Search Results (414)

Due to increasing demand for the exploration of marine resources, underwater optical wireless sensor networks (UOWSNs) have emerged as a promising solution by offering higher bandwidth and lower latency compared to traditional underwater acoustic wireless sensor networks (UAWSNs), with their existing routing protocols facing challenges in energy consumption and packet forwarding. To address these challenges, this paper proposes an energy-partitioned routing protocol based on an advancement function (EPAR) for UOWSNs. By dynamically classifying the nodes into high-energy and low-energy ones, the proposed EPAR algorithm employs an adaptive weighting strategy to prioritize the high-energy nodes in relay selection, thereby balancing network load and extending overall lifetime. In addition, a tunable advancement function is adopted by the proposed EPAR algorithm by comprehensively considering the Euclidean distance and steering angle toward the sink node. By adjusting a tunable parameter α, the function guides forwarding decisions to ensure energy-efficient and directionally optimal routing. Additionally, by employing a hop-by-hop neighbor discovery mechanism, the proposed algorithm enables each node to dynamically update its local neighbor set, thereby improving relay selection and mitigating the impact of void regions on the packet delivery ratio (PDR). Simulation results demonstrate that EPAR can obtain up to about a 10% improvement in PDR and up to about a 30% reduction in energy depletion, with a prolonged network lifetime when compared to the typical algorithms adopted in the simulations. Full article

Underwater sensor networks (UWSNs) play a crucial role in subsea operations like marine exploration and environmental monitoring. A major challenge for UWSNs is achieving effective and energy-efficient data collection, particularly in deep-sea mining, where energy limitations and long-term deployment are key concerns. This study introduces a Channel-Aware AUV-Aided Data Collection Scheme (CADC) that utilizes deep reinforcement learning (DRL) to improve data collection efficiency. It features an innovative underwater node traversal algorithm that accounts for unique underwater signal propagation characteristics, along with a DRL-based path planning approach to mitigate propagation losses and enhance data energy efficiency. CADC achieves a 71.2% increase in energy efficiency compared to existing clustering methods and shows a 0.08% improvement over the Deep Deterministic Policy Gradient (DDPG), with a 2.3% faster convergence than the Twin Delayed DDPG (TD3), and reduces energy cost to only 22.2% of that required by the TSP-based baseline. By combining a channel-aware traversal with adaptive DRL navigation, CADC effectively optimizes data collection and energy consumption in underwater environments. Full article

The autonomous navigation of an Unmanned Underwater Vehicle (UUV) in unknown 3D underwater environments remains a challenging task due to the presence of complex terrain, uncertain obstacles, and strict kinematic constraints. This paper proposes a novel smooth path planning framework that integrates improved Rapidly-exploring Random Tree* (RRT*) with 3D Dubins curves to efficiently generate feasible and collision-free trajectories for nonholonomic UUVs. A fast curve-length estimation approach based on a backpropagation neural network is introduced to reduce computational burden during path evaluation. Furthermore, the improved RRT* algorithm incorporates pseudorandom sampling, terminal node backtracking, and goal-biased exploration strategies to enhance convergence and path quality. Extensive simulation results in unknown underwater scenarios with static and moving obstacles demonstrate that the proposed method significantly outperforms state-of-the-art planning algorithms in terms of smoothness, path length, and computational efficiency. Full article

Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment. Full article

The rise of the Internet of Things (IoT) has made it possible to explore different types of communication, such as underwater IoT (UIoT). This new paradigm allows the interconnection of ships, boats, coasts, objects in the sea, cameras, and animals that require constant monitoring. The use of sensors for environmental monitoring, tracking marine fauna and flora, and monitoring the health of aquifers requires the integration of heterogeneous technologies as well as wireless communication technologies. Aquatic mobile sensor nodes face various limitations, such as bandwidth, propagation distance, and data transmission delay issues. Owing to their versatility, wireless sensor networks support remote monitoring and surveillance. In this work, an architecture for a general packet radio service (GPRS) wireless sensor network is presented. The network is used to monitor the geographic position over the coastal area of the Gulf of Mexico. The proposed architecture integrates cellular technology and some ad hoc network configurations in a single device such that coverage is improved without significantly affecting the energy consumption, as shown in the results. The network coverage and energy consumption are evaluated by analyzing the attenuation in a proposed channel model and the autonomy of the electronic system, respectively. Full article

