPM-based diffusion framework with a Transformer backbone, is then applied for feature enhancement. Finally, the Griffin–Lim algorithm is used to reconstruct the time-domain signals. Building upon this processing pipeline, Mel-Diffusion effectively captures modulation features and latent channel characteristics. In addition, a Depth-Wise–PointWise Feed Forward Network (DPFFN) integrated into the diffusion model reduces model complexity, while Denoising Diffusion Implicit Model (DDIM) sampling improves computational efficiency. Experiments on datasets from the reservoir and the sea demonstrate that Mel-Diffusion generates realistic and diverse signals, improving AMR accuracy by up to 5.51% and 5.27% under data-scarce conditions. After data augmentation, the highest AMR accuracies reach 98.66% and 95.81%, respectively, on the reservoir and sea datasets. These results highlight the effectiveness of Mel-Diffusion as a practical solution for accurate and robust AMR in complex UWA environments.

Physical layer security (PLS), as a key technology for ensuring communication security, is widely applied in both point-to-point and multi-user communication scenarios due to its low computational and communicational overhead. However, executing physical layer security in underwater acoustic sensor networks (UASNs) poses considerable challenges in multi-user scenarios where hybrid attackers are present. To enhance the security performance of OFDMA based UASNs, we first model the secrecy capacity under the scenario of hybrid attackers. Then a joint subcarrier and power allocation algorithm named MWOM to maximize the total secrecy capacity of multiple users is proposed. In addition, an overlapping window-based dynamic clustering scheme is applied to reduce the feedback delay of the communication link. Finally, to reduce the complexity of the proposed allocation algorithm, we map the resource allocation problem to a bipartite graph model and derive the sub-optimal solution of the problem by leveraging graph theory. Simulation results verify that the proposed algorithm outperforms conventional algorithms in security performance, with negligible impact on communication performance. More importantly, we conducted actual sea trials in real underwater environments, which consistently confirmed these results.

In open underwater environments, ensuring accurate positions of sensors while protecting private information of localization systems presents a significant challenge. The physical channel differences between terrestrial and underwater networks render most existing privacy protection schemes designed for terrestrial networks inapplicable underwater. Moreover, limited research on underwater privacy protection has led to high implementation complexity and communication expenses. In this paper, to reduce the complexity of privacy protection, a secure mobile localization scheme using autonomous underwater vehicles (AUVs) as anchors is proposed for underwater sensor networks, based on adversarial neural cryptography utilizing acoustic channel features. Depending on whether eavesdroppers show interest in keys, two adversarial cryptography models are proposed to protect transmission of legitimate localization information and to actively counter eavesdroppers with learning capabilities in real time. Furthermore, to obtain effective keys and minimize unnecessary key transmission, random physical layer channel features are dynamically utilized as real-time keys for the cryptography system, and a synchronous channel probing protocol is designed for key generation. Simulation and experimental results demonstrate that, compared to other approaches, the proposed secure localization scheme effectively prevents the leakage of position information and maintains localization accuracy while operating at lower implementation complexity and communication expenses.

A persistent risk in many underwater localization solutions is the leakage of position information, since anchor positions are revealed to the sensors performing position estimation. While cryptography-based secure localization mechanisms for underwater acoustic sensor networks (UASNs) can protect the privacy of anchors, varying levels of privacy preservation impact localization accuracy. Therefore, it is necessary to derive a quantitative relationship between privacy preservation levels and localization accuracy. In this paper, we first model the probability density function (PDF) of anchor decoding errors under privacy preservation constraints when anchors adopt an encrypted system to safeguard position information. The model parameters are optimized using the expectation-maximization (EM) algorithm. Next, based on the ranging PDF and anchor decoding error PDF distribution, we derive the position error bound (PEB) function that links privacy preservation levels with target localization accuracy. Simulation and field experiment results validate the effectiveness of the theoretical model.

Underwater acoustic sensor networks (UASNs) constitute the primary infrastructure for the Internet of Underwater Things (IoUT), but they face constraints in high-rate reliable transmission due to the complex underwater acoustic (UWA) channel characteristics and the scarce pilot resources. Orthogonal frequency division multiple access (OFDMA) has been widely adopted in UASNs owing to its capability for multi-user parallel transmission and flexible resource allocation. However, the severely limited and non-uniformly distributed pilots pose significant challenges for multi-user channel estimation in OFDMA-based UASNs. To address these challenges, this paper establishes a compressed sensing (CS)-based multi-user channel estimation model for OFDMA-based UASNs. Considering the significant variations in channel conditions among different UWA links, we propose an adaptive pilot allocation method that dynamically optimizes the number of pilots assigned to each user. Building on this, we further propose a bi-objective joint pilot position and power optimization (BOJPO) algorithm, which simultaneously minimizes both the mutual coherence and total coherence of the measurement matrix to enhance estimation accuracy. Simulation and sea experiment results demonstrate that the proposed pilot optimization scheme exhibits superior performance, delivering stable and reliable channel estimation for OFDMA-based UASNs despite severely scarce pilot resources.

