Underwater Acoustic Sensor Networks Research Articles

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.

An Underwater Source Location Privacy Protection Scheme Based on Game Theory in a Multi-Attacker Cooperation Scenario.

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.

An ameliorated localization algorithm for compensating stratification effect based on improved underwater salp swarm optimization technique

SummaryThe underwater acoustic sensor network is a fundamental source for ocean exploration. The potential applications of underwater acoustic sensor network (UASN) include seismic imaging, disaster prevention, mine reconnaissance, pollution monitoring, exploration of natural resources, and military surveillance. To acquire accurate results, implementing all applications of underwater sensor networks requires an adequate network connection and communication technology. The precise placement of underwater sensor nodes needs to be identified in order to achieve effective communication. To accomplish the requirement, the paper presents an efficient localization algorithm to compensate the stratification effect based on an improved underwater salp swarm optimization technique (LAS‐IUSSOT). To compute the location of sensor nodes with high accuracy, the nodes are initially randomly deployed in three‐dimensional underwater acoustic sensor network (3D‐UASN). After that, the hybridization of centroid‐based localization and the ray theory technique is used, and then, the degree of coplanarity is analyzed among the underwater sensor nodes. Then, the computation of the location of unknown nodes is performed using improved underwater salp swarm optimization technique (IUSSOT) to obtain the optimized location and compensate the impact of the stratification. The comparison of the simulation results of the existing algorithm and the proposed algorithm is performed. The LAS‐IUSSOT achieves 40.46% and 28.00% accuracy in terms of localization of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. The LAS‐IUSSOT achieves 49.39% and 62.57% accuracy in terms of ranging of underwater sensor nodes for both the sparse and dense regions in 3D‐UASN. Simulation results illustrate that the proposed algorithm outperforms the existing algorithm in terms of localization and ranging accuracy in both sparse and dense regions in 3D‐UASN, root mean square error (RMSE), normalized localization error (NLE), computation time, and convergence rate.

End-to-end multi-sensor fusion method based on deep reinforcement learning in UASNs

Underwater acoustic sensor networks (UASNs) have attracted considerable attention and are extensively employed for tracking underwater targets. However, due to the complex dynamics of underwater tracking environments, establishing accurate correlations between sensors remains elusive and challenging to precisely model. Traditional multi-sensor fusion methods for handling unknown correlations often face issues related to dependence on prior knowledge and a lack of flexibility. In response, we propose a novel end-to-end multi-sensor fusion algorithm rooted in deep reinforcement learning (DRL). To address the challenge of unknown correlations among sensors in UASNs, we formulate the multi-sensor fusion strategy as a Markov Decision Process (MDP) within the framework of reinforcement learning. The Proximal Policy Optimization (PPO) method is implemented to tackle this MDP without scalability limitations, aiming to derive an optimal sensor scheduling policy for UASNs. To enhance practical applicability, we adopt a mock data approach for algorithm training, eliminating the necessity for ground truth information on non-cooperative targets. Finally, both simulation and real-world experimental results demonstrate the superior performance of the proposed algorithm. It attains an improvement of at least 15% in tracking accuracy and exhibits a notable enhancement in stability when contrasted with traditional algorithms.

Distributed Target Tracking With Fading Channels Over Underwater Acoustic Sensor Networks

Distributed Target Tracking With Fading Channels Over Underwater Acoustic Sensor Networks

Statement of Removal

A randomly fluctuated vortex-induced vibration (VIV) affects the localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) to cause an offset time and time synchronization error which disturbs the localization of the non-cooperative moving target. Definition 2.1, theorem 2.2 has been introduced in the present work to describe the genesis and consequences of the VIV effect in UAWSN. An elliptic localization introduces the direct and indirect path measurements of the moving target. The standard CRLB parameter quantifies the VIV effect. The compensation of vortex-induced vibrations (CVV) algorithm is proposed to quantify the degradation of localization performance and to estimate the position of the moving target. The theoretical measurements of position error, velocity error with respect to the variation of noise, number of receiver sensors, and probability of line of sight (LOS) errors are compared to the standard methods. The proposed CVV algorithm improves the localization performance by 32.24% and reduces 34.36% of position errors, and 37.46% of velocity errors up to 600 meters. It can be useful for the sub-aquatic internet of underwater things.

Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs

An increasing number of scholars are researching underwater acoustic sensor networks (UASNs), including the physical layer, the protocols of the routing layer, the MAC layer, and the cross-layer. In UASNs, the ultimate goal is to transmit data from the seabed to the surface, and a well-performed routing protocol can effectively achieve this goal. However, the nodes in the network are prone to drift, and the topology is easily changed because of the movement caused by ocean currents, resulting in a routing void. The data cannot be effectively aggregated to the sink terminal on the surface. Thus, it is extremely important to determine how to find an alternative node as a relay node after node drift and how to rebuild a reliable transmission path. Although many relay routing protocols have been proposed to avoid routing voids, few of them consider the relay node selection between the outage probability and the ocean current model. Therefore, we propose an ocean current motion model based routing (OCMR) protocol to avoid the routing void in UASNs. We predicted underwater node movement based on the ocean current motion model and designed a protection radius to construct a limited search coverage based on the optimal outage probability; then, the node with the best fitness value within the protection radius was selected as the alternative relay node using an improved WOA. In OCMR, the problem of the routing void caused by ocean current motion is effectively suppressed. The simulation results show that, compared with VBF, HH-VBF, and QELAR, the proposed OCMR platform performs well in terms of the PDR (packet delivery ratio), average end-to-end delay, and average energy consumption.