Nowadays, underwater activities are becoming more and more important. As the number of underwater sensing devices grows rapidly, the amount of bandwidth needed also increases very quickly. Apart from underwater communication, direct communication across the water–air interface is also highly desirable. Air-to-water wireless transmission is crucial for sending control information or instructions from unmanned aerial vehicles (UAVs) or ground stations above the sea surface to autonomous underwater vehicles (AUVs). On the other hand, water-to-air wireless transmission is also required to transmit real-time information from AUVs or underwater sensor nodes to UAVs above the water surface. Previously, we successfully demonstrated a water-to-air optical camera-based OWC system, which is also known as optical camera communication (OCC). However, the reverse transmission (i.e., air-to-water) using OCC has not been analyzed. It is worth noting that in the water-to-air OCC system, since the camera is located in the air, the image of the light source is magnified due to diffraction. Hence, the pixel-per-symbol (PPS) decoding of the OCC pattern is easier. In the proposed air-to-water OCC system reported here, since the camera is located in the water, the image of the light source in the air will be diminished in size due to diffraction. Hence, the PPS decoding of the OCC pattern becomes more difficult. In this work, we propose and experimentally demonstrate a wide field-of-view (FOV) air-to-water OCC system using CUDA Deep-Neural-Network Long-Short-Term-Memory (CuDNNLSTM). Due to water turbulence and air turbulence affecting the AUV and UAV, a precise line-of-sight (LOS) between the AUV and the UAV is difficult to achieve. OCC can provide wide FOV without the need for precise optical alignment. Results revealed that the proposed air-to-water OCC system can support a transmission rate of 7.2 kbit/s through a still water surface, and 6.6 kbit/s through a wavy water surface; this satisfies the hard-decision forward error correction (HD-FEC) bit-error-rate (BER). Full article

Localizing underwater nodes when they cannot be tethered or float on the surface presents significant challenges, primarily due to node mobility and the absence of fixed anchors with known coordinates. This paper advocates a strategy for tackling such a challenge by using visible light communication (VLC) from an airborne unit. A novel localization method is proposed where VLC transmissions are made towards the water surface; each transmission is encoded with the Global Positioning System (GPS) coordinates with the incident point of the corresponding light beam. Existing techniques deal with the problem in 2D by assuming that the underwater node has a pressure sensor to measure its depth. The proposed method avoids this limitation and utilizes the intensity of VLC signals to estimate the 3D position of the underwater node. The idea is to map the light intensity at the underwater receiver for airborne light beams and devise an error optimization formulation to estimate the 3D coordinates of the underwater node. Extensive simulations validate the effectiveness of the proposed method and capture its performance across various parameters. Full article

This work presents a high-speed hybrid communication system integrating Underwater Optical Wireless Communication (UOWC), Multimode Fiber (MMF), and Free-Space Optics (FSO) channels, leveraging Orbital Angular Momentum (OAM) beams for enhanced data transmission. A Photodetector, Remodulate, and Forward Relay (PRFR) is employed to enable wavelength conversion from 532 nm for UOWC to 1550 nm for MMF and FSO links. Four distinct OAM beams, each supporting a 5 Gbps data rate, are utilized to evaluate the system’s performance under two scenarios. The first scenario investigates the effects of absorption and scattering in five water types on underwater transmission range, while maintaining fixed MMF length and FSO link. The second scenario examines varying FSO propagation distances under different fog conditions, with a consistent underwater link length. Results demonstrate that water and atmospheric attenuation significantly impact transmission range and received optical power. The proposed hybrid system ensures reliable data transmission with a maximum overall transmission distance of 1125 m (comprising a 25 m UOWC link in Pure Sea (PS) water, a 100 m MMF span, and a 1000 m FSO range in clear weather) in the first scenario. In the second scenario, under Light Fog (LF) conditions, the system achieves a longer reach of up to 2020 m (20 m UOWC link + 100 m MMF span + 1900 m FSO range), maintaining a BER ≤ 10⁻⁴ and a Q-factor around 4. This hybrid design is well suited for applications such as oceanographic research, offshore monitoring, and the Internet of Underwater Things (IoUT), enabling efficient data transfer between underwater nodes and surface stations. Full article

In the following study, power allocation in underwater cooperative communication systems was investigated using game theory. To balance the energy consumption of nodes, extend their lifespan, and improve communication quality, a Stackelberg power control algorithm based on a non-uniform pricing mechanism was proposed. The interaction model between the transmitting and relay nodes was constructed as a two-layer Stackelberg game, which consisted of leaders and followers. The transmitting node acts as the leader, with its objective function comprising its transmission cost and the purchasing transmission power cost of the relay nodes. The relay nodes act as followers, with their objective function comprising revenue from selling power and their transmission cost. In addition, the remaining energy is incorporated into the objective function to balance the energy consumption of the nodes. Our simulation results indicate that, compared with algorithms that do not consider remaining energy, this algorithm improves the communication quality of the cooperative system and extends the network’s lifetime. Full article

