Underwater Acoustic Networks Research Articles

Robustness of vortex wave based communications to operational variables

Orbital angular momentum (OAM) based acoustic communications are a promising method of increasing bandwidth in underwater acoustic communications networks as their unique phase patterns form an orthogonal basis set on which communications protocols may be based. The underwater acoustic environment, however, is highly complex and dynamic. These complexities make developing systems capable of long-range communications challenging. Methods for using BELLHOP’s ray tracing software to simulate the time series of vortex-wave based communications signals are presented. Additionally, this study explores the robustness of the inner product deconvolution method of decoding OAM-based communications against various operating conditions, considering both environmental and platform variations. The effects on channel cross-talk are assessed against sound speed profile uncertainty, position errors, Doppler Effect, and turbulence in the water column. These analyses shed light on the operational implementation of OAM-based communications systems.

Multi-Agent Transfer Reinforcement Learning for Resource Management in Underwater Acoustic Communication Networks

Multi-Agent Transfer Reinforcement Learning for Resource Management in Underwater Acoustic Communication Networks

Anti-Eavesdropping by Exploiting the Space–Time Coupling in UANs

Due to the space–time coupling access, we find that anti-eavesdropping opportunities exist in underwater acoustic networks (UANs), where packets can be successfully received only by the intended receiver, but collide at the unintended receivers. These opportunities are highly spatially dependent, and this paper studies the case that linearly deployed sensor nodes directly report data toward a single collector. We found an eavesdropping ring centered around these linearly deployed sensor nodes, where the eavesdropper could steal all the reported data. Since the typical receiving-alignment-based scheduling MAC (TRAS-MAC) will expose the relative spatial information among the sensor nodes with the collector, the eavesdropper can locate the eavesdropping ring. Although moving the collector into the one-dimensional sensor node chain can degrade the eavesdropping ring to a point that constrains the eavesdropping risk, the collector’s location will be subsequently exposed to the eavesdropper. To efficiently protect the reported data and prevent the exposure of the collector’s location, we designed a slotted- and receiving-alignment-based scheduling MAC (SRAS-MAC). The NS-3 simulation results showed the effectiveness of the SRAS-MAC and the TRAS-MAC in protecting data from eavesdropping, which protect 90% of the data from eavesdropping in the one-eavesdropper case and up to 80% of data from eavesdropping in ten-eavesdropper cases. Moreover, unlike TRAS-MAC, which will expose the collector’s location, SRAS-MAC provides multiple positions for the collector to hide, and the eavesdropper cannot distinguish where it is.

Power Allocation Based on Multi-Agent Deep Deterministic Policy Gradient for Underwater Acoustic Communication Networks

This paper proposes a reinforcement learning-based power allocation for underwater acoustic communication networks (UACNs). The objective function is formulated as maximizing channel capacity under constraints of maximum power and minimum channel capacity. To solve this problem, a multi-agent deep deterministic policy gradient (MADDPG) approach is introduced, where each transmitter node is considered as an agent. Given the definition of a Markov decision process (MDP) model for this problem, the agents learn to collaboratively maximize the channel capacity by deep deterministic policy gradient (DDPG) learning. Specifically, the power allocation of each agent is obtained by a centralized training and distributed execution (CTDE) method. Simulation results show the sum rate achieved by the proposed algorithm approximates that of the fractional programming (FP) algorithm and improves by at least 5% compared with the DQN (deep Q-learning network) -based power allocation algorithm.

3D Dynamic Topology With Energy-Aware Forwarding in Underwater Acoustic Networks (MAEARS)

3D Dynamic Topology With Energy-Aware Forwarding in Underwater Acoustic Networks (MAEARS)

Relay Selection and Power Control for Mobile Underwater Acoustic Communication Networks: A Dual-Thread Reinforcement Learning Approach

Relay Selection and Power Control for Mobile Underwater Acoustic Communication Networks: A Dual-Thread Reinforcement Learning Approach

Energy-balanced Routing Protocol with Nonuniform Clustering for Underwater Acoustic Sensors Networks

Energy-balanced Routing Protocol with Nonuniform Clustering for Underwater Acoustic Sensors Networks

FCFS: A Dual-Channel and Reservation-Based MAC Protocol for Underwater Acoustic Networks

FCFS: A Dual-Channel and Reservation-Based MAC Protocol for Underwater Acoustic Networks

AS-MAC: An Adaptive Scheduling MAC Protocol for Reducing the End-to-End Delay in AUV-Assisted Underwater Acoustic Networks

AS-MAC: An Adaptive Scheduling MAC Protocol for Reducing the End-to-End Delay in AUV-Assisted Underwater Acoustic Networks

