Search Results (143)

In this paper, we propose the Adaptive Power-Controlled Energy-Efficient Depth-Based Routing (APC-EEDBR) protocol. This protocol is designed to address the challenges posed by complex environments and limited resources in underwater-sensor networks. Employing a dual-weight adjustment mechanism and adaptive power control enables the protocol to achieve energy-efficient relay selection and enhance the link stability. The protocol adopts a cluster-free, hop-by-hop communication strategy and a cross-layer design to improve path stability and forwarding efficiency while mitigating hotspot issues in data aggregation areas. The simulation results demonstrate that the APC-EEDBR protocol effectively reduces energy consumption and communication overhead by approximately 16%, and significantly prolongs the network lifetime by about 39% compared with EEDBR. Full article

Due to the dynamic and harsh underwater environment, which involves a long propagation delay, high bit error rate, and limited bandwidth, it is challenging to achieve reliable communication in underwater wireless sensor networks (UWSNs) and network support applications, like environmental monitoring and natural disaster prediction, which require energy efficiency and low latency. To tackle these challenges, we introduce AC-RL-based power control (ACRLPC), a novel hybrid MAC protocol that can efficiently integrate Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA)-based MAC and Time Division Multiple Access (TDMA) with Actor–Critic Reinforcement Learning (AC-RL). The proposed framework employs adaptive strategies, utilizing adaptive power control and intelligent access methods, which adjust to fluctuating conditions on the network. Harsh and dynamic underwater environment performance evaluations of the proposed scheme confirm a significant outperformance of ACRLPC compared to the current protocols of FDU-MAC, TCH-MAC, and UW-ALOHA-QM in all major performance measures, like energy consumption, throughput, accuracy, latency, and computational complexity. The ACRLPC is an ultra-energy-efficient protocol since it provides higher-grade power efficiency by maximizing the throughput and limiting the latency. Its overcoming of computational complexity makes it an approach that greatly relaxes the processing requirement, especially in the case of large, scalable underwater deployments. The unique hybrid architecture that is proposed effectively combines the best of both worlds, leveraging TDMA for reliable access, and the flexibility of CSMA/CA serves as a robust and holistic mechanism that meets the desired enablers of the system. Full article

The underwater domain presents unique challenges and opportunities for scientific exploration, resource extraction, and environmental monitoring. Autonomous underwater vehicles (AUVs) rely on simultaneous localization and mapping (SLAM) for real-time navigation and mapping in these complex environments. However, traditional SLAM techniques face significant obstacles, including poor visibility, dynamic lighting conditions, sensor noise, and water-induced distortions, all of which degrade the accuracy and robustness of underwater navigation systems. Recent advances in deep learning (DL) have introduced powerful solutions to overcome these challenges. DL techniques enhance underwater SLAM by improving feature extraction, image denoising, distortion correction, and sensor fusion. This survey provides a comprehensive analysis of the latest developments in DL-enhanced SLAM for underwater applications, categorizing approaches based on their methodologies, sensor dependencies, and integration with deep learning models. We critically evaluate the benefits and limitations of existing techniques, highlighting key innovations and unresolved challenges. In addition, we introduce a novel classification framework for underwater SLAM based on its integration with underwater wireless sensor networks (UWSNs). UWSNs offer a collaborative framework that enhances localization, mapping, and real-time data sharing among AUVs by leveraging acoustic communication and distributed sensing. Our proposed taxonomy provides new insights into how communication-aware SLAM methodologies can improve navigation accuracy and operational efficiency in underwater environments. Furthermore, we discuss emerging research trends, including the use of transformer-based architectures, multi-modal sensor fusion, lightweight neural networks for real-time deployment, and self-supervised learning techniques. By identifying gaps in current research and outlining potential directions for future work, this survey serves as a valuable reference for researchers and engineers striving to develop robust and adaptive underwater SLAM solutions. Our findings aim to inspire further advancements in autonomous underwater exploration, supporting critical applications in marine science, deep-sea resource management, and environmental conservation. Full article

Underwater Wireless Sensor Networks (UWSNs) have a wide range of applications, where issues related to data authentication and communication are critical for enhancing underwater resource utilization and ensuring secure data transmission. Sensor nodes face resource limitations and the threat of quantum computing attacks, making it challenging for traditional authentication protocols to balance security and computational efficiency. By employing the Number Theory Research Unit (NTRU) encryption scheme and incorporating Generalized One-Time Pad (GOTP) key encapsulation along with a node mobility model under ocean current environments, we propose a two-round mutual authentication protocol, named the NTRU-GOTP and Position-aware Authentication Protocol (NTRU-GOPA), to verify location information and enhance security. We verify the protocol’s security using the random oracle model and analyze it through informal methods. Preliminary experiments demonstrate that the proposed protocol is more secure and computationally efficient than existing methods. This method satisfies the requirements for defending against node capture and external network attacks, thereby making it suitable for complex and dynamic underwater network scenarios. Full article

