Designing an Adaptive Underwater Visible Light Communication System.

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

This paper demonstrates a multi-hop underwater visible light communication (VLC) system using light emitting diodes (LEDs). It is demonstrated that using a multi-hop technique, the overall underwater VLC distance can be increased significantly without adding significant complexity to the physical layer. The performance of the multi-hop system is compared to a single-hop technique utilizing three types of water in the laboratory environment, namely sea, canal, and tap water. The obtained results validate that a multi-hop technique can be a viable solution to overcome the problem of limited underwater communication range.

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%.

Underwater visible light communication (UVLC) has recently come to light as a viable wireless carrier for signal transmission in risky, uncharted, and delicate aquatic environments like seas. Despite the potential of UVLC as a green, clean, and safe alternative to conventional communication methods, it is challenged by significant signal attenuation and turbulent channel conditions compared to long-distance terrestrial communication. To address linear and nonlinear impairments in UVLC systems, this paper presents an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) for 64 Quadrature Amplitude Modulation-Component minimal Amplitude Phase shift (QAM-CAP)-modulated UVLC systems. The proposed AFL-DLE is dependent on complex-valued neural networks and constellation partitioning schemes and utilizes the Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA) to improve overall system performance. Experimental outcomes demonstrate that the suggested equalizer achieves significant reductions in bit error rate (55%), distortion rate (45%), computational complexity (48%), and computation cost (75%) while maintaining a high transmission rate (99%). This approach enables the development of high-speed UVLC systems capable of processing data online, thereby advancing state-of-the-art underwater communication.

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

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.

Power efficient ultra-wideband based wireless communication for wireless body area network applications

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.

Recent years have seen a surge in the usage of optical signals for real-time wireless data transfer in a wide range of underwater applications because of their large bandwidth, higher data rates, low latency, security, power, and cost efficiency. However, underwater optical wireless communication (UOWC) is substantially impaired by absorption, scattering, and turbulence effects that induce scintillations during optical signal propagation. A potential modulation scheme can be a promising solution to overcome such scintillations. The pulse position modulation (PPM) may address the fading and optical irradiance across the underwater channels while providing the power efficiency to the wireless sensor networks (WSNs). To access the link performance under the proposed modulation scheme, the closed-form expressions for outage probability and BER metrics are typically required. Unlike horizontal and single-layered models, this work evaluates the performance of vertical cascaded links in the North and Baltic oceans affected by the combined effects of absorption, scattering, and turbulence by utilizing an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>-ary PPM scheme. The unified expressions for the probability density function (PDF) and cumulative distribution function (CDF) are obtained by integrating depth-dependent layered Gamma-Gamma (GG) turbulence with absorption- and scattering-based path-loss (PL) attenuations. The closed-form expressions for the outage probability (OP) and the average bit error rate (ABER) are derived by following the derived PDF and CDF to evaluate the end-to-end (E2E) performance under<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</i>-ary PPM. The analytical results are validated using experimentally extracted channel parameters and rigorous Monte-Carlo (MC) simulations.

The Internet of Underwater Things is a paradigm coming up beside well-known Internet of Things, but focusing on a specific environment that is the underwater scenario. Due to high attenuation and then limited performance, communication links in this harsh scenario are provided by dedicated wireless technologies such as optical wireless communication, acoustic communication. The high data rate achievable with underwater optical wireless makes it an enabling candidate technology for effective underwater communication systems. One of the most prominent topics related with underwater communication, is localisation, for all those underwater applications requiring high accuracy, such as marine ecology, seabed monitoring, etc. However, the high dynamic characteristics of the underwater environment can limit the localisation accuracy, thus resulting in high estimation errors. In this paper, we present an adaptive localisation algorithm that exploits optical wireless connectivity, in the visible range. It takes into account information about water turbidity and is able to dynamically correct the estimation error in case of variable water conditions. The effectiveness of the proposed approach has been compared to a previous technique, which does not exploit information about the environment and then results to be effective only if exists an apriori knowledge of the real water conditions.

This paper presents an approach of model-based design for implementing a digital communication system on a field programmable gate array (FPGA) for a software defined radio (SDR). SDR is a popular prototyping platform for wireless communication systems due to its flexibility and utility. A traditional SDR system performs nearly all computations and signal processing tasks on the host computer, and then sends the waveform to the RF front end. For complex algorithms or high data rate, the host computer becomes the processing bottleneck and FPGA is often employed as a hardware accelerator. This paper demonstrates the procedure of using model-based design for SDR targeted on FPGA hardware. A complete digital communication system, including a transmitter with convolutional encoder and a receiver with Viterbi decoder, is implemented on an FPGA-based SDR platform and validated by over-the-air demonstration. Synchronization algorithms, such as carrier frequency offset, phase offset, and time recovery, are also optimized for hardware efficiency.

In this paper, we propose a real-time transmission and reception scheme for high-quality video using the directional antenna with the smart beamforming. We implemented a Software Defined Radio (SDR) testbed based on Universal Software Radio Peripheral (USRP) and LabVIEW, which can provide a cost effective solution for prototyping wireless communication systems. First, the major challenge for the streaming high quality video is high data rate. In our proposed system, the high data rate up to 56 Mbps is obtained with the directional beamforming transmission/reception. Second, packet efficiency, i.e., successful packet reception of the system should be high. In our proposed system, the packet efficiency up to 98.56% is achieved of by our developed algorithm. Third, the frame rate of the video transmission, which should be high, is attained at 30 fps in our implementation. The high power at the receiver is achieved by the use of the directional antenna. The experimental results show that the Inter Symbol Interference (ISI) is reduced to the great extent. Furthermore, the proposed solution is investigated by using high-order modulation techniques like 64-QAM and 256-QAM.

Owing to the characteristics of underwater communication, such as limited bandwidth, low transmission speed, and long delivery delay, it is significantly challenging to address trust management in the Internet of Underwater Things (IoUT). In the process of trust calculation, trust judgments between nodes may be conflicting based on the obtained diverse trust evidences. However, in existing studies, the analysis of trust-conflict adjudication is nonexhaustive and lacking in detail. Therefore, a controversy-adjudication method is proposed in this study to handle conflict recommendations, and a novel trust management mechanism is further investigated based on the controversy-adjudication method, including three phases: 1) trust calculation; 2) trust recommendation; and 3) trust evaluation. First, trust evidences, e.g., packet delivery ratio, end-to-end packet transmission latency, and residual energy, are collected to calculate trust for trustees. In addition, for the trustor without sufficient trust evidences, recommendations are required and an incentive mechanism is proposed based on the prisoner’s dilemma to encourage neighbors to participate in trust recommendation. Finally, trust values of trustees are obtained by executing trust evaluation based on the controversy-adjudication mechanism. Simulation results demonstrate that the proposed trust management mechanism outperforms existing related works in terms of accuracy and robustness against unreliable IoUT.

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.