In harsh underwater environments, the localization of network nodes faces severe challenges due to open deployment environments. Most existing underwater localization methods suffer from privacy leaks. However, privacy protection schemes applied in terrestrial networks are not viable for underwater acoustic networks due to stratification effects and multipath complexities. In this paper, we introduce a secure localization scheme for underwater wireless sensor networks (UWSNs) utilizing cooperative beamforming among mobile underwater anchor nodes. With this scheme, the underwater sensor communicates and ranges with mobile anchor nodes to perform self-localization via time difference of arrival (TDOA) algorithm. However, the presence of eavesdroppers poses a threat by intercepting information emitted by the anchors. To avoid localization information leakage, then we model the secure localization requirement as a multi-anchors multi-objective dual joint optimization problem to enhance both security and energy performance. The deep reinforcement learning (DRL)-based multi-agent deep deterministic policy gradient (MADDPG) algorithm is applied to solve the optimization problem. Both simulation and field experimental results robustly validate the efficiency and accuracy of the proposed secure localization scheme.

In most existing underwater localization solutions, the risk of position information leakage persists, as anchor positions are revealed to the sensors performing position estimation. Conventional privacy protection methods designed for terrestrial networks often fail underwater due to the unique characteristics of the physical channels. Moreover, solutions proposed for the limited research on underwater privacy protection introduce expensive equipment costs and communication expenses. To tackle these challenges and reduce costs, this paper proposes a novel secure localization scheme tailored for underwater acoustic sensor networks (UASNs) based on adversarial neural cryptography utilizing acoustic channel features. The proposed scheme introduces an adversarial cryptography model to safeguard the transmission of legitimate localization data and dynamically counter eavesdroppers with learning capabilities in real-time. Furthermore, to obtain effective keys and minimize unnecessary key transmission, the scheme strategically employs channel features as cryptographic keys. Simulation results validate the effectiveness of this approach, demonstrating its ability to prevent the leakage of positional data, maintain localization precision, and operate with reduced hardware expenses and communication overhead compared to conventional methods.

In harsh underwater environments, position accuracy and privacy of sensors are equally important. Most underwater localization schemes suffer from privacy leakage, and privacy protection schemes for terrestrial networks are not applicable to underwater acoustic networks with stratification effects and multipath. Therefore, this paper proposes a secure localization scheme based on autonomous underwater vehicle (AUV) formation cooperative beamforming for underwater wire-less sensor networks (UWSNs) considering acoustic channel char-acteristic. The underwater sensor receives position information from multiple AUV anchors and utilizes time difference of arrival (TDOA) for self-localization. Eavesdroppers exist to overhear the information emitted by the anchors, thereby destroying the entire localization system. We model the secure localization requirement as a multi-AUV multi-objective dual joint optimization problem to optimize the security and energy performance, and adopt the deep reinforcement learning (DRL)-based multi-agent deep deterministic policy gradient (MADDPG) algorithm to solve it. Simulation and sea experimental results corroborate the efficiency and accuracy of the proposed secure localization scheme.

Underwater acoustic (UWA) communication faces security challenges owing to the open nature of the UWA channel and the uncertainty of the underwater node deployment environment. Key generation schemes based on physical layer information do not require predefined keys or the introduction of additional assistance nodes, which is feasible in resource-constrained underwater scenarios. This article proposes an underwater key generation scheme that includes a channel probing protocol and a key generation algorithm. Based on the spatio-temporal dynamic characteristics of the UWA channel, the amplitude of and time delay information about the channel impulse responses (CIRs) are combined as channel parameters to generate the raw keys. Further, a synchronous probing approach is designed to decrease the probing time difference between the forward and backward channels to enhance the reciprocity of the measurements. The results of the field experiments demonstrated the effectiveness of the key generation scheme and the performance improvements resulting from synchronous probing.

Physical-layer-based key generation is a promising technique for enhancing the security of underwater acoustic sensor networks (UASNs) and has been widely studied in point-to-point scenarios. However, when it is extended to multiuser scenarios in UASNs, its overhead costs are high and the information transmitted is prone to leakage. In this study, a group key generation scheme called STO-GSKG is proposed to share a secret key for multiple users. The pairwise keys extracted from the acoustic channels between the cluster head and the member nodes are used to protect the distribution of group keys. Specifically, to reduce the time consumption and communication overhead required for probing channels, a spatial-temporal reuse-based channel probing method that exploits large delays of underwater acoustic (UWA) channels is proposed. Then, secret bit sequences with high consistency and strong randomness are generated using a joint design that utilizes the multipath characteristics of UWA channels and introduces time-varying channel obfuscation information. The performance of the proposed scheme is evaluated through simulation and field experiments. The results show that the STO-GSKG scheme is more effective and robust than existing methods.