Source-Based Disjoint Multi-Path Routing Protocol for Underwater Acoustic Networks: Protocol Design and Sea Trials

Source-Based Disjoint Multi-Path Routing Protocol for Underwater Acoustic Networks: Protocol Design and Sea Trials

CPR: A Confined Percolation Routing for Distributed Underwater Acoustic Networks

CPR: A Confined Percolation Routing for Distributed Underwater Acoustic Networks

Optimized deep learning driven signal detection and adaptive channel estimation in underwater acoustic IoT networks

SummaryNowadays, multiple input multiple outputs with orthogonal frequency division multiplexing (MIMO‐OFDM) provide better communication performance that can be applied to the fast‐growing Internet of Things (IoTs). In underwater IoT, information fades away rapidly due to varying water conditions. Therefore, the MIMO model can be applied to the OFDM acoustic system, enabling high‐speed data transmission without affecting the channel effectively. However, detecting the underwater signal and estimating the channel is highly necessary for enhancing underwater acoustic communication (UWAC). Recently, many techniques have been introduced for effectively performing signal detection and channel estimation. However, those techniques face high time complexity due to increased channel interferences and noises during data transmission. Hence, this article brings a novel technique for SD and CE for the UWAC‐IoT‐enabled MIMO‐OFDM system. An adaptive recursive least square (ARLS) technique is proposed in this study for CE that aids in evaluating the acoustic channel parameters effectively. For performing SD, a bi‐directional deep pelican convolutional neural network (BDPCNN) technique is introduced to ensure the presence and absence of signals at the receiver end. The proposed method is analyzed via the MATLAB platform, and the performances are analyzed under different water types like turbid water, coastal water, clear ocean water, and pure seawater. Different performance metrics like bit error rate (BER), mean square error (MSE), energy efficiency (EE), and time complexity are analyzed with different existing techniques. The experimental section obtains the BER of 0.0086, 0.013, 0.017, and 0.021 for turbid, coastal, clear, and pure seawater, respectively.

Exploiting Propagation Delay in Underwater Acoustic Communication Networks via Deep Reinforcement Learning.

This article proposes a novel deep-reinforcement learning-based medium access control (DL-MAC) protocol for underwater acoustic networks (UANs) where one agent node employing the proposed DL-MAC protocol coexists with other nodes employing traditional protocols, such as time division multiple access (TDMA) or q -Aloha. The DL-MAC agent learns to exploit the large propagation delays inherent in underwater acoustic communications to improve system throughput by either a synchronous or an asynchronous transmission mode. In the sync-DL-MAC protocol, the agent action space is transmission or no transmission, while in the async-DL-MAC, the agent can also vary the start time in each transmission time slot to further exploit the spatiotemporal uncertainty of the UANs. The deep Q -learning algorithm is applied to both sync-DL-MAC and async-DL-MAC agents to learn the optimal policies. A theoretical analysis and computer simulations demonstrate the performance gain obtained by both DL-MAC protocols. The async-DL-MAC protocol outperforms the sync-DL-MAC protocol significantly in sum throughput and packet success rate by adjusting the transmission start time and reducing the length of time slot.

Trust-Aware and Fuzzy Logic-Based Reliable Layering Routing Protocol for Underwater Acoustic Networks

Routing protocols based on trust mechanisms have been widely investigated for wireless sensor networks, and the works have achieved good results, while there are few works on trusted routing for underwater acoustic networks (UANs). However, trust-aware routing is the key to improving the packet delivery rate and the energy efficiency of UANs. Therefore, inspired by the theory of trust evaluation, a trust-aware and fuzzy logic-based reliable layering routing protocol (TAFLRLR) is proposed. In the TAFLRLR protocol, to avoid the problem of the void area and improve the transmission reliability, the candidate nodes of the next-hop forwarding nodes are determined according to the layers of neighbor nodes. Moreover, a fuzzy logic-based trust evaluation mechanism (FLTEM) is provided, which employs the fuzzy comprehensive evaluation decision model to calculate the comprehensive trust value for underwater sensor nodes. Further, the node density of a candidate node and its comprehensive trust value are taken as the input of a fuzzy control system and the forwarding probability (FP) of the node is taken as the output, and the candidate node with the highest FP is selected as the best forwarding node. Simulation results illustrate the superiority and effectiveness of the TAFLRLR protocol in terms of energy efficiency, routing reliability, and transmission reliability.