Underwater wireless sensor networks (UWSNs) widely used for maritime object detection or for monitoring of oceanic parameters that plays vital role prediction of tsunami to life-cycle of marine species by deploying sensor nodes at random locations. However, the dynamic and unpredictable underwater environment poses significant challenges in communication, including interference, collisions, and energy inefficiency. In changing underwater environment to make routing possible among nodes or/and base station (BS) an adaptive receiver-initiated deep adaptive with power control and collision avoidance MAC (DAWPC-MAC) protocol is proposed to address the challenges of interference, collisions, and energy inefficiency. The proposed framework is based on Deep Q-Learning (DQN) to optimize network performance by enhancing collision avoidance in a varying sensor locations, conserving energy in changing path loss with respect to time and depth and reducing number of relaying nodes to make communication reliable and ensuring synchronization. The dynamic and unpredictable underwater environment, shaped by variations in environmental parameters such as temperature (T) with respect to latitude, longitude, and depth, is carefully considered in the design of the proposed MAC protocol. Sensor nodes are enabled to adaptively schedule wake-up times and efficiently control transmission power to communicate with other sensor nodes and/or courier node plays vital role in routing for data collection and forwarding. DAWPC-MAC ensures energy-efficient and reliable time-sensitive data transmission, improving the packet delivery rati (PDR) by 14%, throughput by over 70%, and utility by more than 60% compared to existing methods like TDTSPC-MAC, DC-MAC, and ALOHA MAC. These enhancements significantly contribute to network longevity and operational efficiency in time-critical underwater applications. Full article

As advancements in autonomous underwater vehicle (AUV) technology unfold, the role of underwater wireless sensor networks (UWSNs) is becoming increasingly pivotal. However, the high energy consumption in these networks can significantly reduce their operational lifespan, while latency issues can impair overall network performance. To address these challenges, a novel mixed packet forwarding strategy is developed, which incorporates a wakeup threshold and a dynamically adjusted access probability for the cluster head (CH). This approach aims to conserve energy while maintaining acceptable network latency levels. The wakeup threshold restricts the frequency of state switching for the CH, thereby reducing energy consumption. Meanwhile, the dynamic access probability regulates the influx of packets to mitigate system congestion based on current network conditions. Furthermore, to accommodate the network’s varied transmission demands, packets generated by sensor nodes (SNs) are categorized into two types according to their sensitivity to latency. A discrete−time queueing model with preemptive priority is then established to evaluate the performance of different packets and the CH. Numerical results show how different parameters affect network performance and demonstrate that the proposed mixed packet forwarding mechanism can effectively manage the trade−off between latency and energy consumption, outperforming the traditional mechanism within a specific range of parameters. Full article

Underwater Wireless Sensor Networks (UWSNs) are pivotal for advancing maritime capabilities. These networks predominantly utilize acoustic communication, characterized by an open and shared acoustic channel and energy-limited underwater nodes, which underscores the critical importance of node authentication and management. Blockchain technology, recognized for its security, confidentiality, and traceability, is particularly suitable for scenarios requiring secure data exchange. This paper proposes a blockchain-based collaborative node authentication model tailored for clustered networks in UWSNs to tackle the challenges posed by the open nature of acoustic channels and the constrained energy resources of underwater nodes. Autonomous Underwater Vehicles (AUVs) are deployed as blockchain nodes to aid cluster heads in identity verification, while all underwater acoustic nodes are integrated as lightweight blockchain nodes, thus ensuring uniform management and authentication. Furthermore, this study enhances existing clustering algorithms to prolong the operational lifespan of the network and introduces a group signature and authentication mechanism tailored to the unique conditions of underwater blockchain edge computing. This mechanism includes a robust two-round block verification scheme designed to secure the blockchain against potential consensus algorithm attacks. Comprehensive simulations are presented, validating the effectiveness of the proposed group signature solution in enhancing the security and sustainability of underwater clustered networks. Full article

Approximately 75% of the Earth’s surface is covered by water, and 78% of the global animal kingdom resides in marine environments. Furthermore, algae and microalgae in marine ecosystems contribute up to 75% of the planet’s oxygen supply, underscoring the critical need for conservation efforts. This review systematically evaluates the impact of underwater communication systems on aquatic ecosystems, focusing on both wired and wireless technologies. It highlights the applications of these systems in Internet of Underwater Things (IoUT), Underwater Wireless Sensor Networks (UWSNs), remote sensing, bathymetry, and tsunami warning systems, as well as their role in reducing the ecological footprint of human activities in aquatic environments. The main contributions of this work include: a benchmark of various underwater communication systems, comparing their advantages and limitations; an in-depth analysis of the adverse effects of anthropogenic emissions associated with communication systems on marine life; and a discussion of the potential for underwater communication technologies, such as remote sensing and passive monitoring, to aid in the preservation of biodiversity and the protection of fragile ecosystems. Full article