In response to the increasing demand for marine data transmission, underwater acoustic (UWA) communication is widely recognized as a key technology to support various applications of the Internet of Underwater Things (IoUT). However, due to the narrow bandwidth shared by multiple acoustic systems and the constrained energy of the underwater nodes, it is an urgent problem to design an energy-efficient resource allocation scheme which can avoid interference simultaneously. In this article, we establish a cognitive orthogonal frequency-division multiple access (OFDMA) UWA communication system, where dynamic resource allocation relies on the feedback channel state information (CSI) and spectrum usage of interference. In order to detect interference, a spectrum sensing scheme applicable to the underwater environment is proposed and experimentally verified. Subsequently, the resource allocation problem is mathematically modeled with energy efficiency (EE) as the reasonable optimization objective in UWA communication systems. In order to reduce the computational complexity, the original problem is decomposed into two subproblems and a low-complexity algorithm named acrlong BOSC- acrlong TLI is developed for feasible solutions. Simulation and sea experiment results show that the proposed algorithm outperforms the existing underwater resource allocation schemes.

Emerging autonomous underwater vehicles (AUVs) with small size, low speed, and low noise characteristics have shown promising applications in underwater monitoring. However, this also makes passive localization of underwater non-cooperative targets more challenging. Most localization schemes aim to improve localization accuracy while giving less consideration to additional related costs such as range flexibility, security, and energy consumption. To reduce such costs while ensuring accuracy, a weak-control AUV formation-based cooperative passive localization scheme is proposed, where AUVs do not require strict formation control or rely on anchor nodes to obtain absolute positions. Initially, there is a loose formation of multiple AUVs patrols until a target signal is passively and synchronously detected. Subsequently, the formation calibration is triggered. A rough formation calibration method is proposed based on signal interaction, where stratification effect compensation is considered. A precise formation calibration method adopting an inertial measurement unit and an extended Kalman filter algorithm is proposed to obtain a more accurate formation position by compensating for AUV drift error during rough calibration. Finally, the target position can be localized based on precise AUV formation using time difference of arrival method. Simulation and sea experimental results corroborate the efficiency and accuracy of the proposed localization scheme.

In recent years, underwater acoustic sensor networks (UASNs) have become a promising research field. Due to the limited energy of underwater nodes and highly dynamic acoustic link status, it is a challenge to balance the performance of reliability and energy consumption for existing routing schemes . In this paper, an adaptive cooperation-based geographic segmented opportunistic routing protocol (ACGSOR) for UASNs is proposed. ACGSOR is a geographic and opportunistic routing protocol which restricts the number of relay nodes . To deal with the void region problem and reduce energy consumption, a novel local topology-aware strategy is designed to obtain two-hop node information. With the local topology information, the proposed routing segmentation scheme divides the end-to-end path into several short opportunistic routing segments. Furthermore, a cooperation mechanism is applied to switch the transmission mode adaptively. Simulation results show that, compared with existing protocols, ACGSOR can improve the network performance in different scenarios.

In underwater Internet of Things (IoT) applications, efficient and reliable transmission is demanding. Recently, the nonorthogonal multicarrier (MC) modulation is utilized as a promising spectrum-efficient solution in the underwater acoustic (UWA) channel, where generally, the constellation mapping (CM) methods (such as 8-PSK and 16-QAM) are utilized to further increase the transmission rate. In this article, we focus on the transmission of sonar images among IoT nodes and a constellation diagram learning-based adaptive sparse nonorthogonal wavelet division multiplexing (CDLA-SNOWDM) system is proposed to combat complex channel states between IoT nodes. We found that different CM methods contribute to unique time-frequency characteristics in constellation diagrams. We first utilize the time-frequency characteristics in designing the nonorthogonal subcarriers. The complex-valued CM signals are regarded as a new type of image. We introduce dictionary learning (DL) to nonorthogonal subcarrier designing, with the idea that designing CM signal adaptive nonorthogonal subcarriers can be regarded as designing an adaptive subcarrier dictionary. The CDLA-SNOWDM modulation is performed as a projection of the CM image to the subcarriers with an adaptive dictionary. The learned adaptive subcarriers can represent constellation diagrams more efficiently and improve transmission reliability. Both simulations and experiments show that the proposed scheme improved the reliability in transmitting sonar images over other orthogonal and nonorthogonal modulation schemes in UWA scenarios.

