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Underwater quantum communication has recently been explored using polarization and orbital angular momentum (OAM). Here, we show that spatially structured modes, e.g., a coherent superposition of beams carrying both polarization and OAM, can also be used for underwater quantum cryptography. We also use the polarization degree of freedom to investigate the impact of the channel length on key rates for quantum communication applications. The underwater channel proves to be a difficult environment for establishing quantum communication as underwater optical turbulence results in significant beam wandering and distortions. However, the errors associated to the turbulence do not result in error rates above the threshold for establishing a positive key in a quantum communication link with both the polarization and spatially structured photons. The impact of the underwater channel on the spatially structured modes is also investigated at different distances using polarization tomography.

Underwater acoustic (UWA) channel prediction technology, as an important topic in UWA communication, has played an important role in UWA adaptive communication network and underwater target perception. Although many significant advancements have been achieved in underwater acoustic channel prediction over the years, a comprehensive summary and introduction is still lacking. As the first comprehensive overview of UWA channel prediction, this paper introduces past works and algorithm implementation methods of channel prediction from the perspective of linear, kernel-based, and deep learning approaches. Importantly, based on available at-sea experiment datasets, this paper compares the performance of current primary UWA channel prediction algorithms under a unified system framework, providing researchers with a comprehensive and objective understanding of UWA channel prediction. Finally, it discusses the directions and challenges for future research. The survey finds that linear prediction algorithms are the most widely applied, and deep learning, as the most advanced type of algorithm, has moved this field into a new stage. The experimental results show that the linear algorithms have the lowest computational complexity, and when the training samples are sufficient, deep learning algorithms have the best prediction performance.

The innovative concept of Internet of Underwater Things (IoUT) has a huge impact in different sectors including a small scientific laboratory, to a medium sized harbor, and to monitor vast undiscovered oceans. Internet of Underwater Things (IoUT) has become a powerful technology to support various applications such as collecting real-time aquatic information, naval military applications, maritime security, natural disaster prediction and control, archaeological expeditions, oil and gas exploration, shipwrecks discovery, water contamination, marine life observation and smart Ocean. IoUT is referred as smart intricately linked underwater objects to monitor these underwater operations. The IoUT framework incorporates several underwater communication technologies based on magnetic induction, optical signals, radio signals and acoustic waves. It is an emerging communication ecosystem which can reveal a new era of research, business and naval applications. It is a novel and vibrant paradigm for the Blue Economy sector bringing the ability to communicate autonomous underwater vehicles (AUVs), sensing, actuating and transferring this data to control centers using regular internet speeds through low cost technologies. It is anticipated to support future networking systems which can bring tremendous improvement in previous generations in terms of stable networking, high coverage, massive connectivity, low latency, high data rate and low power consumption. This study introduces the possible network framework of IoUT which is naturally heterogeneous and must be flexible enough to work under unpredicted ocean conditions. In this study, we examine channel models, routing protocols, networking topologies and simulation tools. Furthermore, we discussed recent advancements in IoUT in terms of smart devices, consumer electronics, communication and role of AUVs. In addition, edge computing, optical wireless communication (OWC), data analytics, blockchain, intelligent reflecting surfaces (IRS) and machine learning were viewed as promising techniques to support IoUT. We have dedicated a complete section to applications of IoUT. Finally, numerous open research challenges and future directions were presented. We believe this survey will be helpful to aggregate the research efforts and eliminate the technical uncertainties towards breakthrough novelties of IoUT.

The current era is notably characterized by the major advances in communication technologies. The increased connectivity has been transformative in terrestrial, space, and undersea applications. Nonetheless, the water medium imposes unique constraints on the signals that can be pursued for establishing wireless links. While numerous studies have been dedicated to tackling the challenges for underwater communication, little attention has been paid to effectively interfacing the underwater networks to remote entities. Particularly it has been conventionally assumed that a surface node will be deployed to act as a relay using acoustic links for underwater nodes and radio links for air-based communication. Yet, such an assumption could be, in fact, a hindrance in practice. The paper discusses alternative means by allowing communication across the air–water interface. Specifically, the optoacoustic effect, also referred to as photoacoustic effect, is being exploited as a means for achieving connectivity between underwater and airborne nodes. The paper provides background, discusses technical challenges, and summarizes progress. Open research problems are also highlighted.

Because of the Autonomous Underwater Vehicles (AUVs) potential for use in marine and oceanographic research, as well as in sectors like environmental monitoring and oil and gas development, underwater exploration and offshore wind energy, research in the underwater environment has gained a lot of attention in recent years. AUV navigation in the complicated and unpredictable underwater environment is one of the biggest challenges. Research in underwater technology has advanced dramatically, and current AUVs with proper path planning can operate for prolonged periods of time at vast depths to complete the underwater operations. This study investigates several paths planning techniques, classifying them as local or global strategies, and incorporates classical, graph-based, and intelligent optimization algorithms to improve navigation and obstacle avoidance. The examination focuses on the history of these approaches, demonstrating their increased efficiency in dynamic and complicated situations. This overview addresses the challenges that AUVs encounter in the maritime environment, notably in terms of course navigation planning and communication constraints. When applying these algorithms to AUV path planning issues, researchers frequently include extra limitations and goals unique to underwater environments, such as currents, obstructions, energy consumption, and communication constraints. It places a strong emphasis on navigating an ideal path between the starting to the end point. The global and local components of the path planning method are used to address for AUVs using efficient navigation algorithms are briefly discussed in this review study based on their advantages and disadvantages. A suggestion for additional research on AUV path planning is made on effectiveness of the reported path planning the strategies will serve as a catalyst to inspire researchers within the field to concentrate on specific issues identified for the future advancement of AUVs. The global and local path planning methods are used to address navigation based on tradition, group intelligent optimization and graph search algorithms.

