Underwater Acoustic Sensor Networks Research Articles

A Fair MAC Protocol Based on Dual Bandwidth Allocation Iterations in Underwater Acoustic Sensor Networks

ABSTRACTMost existing passive allocation‐based media access control (MAC) protocols for underwater acoustic sensor networks (UASNs) overlook the fairness issue in channel bandwidth allocation, resulting in certain nodes occupying channel resources for extended periods, thereby leading to low channel utilization. To address this issue, this paper proposes a fair MAC protocol based on dual bandwidth allocation iterations (FBA‐MAC). Firstly, by limiting the maximum number of packets and the maximum number of available time slots that the host node can receive in each communication cycle, the protocol reduces collisions and collisions more effectively, thus controlling the communication delay. Secondly, in the process of channel bandwidth allocation, a dual bandwidth allocation iterative method was used to allocate appropriate channel bandwidth for each member node according to its communication requirements and the generation time of its generated data packets. Finally, in order to minimize the time interval of each packet arriving at the host node to reduce the communication delay, the transmission scheduling scheme was adaptively adjusted by combining the results of channel bandwidth allocation to maximize the channel utilization. Simulation results indicate that compared to the other four MAC protocols, FBA‐MAC demonstrates significant advantages in terms of fairness (10% higher), achieving higher network throughput (10% higher) and shorter end‐to‐end delay. Therefore, FBA‐MAC exhibits superior adaptability and efficiency in dynamic network environments.

Optimisation of underwater acoustic sensor networks

The oceans are the scene of intense economical and industrial activities. It is becoming even more competitive with recent military activities and the upcoming development of marine renewable energies. All of these activities have a significant impact on marine ecosystems and must be carefully evaluated for any maritime planning. The marine environment is also a very effective medium for the propagation of acoustic waves over long distances, depending on the frequency, the source level, the static (bathymetry) and the dynamic (hydrology) oceanographic conditions. Such conditions make acoustic sensors primary candidates for underwater investigations and the distribution of mixed sensors with various sensitivities, frequency bandwidths or positioning can be optimized for detection, localization or monitoring purposes. This contribution aims to present a methodology based on evolutionary algorithms which considers the overall physical state of the marine environment and determines the optimal configurations of mixed underwater acoustic networks. The relevance of various objectives, or metrics, can be discussed, as well as specific search operators for crossover and mutation based on preliminary results.

ADRP-DQL: An adaptive distributed routing protocol for underwater acoustic sensor networks using deep Q-learning

Underwater Wireless Sensor Networks (UWSNs) face unique constraints due to their unstructured and dynamic underwater environment. Data gathering from these networks is crucial as energy resources are limited. In this regard, efficient routing protocols are needed to optimize energy consumption, increase the network lifetime, and enhance data delivery in these networks. In this work, we develop an Adaptive Distributed Routing Protocol for UWSNs using Deep Q-Learning (ADRP-DQL). This protocol employs the ability of reinforcement learning to dynamically learn the best routing decisions based on the network’s state and action-value estimates. It allows nodes to make intelligent routing decisions, considering residual energy, depth and node degree. A Deep Q-Network (DQN) is employed as the function approximator to estimate action values and choose the optimal routing decisions. The DQN is trained using off-policy and on-policy strategies and the neural network model. Simulation results demonstrate that ADRP-DQL performs well regarding energy efficiency (EE), data delivery ratio, and network lifetime. The results highlight the proposed protocol’s effectiveness and adaptability to UWSNs. The ADRP-DQL protocol contributes to intelligent routing for UWSNs, offering a promising approach to enhance performance and optimize energy utilization in these demanding environments.