Physical Layer Authentication Without Adversary Training Data in Resource-Constrained Underwater Acoustic Networks

Physical Layer Authentication Without Adversary Training Data in Resource-Constrained Underwater Acoustic Networks

Downlink Non-Orthogonal multiple access underwater acoustic communication receiver design Deep neural network

Downlink non-orthogonal multiple access (NOMA) is necessary to handle the underwater acoustic channel's limitations on bandwidth. NOMA downlink underwater acoustic (UWA) communication transmits data symbols from a source station to many users. Superimposed coding with variable power levels allows successive interference cancelation (SIC) receivers to decode data. However, SIC receivers require knowledge of channel conditions and channel state information (CSI). This is difficult to acquire, particularly in UWA communication. To address this problem, this paper proposes downlink underwater acoustic using 1D Convolution neural network (CNN). Two users with different power levels and distances from the transmitter employ BPSK modulation to support multiuser communication. Users far from the base station receive the most power. The base station uses superimposed coding. BELLHOP algorithm generates the training dataset with user depth modifications. For training the model, a composite signal passes through the samples of the UWA channel and is fed to the model along with labels. DNN receiver learns the characteristic of the UWA channel and does not depend on CSI. The testing CIR is used to evaluate the trained model. The results are compared to the traditional SIC receiver. The DNN-based DL NOMA underwater acoustic receiver outperformed the SIC receiver in simulations.

Multi-function underwater acoustic networks combining communication, navigation, and sonar

Autonomous underwater vehicles often have on-board acoustic modems and navigation systems that work in connection with multiple vehicles. These capabilities may be combined or enhanced with using multifunction transducers that transmit communication and navigation information. The use of multimode transducers to produce acoustic spiral waves for communication, navigation and sonar are presented.

SPACNet: A Simulation Platform of an Acoustic Cognitive Network

Originating from the concept of cognitive networks (CNs), which are becoming popular in wireless terrestrial communication scenarios, underwater acoustic cognitive networks (UACNs) are drawing more and more attention in the field of the Internet of Underwater Things (IoUT). However, as the implementation of cognitive mechanisms in underwater acoustic networks is different from that of wireless scenarios, it is impossible or difficult for traditional simulation platforms to carry out simulations of UACNs. There is a lack of specialized simulation tools in terms of UACNs. To enable the quantitative evaluation of the effectiveness and performance enhancement of a UACNs in an adverse underwater environment, a simulation platform of acoustic cognitive networks (SPACNet) was designed and investigated in this article. First, based on a state machine-based protocol programming framework, the SPACNet is capable of supporting the implementation of different state-transform types associated with cognitive networking protocols. Moreover, to facilitate the realization of cognitive function at comprehensive levels of signal, information, and link, an underwater acoustic channel model with an environmental parameter input is integrated in SPACNet to generate underwater environment-driven multiple-aspect behaviors. Moreover, a simplified collision model consisting of an environment factor, channel response, and node location is used to reduce the complexity of the simulation of UACNs signal reception. A simulation was carried out to verify the effectiveness of SPACNet in evaluating the cognitive capabilities of UACNs. Finally, a field UACNs experiment was performed to validate the general consistency between the conclusion obtained with the SPACNet-based simulation and that from the field test.

Spectrum-Weighted Fusion Cooperative Detection Algorithm Based on Double Thresholds for Underwater Acoustic Networks.

Spectrum-sensing technology is crucial for the development of underwater acoustic communication networks and plays a key role in detecting spectrum holes and channel occupancy. Energy detection technology, as the fundamental spectrum sensing technology in cognitive radio, has reached a mature level of development. Its application in hydroacoustic communications can significantly enhance the utilization of the hydroacoustic spectrum. However, due to the complexity of the hydroacoustic channel compared with that of the radio channel, the traditional double-threshold energy detection technique faces challenges such as fixed threshold values and limited flexibility. To address this, we propose a model for the hydroacoustic channel that incorporates a weight factor based on the signal-to-noise ratio in the algorithm. This allows for adaptive threshold values based on the user's signal-to-noise environment, reducing false detection rates and improving overall detection performance. Through simulation experiments and comparisons, our proposed signal-to-noise weighted collaborative spectrum-sensing technique demonstrates superior detection performance compared with other spectrum-sensing techniques.

Underwater acoustic target recognition based on spectrum component analysis of ship radiated noise

Underwater acoustic target recognition based on ship radiated noise has always been a challenging task due to the complexity of underwater environment and the antagonism of targets. An underwater acoustic target recognition network based on Ship radiated Noise spectrum component Analysis (SNANet) is proposed in this paper by extracting the spectrum features of each component in different frequency bands, which improves the recognition accuracy as compared with existing end-to-end recognition methods. Adaptive weight based on forward weight and backward weight is used to fuse main and auxiliary features. Experiments have demonstrated that SNANet surpasses currently existing models in performance on the public dataset DeepShip.