In view of the poor adaptability and uneven coverage of static underwater wireless sensor networks (UWSNs) to environmental changes and the need for dynamic monitoring, a three-dimensional coverage method involving a dynamic UWSNs for marine ranching, based on an improved sparrow search algorithm (ISSA), is proposed. Firstly, the reverse learning strategy was introduced to generate the reverse sparrow individuals and fuse with the initial population, and the individual sparrows with high fitness were selected to improve the search range. Secondly, Levy flight was introduced to optimize the location update of the producer, which effectively expanded the local search capability of the algorithm. Finally, the Cauchy mutation perturbation mechanism was introduced into the scrounger location to update the optimal solution, which enhanced the ability of the algorithm to obtain the global optimal solution. When deploying UWSNs nodes, an autonomous underwater vehicle (AUV) was used as a mobile node to assist the deployment. In the case of underwater obstacles, the coverage hole in the UWSNs was covered by an AUV at specific times. The experimental results show that compared with other algorithms, the ISSA has a shorter mobile path and achieves a higher coverage rate, with lower node energy consumption. Full article

Underwater wireless sensor networks (UWSNs) have been applied in lots of fields. However, coverage holes are usually caused by complex underwater environment. Coverage holes seriously affect UWSNs’ performance and quality of service; thus, their recovery is crucial for 3D UWSNs. Although most of the current research recovery algorithms demand hole detection, the number of additional mobile nodes is too large, the communication and computing costs are high, and the coverage and energy balance are poor. Therefore, these methods are not suitable for UWSN hole repairing. In order to enhance the performance of hole recovery, a coverage hole recovery method for 3D UWSNs in complex underwater environments based on virtual force guidance and energy balance is proposed. The proposed method closely combines the node energy and considers complex environmental factors. A series of multi-dimensional virtual force models are established based on energy between nodes, area boundaries, zero-energy holes, low-energy coverage holes, underwater terrain, and obstacle forces. Then, a coverage hole recovery method for 3D UWSNs based on virtual force guidance and energy balance (CHRVE) is proposed. In this method, the direction and step size of mobile repairing node movement is guided by distributed computation of virtual forces, and the nodes are driven towards the target location by means of AUV or other carrier devices. The optimal position to improve coverage rate and node force balance is obtained. Simulation experiments show good adaptability and robustness to complex underwater terrain and different environments. The algorithm does not require precise coverage hole boundary detection. Furthermore, it balances network energy distribution significantly. Therefore, this method reduces the frequency of coverage hole emergence and network maintenance costs. Full article

Deploying and effectively utilizing wireless sensor networks (WSNs) in underwater habitats remains a challenging task. In underwater wireless sensors networks (UWSNs), the availability of a continuous energy source for communicating with nodes is either very costly or is prohibited due to the marine life law enforcement agencies. So, in order to address this issue, we present a Q-learning-based approach to designing an energy-efficient medium access control (MAC) protocol for UWSNs through collision avoidance. The main goal is to prolong the network’s lifespan by optimizing the communication methods, specifically focusing on improving the energy efficiency of the MAC protocols. Factors affecting the energy consumption in communication are adjustments to the interference ranges, i.e., changing frequencies repeatedly to obtain optimal communication; data packet retransmissions in case of a false acknowledgment; and data packet collision occurrences in the channel. Our chosen protocol stands out by enabling sensor (Rx) nodes to avoid collisions without needing extra communication or prior interference knowledge. According to the results obtained through simulations, our protocol may increase the network’s performance in terms of network throughput by up to 23% when compared to benchmark protocols depending on the typical traffic load. It simultaneously decreases end-to-end latency, increases the packet delivery ratio (PDR), boosts channel usage, and lessens packet collisions by over 38%. All these gains result in minimizing the network’s energy consumption, with a proportional gain. Full article

Currently, underwater sensor networks are extensively applied for environmental monitoring, disaster prediction, etc. Nevertheless, owing to the complicacy of the underwater environment, the limited energy of underwater sensor nodes, and the high latency of hydroacoustic channels, the energy-efficient operation of underwater sensor networks has become an important challenge. In this paper, a high-efficiency clustering routing protocol in AUV-assisted underwater sensor networks (HECRA) is proposed to address the energy limitations and low data transmission reliability in underwater sensor networks. The protocol optimizes the cluster head selection strategy of the traditional low-energy adaptive clustering hierarchy (LEACH) protocol by introducing the residual energy and node degree in the cluster head selection phase and performs some optimizations in the cluster formation and data transmission phases, including selecting clusters for joining by ordinary nodes based on the residual energy of the cluster head nodes and weight computation based on the depth and residual energy of the cluster head nodes to select the optimal message forwarding nodes. In addition, this paper introduces an autonomous underwater vehicle (AUV) as a dynamic relay node to improve network transmission efficiency. According to the simulation results, compared with the existing LEACH, the energy efficient routing protocol based on layers and unequal clusters in underwater wireless sensor networks (EERBLC) and energy-efficient clustering multi-hop routing protocol in a UWSN (EECMR), the HECRA significantly improves network lifetime, the residual node energy, and the number of successfully transmitted packets, which can effectively prolong network lifetime and ensure efficient data transmission. Full article