Asynchronous localization of dynamic node through underwater acoustic sensor networks

In most cases, the operation of underwater acoustic sensor networks (UASNs) relies on accurate sensor location information. However, UASNs present more challenges than terrestrial sensor networks, such as the propagation speed variation with depth (i.e., stratification effect), asynchronous clock, node mobility, etc. In this paper, an efficient method of asynchronous localization is proposed by taking into account the stratification effect and node mobility. Firstly, a network is constructed that consists of surface buoys, AUVs, and target sensors. Secondly, an iterative least squares (LS) method is developed to locate AUV by establishing the relationship between propagation delay and location estimation. Thirdly, the passive mobility velocity of AUV is used to develop and solve the mobility model of the target sensors. Then, with the support of AUV, an asynchronous localization method is developed to estimate the target position, which improves the localization accuracy through motion and stratification compensation strategies. Moreover, the proposed method is validated through Cramer Rao lower bound (CRLB) analysis. Finally, simulation is performed to show that the proposed method is more efficient in reducing the estimation errors.

A Novel Medium Access Policy Based on Reinforcement Learning in Energy-Harvesting Underwater Sensor Networks.

Underwater acoustic sensor networks (UASNs) are fundamental assets to enable discovery and utilization of sub-sea environments and have attracted both academia and industry to execute long-term underwater missions. Given the heightened significance of battery dependency in underwater wireless sensor networks, our objective is to maximize the amount of harvested energy underwater by adopting the TDMA time slot scheduling approach to prolong the operational lifetime of the sensors. In this study, we considered the spatial uncertainty of underwater ambient resources to improve the utilization of available energy and examine a stochastic model for piezoelectric energy harvesting. Considering a realistic channel and environment condition, a novel multi-agent reinforcement learning algorithm is proposed. Nodes observe and learn from their choice of transmission slots based on the available energy in the underwater medium and autonomously adapt their communication slots to their energy harvesting conditions instead of relying on the cluster head. In the numerical results, we present the impact of piezoelectric energy harvesting and harvesting awareness on three lifetime metrics. We observe that energy harvesting contributes to 4% improvement in first node dead (FND), 14% improvement in half node dead (HND), and 22% improvement in last node dead (LND). Additionally, the harvesting-aware TDMA-RL method further increases HND by 17% and LND by 38%. Our results show that the proposed method improves in-cluster communication time interval utilization and outperforms traditional time slot allocation methods in terms of throughput and energy harvesting efficiency.

An Energy Efficient Transmission Mechanism based on Optimal Relay Distance for Underwater Acoustic Sensor Networks

Abstract Due to the limited resources and energy of communication in underwater acoustic sensor network (UASN), there are many challenges in designing energy-efficient and reliable media access control (MAC) protocols for UASN. This paper proposes an energy-efficient media access control (MAC) protocol with dynamic transmission range control (TRC), referred to as TRA-MAC. The protocol provides an adaptive, multi-hop and energy-saving solution for underwater sensor network communication. The protocol dynamically adjusts the transmission power of the node according to the distance between the source node and the destination node, and has the ability to adapt to network conditions. Simulation results show that compared with Slotted Aloha, the proposed adaptive relay node selection mechanism increases the energy efficiency by 43.07%, the network throughput by 46.6%, and the end-to-end delay is greatly reduced. Compared with Slotted FAMA, it has more significant advantages.

MRNQ: Machine learning-based reliable node quester for reliable communication in underwater acoustic sensor networks

MRNQ: Machine learning-based reliable node quester for reliable communication in underwater acoustic sensor networks

SFDM: A time-synchronization-free detection mechanism for mobile underwater acoustic sensor networks

Detection and localization are important applications of underwater acoustic sensor networks, and the use of passive listening arrays composed of multiple underwater unmanned platforms to achieve passive detection and localization of targets has been a hot research topic in recent years. However, most schemes based on mobile listening arrays only aim to improve localization accuracy, with less consideration of the high communication and time overhead of formation maintenance and the high cost of time synchronization among nodes. To reduce the cost while ensuring localization accuracy, this paper proposes a time-synchronization-free detection mechanism for mobile underwater acoustic sensor arrays. Initially, the nodes cruise underwater until they passively detect a target signal. Subsequently, a pseudo-synchronization and rough self-calibration process is triggered among the nodes, which relies on the communication among the nodes. The communication stage is based on a time-synchronization-free detection protocol. An improved sound speed stratification effect compensation algorithm and an unscented particle filter algorithm are proposed to improve the calibration accuracy of the array. Finally, the Time Difference of Arrival method is utilized to locate the target position. Simulation and sea trial results confirm the feasibility and accuracy of the proposed mechanism. In addition, the mechanism is effective in reducing hardware costs.