Underwater acoustic communication networks (UACNs) play a critical role in ocean environmental monitoring, maritime rescue, and military applications. However, they are highly susceptible to performance degradation due to narrow bandwidths, long propagation delays, and severe multipath effects, especially adversarial jamming attacks. Traditional anti-jamming techniques struggle to adapt to the dynamic nature of underwater acoustic channels effectively. To address this issue, an anti-jamming power control and relay optimization method was developed based on transfer reinforcement learning. By introducing relay nodes, the reliability of jammed communication links is enhanced. Transfer learning was used to initialize Q-values and strategy distributions and accelerate the convergence of reinforcement learning in the underwater communication environment, thereby mitigating the inefficiency of random exploration in the early stages. The proposed method optimizes the transmission power and relay selection to improve the signal-to-interference-plus-noise ratio (SINR) and reduce the bit error rate (BER). Simulation results demonstrated that the proposed method significantly enhanced the anti-jamming performance and communication efficiency of underwater acoustic communication even in complex interference scenarios. 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 acoustic sensor networks (UASNs) play an increasingly crucial role in both civilian and military fields. However, existing routing protocols primarily rely on node position information for forwarding decisions, neglecting link quality and energy efficiency. To address these limitations, we propose a fuzzy logic reasoning adaptive forwarding (FLRAF) routing protocol for three-dimensional (3D) UASNs. First, the FLRAF method redefines a conical forwarding region to prioritize nodes with greater effective advance distance, thereby reducing path deviations and minimizing the total number of hops. Unlike traditional approaches based on pipeline or hemispherical forwarding regions, this design ensures directional consistency in multihop forwarding, which improves transmission efficiency and energy utilization. Second, we design a nested fuzzy inference system for forwarding node selection. The inner inference system evaluates link quality by integrating the signal-to-noise ratio and some metrics related to the packet reception rate. This approach enhances robustness against transient fluctuations and provides a more stable estimation of link quality trends in dynamic underwater environments. The outer inference system incorporates link quality index, residual energy, and effective advance distance to rank candidate nodes. This multimetric decision model achieves a balanced trade-off between transmission reliability and energy efficiency. Simulation results confirm that the FLRAF method outperforms existing protocols under varying node densities and mobility conditions. It achieves a higher packet delivery rate, extended network lifetime, and lower energy consumption. These results demonstrate that the FLRAF method effectively addresses the challenges of energy constraints and unreliable links in 3D UASNs, making it a promising solution for adaptive and energy-efficient underwater communication. 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

The transmission of marine data is an urgent global challenge. Due to the particularity of underwater environments, the efficiency and reliability of data transmission in underwater acoustic communication are severely restricted, especially in long-distance and large-scale data transmission situations. This study proposes a dual-link data transmission method based on the Internet of Vessels, utilizing the powerful communication capabilities and flexibility of ships as relay nodes for data transmission. By constructing both above-water and underwater dual-link collaborative transmission, the method effectively improves data transmission rates and stability. Additionally, a spatial crowdsourcing allocation algorithm based on Bayesian reputation selection is designed to assess the capability of ships to complete tasks, and an integrated scoring function is used to select the optimal relay ship, solving the problems of relay ship selection and transmission path selection in the data transmission process. Furthermore, this study introduces an incentive mechanism for data transmission based on the Internet of Vessels, which maximizes the stability of data transmission. Experimental results show that the dual-link data transmission method of the Internet of Vessels significantly improves the reliability and transmission speed of underwater communication, providing a novel and practical solution for long-distance, large-volume data transmission in maritime environments. Full article

Localization is vital and fundamental for underwater sensor networks. However, the field still faces several challenges, such as the difficulty of accurately deploying beacon nodes, high deployment costs, imprecise underwater ranging, and limited node energy. To overcome these challenges, we propose a crowdsensing-based underwater localization algorithm (CSUL) by leveraging the computational and localization resources of vessels. The algorithm is composed of three stages: crowdsensing, denoising, and aggregation-based optimization. In the crowdsensing stage, nodes transmit localization requests, which are received by vessels and broadcasted to nearby vessels. Using concentric circle calculations, the localization problem is transformed from a three-dimensional space to a two-dimensional plane. An initial set of potential node locations, termed the concentric circle center set, is derived based on a time threshold. The denoising stage employs a Density-Based Noise Removal (DBNR) algorithm to eliminate noise caused by vessel mobility, environmental complexity, and the time threshold, thereby improving localization accuracy. Finally, in the aggregation-based optimization stage, the denoised node location set is refined using a centroid-based approximate triangulation (CBAT) algorithm to determine the final node location. Simulation results indicate that the proposed method achieves high localization coverage without requiring anchor nodes and significantly improves localization accuracy. Additionally, since all localization computations are carried out by vessels, node energy consumption is greatly reduced, effectively extending the network’s lifetime. Full article