The transmission environment of underwater wireless sensor networks is open, and important transmission data can be easily intercepted, interfered with, and tampered with by malicious nodes. Malicious nodes can be mixed in the network and are difficult to distinguish, especially in time-varying underwater environments. To address this issue, this article proposes a GAN-based trusted routing algorithm (GTR). GTR defines the trust feature attributes and trust evaluation matrix of underwater network nodes, constructs the trust evaluation model based on a generative adversarial network (GAN), and achieves malicious node detection by establishing a trust feature profile of a trusted node, which improves the detection performance for malicious nodes in underwater networks under unlabeled and imbalanced training data conditions. GTR combines the trust evaluation algorithm with the adaptive routing algorithm based on Q-Learning to provide an optimal trusted data forwarding route for underwater network applications, improving the security, reliability, and efficiency of data forwarding in underwater networks. GTR relies on the trust feature profile of trusted nodes to distinguish malicious nodes and can adaptively select the forwarding route based on the status of trusted candidate next-hop nodes, which enables GTR to better cope with the changing underwater transmission environment and more accurately detect malicious nodes, especially unknown malicious node intrusions, compared to baseline algorithms. Simulation experiments showed that, compared to baseline algorithms, GTR can provide a better malicious node detection performance and data forwarding performance. Under the condition of 15% malicious nodes and 10% unknown malicious nodes mixed in, the detection rate of malicious nodes by the underwater network configured with GTR increased by 5.4%, the error detection rate decreased by 36.4%, the packet delivery rate increased by 11.0%, the energy tax decreased by 11.4%, and the network throughput increased by 20.4%. Full article

With the wide application of Underwater Wireless Sensor Networks (UWSNs) in various fields, more and more attention has been paid to deploying and adjusting network nodes. A UWSN is composed of nodes with limited mobility. Drift movement leads to the network structure’s destruction, communication performance decline, and node life-shortening. Therefore, a Node Adjustment Scheme based on Motion Prediction (NAS-MP) is proposed, which integrates the layered model of the ocean current’s uneven depth, the layered ocean current prediction model based on convolutional neural network (CNN)–transformer, the node trajectory prediction model, and the periodic depth adjustment model based on the Seagull Optimization Algorithm (SOA), to improve the network coverage and connectivity. Firstly, the error threshold of the current velocity and direction in the layer was introduced to divide the depth levels, and the regional current data model was constructed according to the measured data. Secondly, the CNN–transformer hybrid network was used to predict stratified ocean currents. Then, the prediction data of layered ocean currents was applied to the nodes’ drift model, and the nodes’ motion trajectory prediction was obtained. Finally, based on the trajectory prediction of nodes, the SOA obtained the optimal depth of nodes to optimize the coverage and connectivity of the UWSN. Experimental simulation results show that the performance of the proposed scheme is superior. Full article

The efficient coverage of underwater wireless sensor networks (UWSNs) has become increasingly important because of the scarcity of underwater node resources. Complex underwater environments, water flow forces, and undulating seabed reduce the coverage effect of underwater nodes, even leading to coverage holes in UWSNs. To solve the problems of uneven coverage distribution and coverage holes, a three-dimensional iterative enhancement algorithm is proposed for UWSN coverage hole recovery using intelligent search followed by virtual force. Benefiting from biological heuristic search algorithms, improved particle swarm optimization is applied for node pre-coverage. With the change in iteration times, the adaptive inertia weight, acceleration factor, and node position are constantly updated. To avoid excessive coverage holes caused by search falling into local optimum, underwater nodes are considered as particles in the potential field whose virtual forces are calculated to guide nodes towards higher coverage positions. In addition, based on the optimal node location obtained by the proposed algorithm, the monitoring area is divided based on the clustering idea. The underwater routing protocol DBR based on depth information is subsequently used to optimize node residual energy, and its average is calculated comprehensively and compared with the other three coverage algorithms using the DBR routing protocol. Based on the experimental data, after 100 iterations, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed algorithm are 83.28%, 88.85%, 89.31%, and 91.36%, respectively. Moreover, the proposed algorithm is further verified from the aspects of different node numbers, coverage efficiency, node movement trajectory, coverage hole, and average residual energy of nodes, which provides conditions for resource development and scientific research in marine environments. Full article