Adaptive Visible Light Positioning with MSE Inner Loop for Underwater Environment

Underwater wireless sensor networks are essential for ocean exploration and military use. Acoustic communication is the predominant method due to the unique underwater conditions, though challenges like low bandwidth and multipath effects hinder its effectiveness. Terrestrial communication protocols are not directly applicable underwater, making network security and stable data transmission critical research areas. This paper reviews underwater sensor network communication technologies, architectures, protocol stacks, and security challenges, assessing the suitability of various communication methods and the components of these networks. It also analyzes the main topological structures and the security risks across the physical, data link, network, transport, and application layers. Additionally, the paper investigates positioning and navigation technologies in underwater communications and their future applications. Advancements in underwater optical communication and the development of intelligent underwater networks and the underwater Internet of Things (IoUT) are identified as key future directions.

Experimental demonstration of underwater wireless optical OFDM communication system with a single SPAD receiver

The Internet of Underwater Things (IoUT) has lately gained popularity as a means of facilitating a wide range of underwater applications. In the IoUT, underwater communication is best accomplished by the usage of acoustic waves, whereas the terrestrial communication between the surface sinks and the on‐shore control stations is typically achieved using radio waves. As a result, the greatest portion of an IoUT is enabled by the underwater acoustic sensor network (UASN), where the specific issues provided by the use of acoustic waves, the underwater node mobility, and the localization difficulties have yet to be addressed. In this paper, we discuss the challenges faced by the IoUT in terms of the currently proposed routing protocols and propose a Directional Selective Power Routing Protocol (DSPR) to cope with most of these challenges. The proposed protocol (i.e., DSPR) uses the angle of arrival and the sender depth information to find the best direction to the surface sink. In addition, the DSPR uses selective power control to enhance the delivery ratio and ensure connectivity while reducing energy consumption. To testify the performance of the proposed protocol, intensive simulation experiments have been conducted. The simulation results show that the proposed DSPR protocol outperforms two variations of the fixed directional routing (DR) protocol and the variable power depth‐based routing (VDBR) protocol in terms of energy consumption and delivery ratio. For instance, the proposed DSPR protocol achieves at least 8 times enhancement in energy consumption compared with VDBR. In addition, DSPR saves around 30% of energy consumption over the DR protocols when the network is mobile. Moreover, the DSPR protocol acquires a delivery ratio above 90% for static/dynamic scenarios in both sparse and dense networks.

The underwater Internet of things (UIoT) has become a prominent technology in next-generation ocean monitoring systems, with autonomous underwater vehicles (AUVs) serving as critical mobile edge sensing platforms for sensor network data collection. The deployment and application of 5 G technology in UIoT mark a significant advancement in underwater network communication capabilities. However, the performance of UIoT is critically constrained by two fundamental challenges: the dynamic and unpredictable nature of underwater environments, and the energy limitations of AUV serving as mobile edge nodes. For instance, node mobility caused network instability, affecting data collection efficiency. And high and uneven energy consumption leads to shortened network lifetime. Moreover, limited AUV energy results in AUV loss and diminished data collection efficiency. To solve these problems, an energy-efficient optimization data collection algorithm based on mobile edge sensing in 5 G UIoT (EEODC-MES) is proposed in this paper. In EEODC-MES, the network clustering is constructed by analyzing the movement characteristics of sensor nodes, and a cluster-head node is selected. Subsequently, the reward for edge sensing device (AUV) collecting data from cluster-head nodes is calculated based on the payoff matrix. The cluster-head node with the highest reward value is prioritized for AUV visitation. AUV decides whether to continue visiting cluster nodes or return to base based on its remaining energy and return energy consumption. The performance of EEODC-MES is compared with that of other data collection algorithms, namely greedy and adaptive AUV path-finding (GAAP), AUV-aided energy-efficient data collection (AEEDCO), and traveling salesperson problem (TSP). Compared with GAAP, AEEDCO, and TSP, EEODC-MES, respectively, improves the network lifetime by 31.8%, 30.1%, and 7.1%. Compared with GAAP and TSP, EEODC-MES, respectively, reduces the collection delay by 26.08% and 51.77%.