Hybrid Trust Model for Identifying Malicious Attacks in Underwater Acoustic Sensor Network

Hybrid Trust Model for Identifying Malicious Attacks in Underwater Acoustic Sensor Network

SSH-MAC: Service-Aware and Scheduling-Based Media Access Control Protocol in Underwater Acoustic Sensor Network

In the framework of the space-air-ground-ocean integrated network, the underwater acoustic sensor network (UASN) plays a pivotal role. The design of media access control (MAC) protocols is essential for the UASN to ensure efficient and reliable data transmission. From the perspective of differentiated services in the UASN, a service-aware and scheduling-based hybrid MAC protocol, named the SSH-MAC protocol, is proposed in this paper. In the SSH-MAC protocol, the centralized scheduling strategy is adopted by sensor nodes with environmental monitoring service, and the distributed scheduling strategy is adopted by sensor nodes with target detection service. Considering the time-varying data generation rate of sensor nodes, the sink node will switch the scheduling mode of sensor nodes based on the specific control packet and adjust the resource allocation ratio between centralized scheduling and distributed scheduling. Simulation results show that the performance of the SSH-MAC protocol, in terms of utilization, end-to-end delay, packet delivery ratio, energy consumption, and payload efficiency, is good.

Energy-Efficient and Reliable Deployment Models for Hybrid Underwater Acoustic Sensor Networks with a Mobile Gateway

Energy-Efficient and Reliable Deployment Models for Hybrid Underwater Acoustic Sensor Networks with a Mobile Gateway

A reliable cluster-based opportunistic routing protocol for underwater wireless sensor networks

Underwater Acoustic Sensor Networks (UASNs) have gained significant popularity and application in various marine engineering exploration scenarios, driven by the global evolution of the ocean ecosystem. However, UASNs face numerous challenges arising from the unique characteristics of the underwater environment and acoustic channels, such as limited energy resources, unreliable communication, and dynamic network topology in uncertain environments, all of which impact the network lifetime and transmission reliability. To address these challenges and enable efficient underwater data packet transmissions, this paper proposes a reliable cluster-based routing method called RCRP. The RCRP organizes sensor nodes into clusters, where cluster heads transmit the fused data to the sink node through multi-hop forwarding. To overcome routing voids caused by node failures during packet transmission, a prediction model based on the Markov chain is introduced to identify void nodes and inform neighboring nodes about the routing holes. Furthermore, a connection prediction mechanism is developed to tackle changes in the uncertain network topology, taking into account node mobility and the received SNR. For packet transmissions, a waiting mechanism inspired by the opportunistic routing (OR) paradigm is proposed, which determines the optimal forwarding route based on the calculated probability of successful connection between nodes. Simulation results demonstrate that RCRP outperforms existing routing protocols in terms of packet delivery ratio, surviving time, and energy consumption, highlighting its effectiveness in enhancing the performance of UASNs.

GNN-IR: An Intelligent Routing Method Based on Graph Neural Network in the Underwater Acoustic Sensor Network

GNN-IR: An Intelligent Routing Method Based on Graph Neural Network in the Underwater Acoustic Sensor Network

Node Load and Location-Based Clustering Protocol for Underwater Acoustic Sensor Networks

Clustering protocols for underwater acoustic sensor networks (UASNs) have gained widespread attention due to their importance in reducing network complexity. Congestion occurs when the intra-cluster load is greater than the upper limit of the intra-cluster information transmission capacity, which leads to a dramatic deterioration of network performance despite the reduction of network complexity. To avoid congestion, we propose a node load and location-based clustering protocol for UASNs (LLCP). First, a node load and location-based optimization mechanism is proposed. The number of cluster members is optimized based on node load and location to maximize the number of cluster members while avoiding congestion. Then, a node degree and location-based cluster member selection mechanism is proposed to select the optimal cluster members. Finally, a priority-based clustering mechanism is proposed. The node clustering order is adjusted based on the clustering priority to maximize the reduction of network complexity by increasing the average number of cluster members. Simulation results show that our proposed LLCP minimizes the network complexity while avoiding congestion.