Underwater autonomous operation is becoming increasingly crucial as a means to escape the hazardous high-pressure deep-sea environment. As a result, it is essential for there to be underwater exploration. The development of sophisticated computer vision is the single most significant factor for the success of underwater autonomous operations. In order to improve low-quality photos and compensate for low-light circumstances, preprocessing is used in underwater vision. This allows for clearer pictures to be seen. In this paper, we propose a deep convolutional neural network (DCNN) method for solving the weakly illuminated problem for underwater pictures. This method combines the max-RGB and shade-of-grey approaches to improve underwater visibility and to train the plotting association necessary to obtain the lighting plot. Using this method, we are able to resolve the problematic of weakly illuminated pictures in a way that is efficient. After the photos have been prepared, a deep convolutional neural network (DCNN) approach is developed for detection and classification in the water. Two updated methods are then utilized in order to adapt the architecture of the DCNN to the qualities of underwater vision. The purpose of this investigation is to present a Kalman Filter (KF) method as a solution to the difficulties associated with underwater communication in terms of object tracking and detection. We were able to separate a section of the object by employing a threshold segment and morphological technique. This allowed us to investigate the invariant moment and area properties of the section. Based on the findings, it can be decided that the suggested technique is useful for monitoring underwater targets using DCNN-KF. Furthermore, it displays high resilience, high accuracy, and real-time characteristics. Results from the simulations show that the suggested model DCNN-KF does a better job of localization than the most advanced methods at the time of the study.

The term “Internet of Underwater Things (IoUT)” refers to a network of intelligent interconnected underwater devices designed to monitor various underwater activities. The IoUT allows for a network of autonomous underwater vehicles (AUVs) to communicate with each other, sense their surroundings, collect data, and transmit them to control centers on the surface at typical Internet speeds. These data serve as a valuable resource for various tasks, including conducting crash surveys, discovering shipwrecks, detecting early signs of tsunamis, monitoring animal health, obtaining real-time aquatic information, and conducting archaeological expeditions. This paper introduces an additional set of alternative simulation tools for underwater networks. We categorize these tools into open-source and licensed simulator options and recommend that students consider using open-source simulators for monitoring underwater networks. There has not been widespread deployment or extensive research on underwater 5G-based networks. However, simulation tools provide some general insights into the challenges and potential issues associated with evaluating such networks, based on the characteristics of underwater communication and 5G, by surveying 5G-based underwater networks and 5G key aspects addressed by the research community in underwater network systems. Through an extensive review of the literature, we discuss the architecture of both Internet of Underwater application-assisted AUVs and Internet of Underwater Things communications in the 5G-based system.

Oceans cover more than 70% of the Earth’s surface. For various reasons, almost 95% of these areas remain unexplored. Underwater wireless communication (UWC) has widespread applications, including real-time aquatic data collection, naval surveillance, natural disaster prevention, archaeological expeditions, oil and gas exploration, shipwreck exploration, maritime security, and the monitoring of aquatic species and water contamination. The promising concept of the Internet of Underwater Things (IoUT) is having a great influence in several areas, for example, in small research facilities and average-sized harbors, as well as in huge unexplored areas of ocean. The IoUT has emerged as an innovative technology with the potential to develop a smart ocean. The IoUT framework integrates different underwater communication techniques such as optical, magnetic induction, and acoustic signals. It is capable of revolutionizing industrial projects, scientific research, and business. The key enabler technology for the IoUT is the underwater wireless sensor network (UWSN); however, at present, this is characterized by limitations in reliability, long propagation delays, high energy consumption, a dynamic topology, and limited bandwidth. This study examines the literature to identify potential challenges and risks, as well as mitigating solutions, associated with the IoUT. Our findings reveal that the key contributing elements to the challenges facing the IoUT are underwater communications, energy storage, latency, mobility, a lack of standardization, transmission media, transmission range, and energy constraints. Furthermore, we discuss several IoUT applications while highlighting potential future research directions.

Underwater Optical Wireless Communication (UOWC) is not a new idea, but it has recently attracted renewed interest since seawater presents a reduced absorption window for blue-green light. Due to its higher bandwidth, underwater optical wireless communications can support higher data rates at low latency levels compared to acoustic and RF counterparts. The paper is aimed at those who want to undertake studies on UOWC. It offers an overview on the current technologies and those potentially available soon. Particular attention has been given to offering a recent bibliography, especially on the use of single-photon receivers.

In this study, the challenge of teleoperating robots in harsh environments such as underwater or in tunnels is addressed. In these environments, wireless communication networks are prone to congestion, leading to potential mission failures. Our approach integrates a Human–Robot Interface (HRI) with a network congestion control algorithm at the application level for conservative transmission of images using the Robot Operating System (ROS) framework. The system was designed to avoid network congestion by adjusting the image compression parameters and the transmission rate depending on the real-time network conditions. To evaluate its performance, the algorithm was tested in two wireless underwater use cases: pipe inspection and an intervention task. An Autonomous Underwater Vehicle for Intervention (I-AUV) equipped with a Visual Light Communication (VLC) modem was used. Characterization of the VLC network was performed while the robot performed trajectories in the tank. The results demonstrate that our approach allows an operator to perform wireless missions where teleoperation requires images and the network conditions are variable. This solution provides a robust framework for image transmission and network control in the application layer, which allows for integration with any ROS-based system.