To establish a secure and high-bandwidth communication link between the gateway node and the central node in the internet of underwater things (IoUwT), it is meaningful to introduce quantum key distribution (QKD) protocols into underwater wireless optical communication (UWOC) systems. However, the line-of-sight (LOS) requirement for photon transmission will pose an inevitable challenge to the QKD-based UWOC system. In this work, an optical intelligent reflecting surface (OIRS) array mounted on an autonomous underwater vehicle (AUV) is utilized for the first time to alleviate the LOS blockage and enable more reliable underwater wireless optical quantum link for both discrete-variable quantum key distribution (DV-QKD) and continuous-variable quantum key distribution (CV-QKD). To begin with, a novel statistical model of the aggregated channel transmissivity experienced by the OIRS-based quantum states in underwater link is derived by combing the effects of oceanic absorption, scattering, turbulence and OIRS misalignment, where the oceanic turbulence-induced irradiance fluctuation is modeled by exponential and generalized gamma (EGG) distribution, and the jitter angle associated with AUV-carried OIRS misalignment is characterized by a Rayleigh distribution. Then, on the basis of this statistical model and the Gauss-Chebyshev quadrature, the quantum bit error rate and the lower bound secret key rate (SKR) for the AUV-carried OIRS-assisted DV-QKD link are obtained with weak coherent optical source and decoy state idea by utilizing Gottesman-Lo-Lütkenhaus-Preskill. In terms of univariate Fox-H function, the average Pirandola-Laurenza-Ottaviani-Banchi (PLOB) bound over the bosonic pure-loss channel, as well as the thermal upper and lower bounds over the thermal-loss channel are both derived for the AUV-carried OIRS-assisted CV-QKD link. Additionally, the achievable SKR for a practical GG02 CV-QKD protocol in underwater channels is also presented through the worst-case analysis. Furthermore, the impacts of the number of OIRS elements, OIRS positioning, jitter variances, the probability of erroneous detection and link distance on the security performance of the proposed links are studied with different water types, thereby offering valuable insights for the QKD-based UWOC system in IoUwT.

The development of intelligent systems for the monitoring, exploration, and administration of underwater environments is made possible by the Internet of Underwater Things (IoUT), which is a revolutionary development in marine and environmental research. This thorough research examines the advancements, challenges, and promise of IoUT with a focus on its applications in domains such as resource extraction, the science of oceanography underwater tracking, and tracking the environment. IoUT systems require customised approaches in processing information, conservation of energy, connectivity, and sensor that is being tested design because to the particular difficulties of underwater settings. The new protocols and methods designed for underwater applications—such as acoustic, optical, and electromagnetic communications—as well as the incorporation of artificial intelligence (AI) and machine learning (ML) technologies for improved data processing and taking decisions are covered in this study. Furthermore, we draw attention to the urgent issues surrounding data security, environmentally friendly interaction, and installation expenses while providing suggestions for future lines of inquiry and technical advancements.

Guest Editorial

Underwater is a harsh and dynamic environment. Its physical properties are unsteady in the space-time domain and that complicates the communication process inside the water. Moreover, underwater transmissions suffer from many attenuations and scattering sources due to several environmental noises such as shipping, turbulence, wave dynamics, and the background life of the marine organisms.In this chapter, we introduce the underwater communication basics and challenges compared with terrestrial networks, then illustrate the Internet of Underwater Things (IoUT) network structure and the underwater node architecture, and finally, overview the machine learning modeling in underwater communication.KeywordsUnderwaterInternet of Underwater Things (IoUT)Cooperative communicationMachine learning modelingUnderwater wireless sensor networksAmplify-and-forward (AF)Decode-and-forward (DF)Hybrid and cooperative transmissionTransmission power levelsRelayMoteMachine learning techniquesSupervised learningMachine learning algorithms

In traditional underwater communication systems, acoustic waves are commonly used. However, acoustic underwater systems have low bandwidth, which is not enough for modern wireless sensors and underwater vehicles. The use of radio waves also does not provide the desired combination of transmission speed and range. All this has led to the development of optical wireless underwater communication, as it potentially provides a higher data rate than traditional acoustic underwater communication systems with significantly lower energy consumption. One of the main difficulties in designing underwater optical wireless systems is the complexity of modeling the physical and chemical characteristics of ocean water. The purpose of this survey is to generalize the available models of underwater optical communication links taking into account the various propagation effects that affect the range and speed of the system.

The present study examined and simulated the coding or no coding performance for different configurations of multi-input, multioutput underwater wireless optical communication systems. MIMO diversity and channel coding were used to reduce the power transmitter, assuming lognormal distribution random for weak ocean turbulence. The simulation tested for a link range of 30 m in low ocean turbulence and 500 Mbps on–off-keying was also presented using Monte-Carlo. The results showed improvement in power transmitted (gain) for 2x5 compared to Uncoded SISO, which was about 33.60db using the concatenated code. So that the underwater wireless optical communications (UWOC) even now will also be significantly impacted by the Internet of Underwater Things (IoUT) and ultimate mobile technologies five generation communication systems because of their extraordinarily high demand for data safety, bit rate, energy consumption, so it is used in combined data transfer between underwater communication devices. The UWOC system based on coded MIMO may successfully be used in IoUT due to its high robustness and power qualification.