A dynamic routing scheme for underwater acoustic sensor networks in submarine disaster applications

IntroductionThe ocean economy serves as a critical engine for the development of human society and economy, and its stable growth is of utmost importance. However, the frequent and unexpected occurrence of natural disasters, such as submarine disasters, poses significant threats to human society, especially disasters related to wave phenomena like the Ekman current, which has particularly prominent impacts.MethodsThe study proposes a dynamic routing scheme for underwater acoustic sensor networks. It establishes a cone-shaped distributed network, utilizing autonomous underwater vehicles to collect crucial data from nodes deployed on the seabed and transmitting it through the underwater cone-shaped distributed sensor network. Additionally, it adopts source location protection (SLP) technology to ensure the privacy of source locations. To validate the practicality and stability of this scheme, simulation experiments targeting the Ekman current were conducted in MATLAB.ResultsThe experimental results demonstrate that the cone–SLP network significantly reduces the frequency of routing information exchange and energy consumption among acoustic sensors, effectively enhancing the economy and durability of the sensor network. Furthermore, it exhibits robust practicality and high stability in complex and variable submarine environments.DiscussionThis research not only provides a practical method for effectively monitoring submarine disasters but also offers valuable experience and significant reference value for other similar projects, playing a more crucial role in the sustainable development of the ocean economy.

An active jamming-based helper deployment scheme for underwater acoustic sensor networks

Underwater acoustic sensor networks (UASNs) are usually deployed in unattended, opaque and even hostile environments; thus, they may face serious threats. In the last few years, physical layer security (PLS) has emerged as a new technique that can improve the security performance of networks. In this paper, we research the physical layer security scheme of UASNs by actively jamming against eavesdropping attacks. Utilizing the large propagation delay of the underwater acoustic (UWA) signal, the jamming signals can interfere with legitimate signals to prevent eavesdropping. Therefore, we propose an active jamming-based helper deployment scheme (AJHDS) for UASNs, which deploys the helpers to the target water area and realizes the secure transmission of the network. Both the simulation and field test results show that the scheme can significantly reduce the interception capability of eavesdroppers. Furthermore, the field sea experiment evaluates that the location of helpers affects the eavesdropper’s acquisition of legitimate data packets.

An Efficient Framework for Localization Based Optimized Opportunistic Routing Protocol in Underwater Acoustic Sensor Networks

An Efficient Framework for Localization Based Optimized Opportunistic Routing Protocol in Underwater Acoustic Sensor Networks

A Federated Deep Reinforcement Learning-Based Trust Model in Underwater Acoustic Sensor Networks

A Federated Deep Reinforcement Learning-Based Trust Model in Underwater Acoustic Sensor Networks

Achieving Fair-Effective Communications and Robustness in Underwater Acoustic Sensor Networks: A Semi-Cooperative Approach

Achieving Fair-Effective Communications and Robustness in Underwater Acoustic Sensor Networks: A Semi-Cooperative Approach

An Adaptive Trust Evaluation Model for Detecting Abnormal Nodes in Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks have a wide range of applications in both civil and military fields, but the complex and changing underwater environment makes them vulnerable to multiple security threats. Trust mechanisms are effective ways to enhance network security and reliability. In order to improve the accuracy of trust evaluation and the detection rate of abnormal nodes, this paper proposes an adaptive trust evaluation model based on fuzzy logic. This model adopts a variable weight fuzzy comprehensive evaluation algorithm to dynamically adjust the weights of three direct trust indicators to ensure the accuracy of direct trust evaluation. Then, it uses fuzzy closeness to eliminate unreliable recommendation trust and adjusts the weight of recommendation trust through deviation to improve the accuracy of indirect trust. The simulation results show that the model can effectively improve the accuracy of trust evaluation and the detection rate of abnormal nodes. Especially when the link quality is unstable, the success rate of detecting abnormal nodes in this model is improved by more than 10% compared with the existing trust model.