Because of the characteristics of the underwater acoustic sensor networks (UASNs), such as long propagation delay, narrow bandwidth, limited resources, and intermittent connectivity, the design of the routing protocol has always been the focus and difficulty in UASNs research. Most of the existing coding-based underwater routing protocols only use the error correction function of network coding to improve the reliability of data transmission, ignoring that the network coding can effectively optimize the performance of packet transmission. In this article, we propose a hybrid coding-aware routing protocol (HCAR). HCAR introduces interflow network coding and designs a new routing framework that combines reactive routing with opportunistic routing. It exhaustively explores potential coding opportunities in the network and proactively creates coding opportunities during the entire routing process, which enables HCAR to take full advantage of the broadcast nature of the wireless medium, thereby effectively reducing the number of transmissions. In addition, we design a new encoding method that does not rely on opportunistic listening, and thus avoids introducing excessive delay and overhead. The simulation results show that HCAR can effectively optimize the routing problem of the networks with multiple source nodes and a single sink node, further reducing the transmission overhead while ensuring reliability. We also implemented the key mechanism of the HCAR protocol to verify its feasibility through sea tests.

Physical layer security (PLS) is one of the important methods to solve the security problems of Underwater Acoustic Sensor Networks (UASNs). In this paper, combining the characteristics of cooperative communication and cooperative jamming in PLS, we propose a Secure Relay Selection Scheme based on Cooperative Jamming for UASNs. On the one hand, according to the link quality and residual energy, one or several nodes are selected to forward information as relays. On the other hand, the scheme takes advantage of long propagation delay of the underwater acoustic channel and employs two different jamming ways: Self-Protection Jamming and Assistance Jamming, both of which can interfere with eavesdroppers without affecting the reception of legal users. The proposed scheme can increase the channel capacity of the legal channel by deploying relay nodes to forward the information and reduce the channel capacity of the eavesdropping channel by Self-Protection Jamming and Assistance Jamming. Simulation and field experiment results show that the proposed scheme can improve the PLS of the network.

The accurate localization of nodes is one of the most basic tasks in underwater acoustic sensor networks (UASNs). However, in large-scale UASNs, nodes are difficult to be accurately located because GPS signals cannot be received and because underwater nodes cannot establish one-hop communication with beacons due to the limited transmission range. With the development of self-sinking beacon technology, in this article, we propose a mobile-beacon-based iterative localization (MBIL) mechanism to realize node hierarchically positioning of large-scale multihop UASNs with an aim at increasing the percentage of localized nodes and reducing the localization error in the network. The proposed mechanism first uses mobile beacon nodes to localize adjacent sensor nodes. Once the location information is obtained, the sensor node will calculate its confidence value to determine whether it is a qualified reference node. Then, the unknown nodes select three adjacent reference nodes with the highest evaluation index for localization, and the remaining unknown nodes are iteratively localized. Simulation results show that the proposed mechanism can not only achieve a higher proportion of localized nodes in a shorter time, but also effectively reduce the localization error. In addition, MBIL effectively balances the energy consumption of sensor nodes, which can prolong the network's lifetime.

Research on underwater acoustic sensor networks has become a compelling field in recent years since resources on land are being depleted. Therefore, robust and efficient routing protocols are needed to ensure the reliability of message gathering and transmitting in underwater sensor networks. In the process of biological foraging in nature, creatures such as insects can find paths while adapting to dynamic environmental conditions through group cooperation, which provides a new perspective for research on routing protocols. In this study, the authors proposed an ant colony algorithm‐based routing protocol (ACAR). In ACAR, the pheromone with a novel physical meaning and concentration changing mechanism is utilised to guide ants (path establishing packets) to the sink node. In addition, node depth information is used to decrease redundancy in the underwater environment. The routing process can be summarised in three parts: pheromone list setup, routing decision and damaged path repair. Simulation results, carried out on an underwater simulator based on NS‐2, showed that ACAR outperforms other schemes with regards to network lifetime with a relatively better delivery ratio and latency in the proposed network scenarios.

In the underwater environment exist marine mammals and underwater wireless sensor networks (UWSNs), both of which use acoustic signals for communication. However, most of existing researches only focus on the performance, such as energy efficiency, packet error rates and propagation delay, of acoustic communication in UWSNs, which seems not friendly to marine mammals. With the purpose of sharing the scarce acoustic spectrum with marine mammals in a friendly way, an environment-friendly spectrum decision strategy is proposed in this paper. The strategy features a localization scheme for marine mammals and an environment-friendly channel allocation scheme. Based on the estimated locations of marine mammals, the transmission power and available channels are jointly allocated to the contending users. In this way, the proposed strategy can maximize the network capacity while avoiding interference with marine mammals. Simulation results show that the localization scheme has a success rate of localization around 90% and a relatively small average localization error at about 10m; the environment-friendly channel allocation scheme can work effectively as the locations of marine mammals change.