The emerging Internet of Underwater Things (IoUT) and deep learning technologies are combined to provide a novel, intelligent, and efficient data processing and analyzing schema, which facilitates the sensing and computing abilities for the smart ocean. The underwater acoustic (UWA) communication network is an essential part of IoUT. The thermocline, in which temperature and density change drastically, affects the connectivity and communication performance between IoUT nodes, as well as the network topologies. In this paper, we propose DeepOcean, a deep learning framework for spatio-temporal ocean sensing data prediction, which consists of a generative module and a prediction module. We implement the generative module with a multi-layer perceptron (MLP) to capture the spatial dependencies and construct high-resolution data based on sparse observations. The prediction module is implemented with our proposed Multivariate Convolutional LSTM (MVC-LSTM) neural network, which captures both the spatio-temporal dependencies and the interactions of different oceanographic features for prediction. We evaluate the effectiveness of DeepOcean with Argo data, where the proposed framework outperforms fifteen state-of-art baselines in terms of accuracy.

As the dominant information carrier in water, the acoustic wave is widely used for underwater detection, communication and imaging. Even though underwater acoustic communication has been greatly improved in the past decades, it still suffers from slow transmission speeds and low information capacity. The recently developed acoustic orbital angular momentum (OAM) multiplexing communication promises a high efficiency, large capacity and fast transmission speed for acoustic communication. However, the current works on OAM multiplexing communication mainly appear in airborne acoustics. The application of acoustic OAM for underwater communication remains to be further explored and studied. In this paper, an impedance matching pentamode demultiplexing metasurface is designed to realize multiplexing and demultiplexing in underwater acoustic communication. The impedance matching of the metasurface ensures high transmission of the transmitted information. The information encoded into two different OAM beams as two independent channels is numerically demonstrated by realizing real-time picture transfer. The simulation shows the effectiveness of the system for underwater acoustic multiplexing communication. This work paves the way for experimental demonstration and practical application of OAM multiplexing for underwater acoustic communication.

The Internet of underwater things (IoUT) has become a promising and vibrant paradigm which mainly relies on the underwater acoustic wireless sensor networks (UASN). IoUT was first proposed to facilitate offshore real‐time monitoring and exploration of underwater environments. It is now becoming a key component for building and connecting the futuristic underwater smart cities. However, Acoustic waves used in underwater communication suffer from long propagation delays and high transmission energy requirements. Therefore, approaches for autonomous underwater vehicle (AUV) assisted data collection and cluster‐based routing were proposed to overcome these drawbacks. In this article, we present a smart genetic‐based AUV path planning algorithm for data collection to enhance the performance of UASN. In addition, we combine our smart data collection technique with a dynamic location‐unaware clustering algorithm to further reduce energy consumption with mobility consideration. Our simulation results showed that our genetic‐based technique achieves more than 145% increase in network lifetime and almost a 75% reduction in energy expenditure compared with traditional diagonal path selection technique. More simulations comparing our combined technique with a novel clustering technique showed that the network lifetime is twice as better, total energy consumption is nearly 30% less, and the delivery ratio is 15% more at low densities.

Although many researchers have achieved wireless optical ranging and communication based on Avalanche Photon Diode (APD) detectors and PIN detectors. However, due to the low sensitivity of APD and PIN detectors, their working distances are limited. In order to achieve long-distance underwater wireless optical communication, a Single-Photon Avalanche Diode (SPAD) with photon-limited sensitivity is used to detect optical signals. In addition, most literatures do not specially design the data frames of ranging and communication, which makes it difficult to perform ranging and communication at the same time. In view of the fact that there is currently no systematic solution for simultaneous communication and high-precision ranging under underwater photon counting, this paper sorts out the relevant technical details, proposes an integrated ranging and communication scheme applied to photon counting, designs a special data frame that integrates ranging and communication, and proposes an extraction and recovery scheme for ranging and communication signals. Simulation shows that this scheme can achieve underwater photon counting wireless optical communication with a communication distance of 160m and an SER of 9.75×10-6, and the ranging accuracy is better than 1.59cm.

The growing need for underwater observation and subsea monitoring systems has stimulated considerable interest in advancing the enabling technologies of underwater wireless communication and underwater sensor networks. This communication technology is expected to play an important role in investigating climate change, in monitoring biological, biogeochemical, evolutionary, and ecological changes in the sea, ocean, and lake environments, and in helping to control and maintain oil production facilities and harbors using unmanned underwater vehicles (UUVs), submarines, ships, buoys, and divers. However, the present technology of underwater acoustic communication cannot provide the high data rate required to investigate and monitor these environments and facilities. Optical wireless communication has been proposed as the best alternative to meet this challenge. Models are presented for three kinds of optical wireless communication links: (a) a line-of-sight link, (b) a modulating retroreflector link, and (c) a reflective link, all of which can provide the required data rate. We analyze the link performance based on these models. From the analysis, it is clear that as the water absorption increases, the communication performance decreases dramatically for the three link types. However, by using the scattered light it was possible to mitigate this decrease in some cases. It is concluded from the analysis that a high-data-rate underwater optical wireless network is a feasible solution for emerging applications such as UUV-to-UUV links and networks of sensors, and extended ranges in these applications could be achieved by applying a multi-hop concept.