Longevity-Oriented and Reliable Forwarding Percolation Routing in Underwater Acoustic Sensor Networks

Longevity-Oriented and Reliable Forwarding Percolation Routing in Underwater Acoustic Sensor Networks

Performance Analysis of Underwater Acoustic Sensor Networks With Buffer Constraint

Performance Analysis of Underwater Acoustic Sensor Networks With Buffer Constraint

Asynchronous Localization for Underwater Acoustic Sensor Networks: A Continuous Control Deep Reinforcement Learning Approach

Asynchronous Localization for Underwater Acoustic Sensor Networks: A Continuous Control Deep Reinforcement Learning Approach

Relay selection in Underwater Acoustic Sensor Networks for QoS-based cooperative communication using game theory

Underwater Acoustic Sensor Networks (UASN) plays a crucial role in monitoring and transmitting environmental information for marine resource exploration. However, underwater communication faces significant challenges such as signal attenuation, noise interference, and unstable underwater environments. Moreover, with the deployment of a large number of nodes, inefficient resource allocation strategies in cooperative communication lead to strained communication resources as well as increased energy consumption. This paper introduces a novel relay selection approach based on game theory that takes into account both competition and cooperation among nodes. By building a potential game model and a utility function, we derive the optimal relay selection strategy that can enhance system Quality of Service (QoS). We also define conditions for the feasibility of pure strategy Nash Equilibria (NE) and develop a low-complexity QoS-based Relay Selection Iterative Algorithm (QoS-RSIA) to procure feasible pure strategy NE for the designed game. Through comparative analysis with other relay selection algorithms, simulation results show that the proposed method not only enhances system throughput and average spectral efficiency by at least 8.3%, but also significantly reduces time complexity compared to exhaustive search algorithms.

Joint Network Lifetime Maximization and Relay Selection Design in Underwater Acoustic Sensor Networks

Joint Network Lifetime Maximization and Relay Selection Design in Underwater Acoustic Sensor Networks

Modeling Cascading Failures and Invulnerability Analysis of Underwater Acoustic Sensor Networks Based on Complex Network

Modeling Cascading Failures and Invulnerability Analysis of Underwater Acoustic Sensor Networks Based on Complex Network

An Energy Efficient Clustering Routing Protocol Based on Arithmetic Progression for Underwater Acoustic Sensor Networks

An Energy Efficient Clustering Routing Protocol Based on Arithmetic Progression for Underwater Acoustic Sensor Networks

Continuous Objects Tracking Based on Geometric Centroid of Feasible Region in USV-Assisted Underwater Acoustic Sensor Networks

Continuous Objects Tracking Based on Geometric Centroid of Feasible Region in USV-Assisted Underwater Acoustic Sensor Networks

Multi-hop relay selection for underwater acoustic sensor networks: A dynamic combinatorial multi-armed bandit learning approach

An efficient multi-hop relay selection method is the key of multi-hop relay technology to improve wireless communication reliability. Accordingly, this paper devotes a multi-hop relay selection problem for unknown time-varying underwater acoustic sensor networks, and a dynamic combinatorial multi-armed bandit (DCMAB) learning structure is proposed to achieve the multi-hop relay strategy with minimum propagation delay without any prior channel information. Compared with the strategy learning space of the single relay selection problem for static networks, the multi-hop relay learning space shows high-dimensional and dynamic characteristics. To cope with the high-dimensional characteristic of multi-hop relay strategy spaces, DCMAB develops a combinatorial bandit learning manner. It enables the player to learn the high-dimensional multi-hop relay strategy space by exploring the low-dimensional link sub-strategy space, thereby reducing the learning complexity. To cope with the dynamic characteristic of multi-hop relay strategy spaces, DCMAB makes newly-formed links able to employ the historical learning information of experienced links to reason their prior knowledge. Meanwhile, by adopting a probabilistic compensation manner, DCMAB intensifies the exploration for newly-formed links. It successfully overcomes learning inefficiency caused by the lack of learning information on newly-formed links. Besides, an energy-aware-based filtering mechanism is proposed to filter out potential long-delay relay links. It enables the player to focus on exploring and reasoning high-quality links, thereby enhancing the quick search ability of superior multi-hop relay strategies. Finally, the superiority of the proposed algorithm is demonstrated by extensive simulation results.

MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks

<p>In recent years, with the rapid development of science and technology, many new technologies have made people’s exploration of the ocean deeper and deeper, and due to the requirements of national defense and marine development, the underwater acoustic sensor network (UASN) has been paid more and more attention. Nevertheless, the underwater acoustic channel has the properties of considerable propagation delay, limited bandwidth, and unstable network topology. In order to improve the performance of the medium access control (MAC) protocol in UASN, we propose a new MAC protocol based on the Slotted-FAMA of Multiple Reception (MR-SFAMA) protocol. The protocol uses the Q-Learning algorithm to optimize the multi-receiver handshake mechanism. The current state is judged according to the received node request, and the Q-table is established. Through the multi-round interaction between the node and the environment, the Q-table is continuously updated to obtain the optimal strategy and determine the optimal data transmission scheduling scheme. The reward function is set according to the total back-off time and frame error rate, which can reduce the packet loss rate during network data transmission while reducing the delay. In addition, the matching asynchronous operation and uniform random back-off algorithm are used to solve the problem of long channel idle time and low channel utilization. This new protocol can be well applied to unstable network topology. The simulation results show that the protocol performs better than Slotted-FAMA and MR-SFAMA regarding delay and normalized throughput.</p> <p> </p>