Search Results (42)

Efficient and timely data collection in Underwater Acoustic Sensor Networks (UASNs) for Internet of Underwater Things (IoUT) applications remains a significant challenge due to the inherent limitations of the underwater environment. This paper presents a Value of Information (VoI)-based trajectory planning framework for a single Autonomous Underwater Vehicle (AUV) operating in coordination with an Unmanned Surface Vehicle (USV) to collect data from multiple Cluster Heads (CHs) deployed across an uneven seafloor. The proposed approach employs a VoI model that captures both the importance and timeliness of sensed data, guiding the AUV to collect and deliver critical information before its value significantly degrades. A forward Dynamic Programming (DP) algorithm is used to jointly optimize the AUV’s trajectory and the USV’s start and end positions, with the objective of maximizing the total residual VoI upon mission completion. The trajectory design incorporates the AUV’s kinematic constraints into travel time estimation, enabling accurate VoI evaluation throughout the mission. Simulation results show that the proposed strategy consistently outperforms conventional baselines in terms of residual VoI and overall system efficiency. These findings highlight the advantages of VoI-aware planning and AUV–USV collaboration for effective data collection in challenging underwater environments. 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

The Internet of Underwater Things (IoUT) has attracted significant attention from researchers due to the fact that seventy percent of the Earth’s surface is covered by water. Reliable underwater communication is the enabler of IoUT. Different carriers, such as electromagnetic waves, sound, and light, are used to transmit data through the water. Among these, optical waves are considered promising due to their high data rates and relatively good bandwidth efficiency, as water becomes transparent to light in the visible spectrum (400–700 nm). However, limitations such as link range, path loss, and turbulence lead to low power and, consequently, a low signal-to-noise ratio (SNR) at the receiver. In this article, we present the design of a smart transceiver for bidirectional communication. The system adapts the divergence angle of the optical beam from the transmitter based on the power of the signal received. This paper details the real-time data transmission process, where the transmitting station consists of a light fidelity (Li-Fi) transmitter with a 470 nm blue-light-emitting diode (LED) and a software-defined radio (SDR) for underwater optical communication. The receiving station is equipped with a Li-Fi receiver, which includes a photodetector with a wide field of view and an SDR. Furthermore, we use pulse position modulation (PPM), which demonstrates promising results for real-time transmission. A key innovation of this paper is the integration of the Li-Fi system with the SDR, while the system adapts dynamically using a servo motor and an Arduino microcontroller assembly. The experimental results show that this approach not only increases throughput but also enhances the robustness and efficiency of the system. Full article

The Internet of Underwater Things (IoUT) holds significant promise for developing a smart ocean. In recent years, there has been swift progress in data collection methods using autonomous underwater vehicles (AUVs) within underwater acoustic sensor networks (UASNs). One of the key challenges in the IoUT is improving both the energy consumption (EC) of underwater vehicles and the value of information (VoI) necessary for completing missions while gathering sensing data. In this paper, a hybrid optimization technique is proposed based on boundary tour modified ant colony optimization (BTMACO). The proposed optimization algorithm was developed to solve the challenging problem of determining the optimal path of an AUV visiting all sensor nodes with minimum energy consumption. The optimization algorithm specifies the best order in which to visit all the sensor nodes, while it also works to adjust the AUV’s information-gathering locations according to the permissible data transmission range. Compared with the related works in the literature, the proposed method showed better performance, and it can find the best route through which to collect sensor information with minimum power consumption and a 6.9% better VoI. 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

This review examines the integration of blockchain technology with the IoT in the Marine Internet of Things (MIoT) and Internet of Underwater Things (IoUT), with applications in areas such as oceanographic monitoring and naval defense. These environments present distinct challenges, including a limited communication bandwidth, energy constraints, and secure data handling needs. Enhancing BIoT systems requires a strategic selection of computing paradigms, such as edge and fog computing, and lightweight nodes to reduce latency and improve data processing in resource-limited settings. While a blockchain can improve data integrity and security, it can also introduce complexities, including interoperability issues, high energy consumption, standardization challenges, and costly transitions from legacy systems. The solutions reviewed here include lightweight consensus mechanisms to reduce computational demands. They also utilize established platforms, such as Ethereum and Hyperledger, or custom blockchains designed to meet marine-specific requirements. Additional approaches incorporate technologies such as fog and edge layers, software-defined networking (SDN), the InterPlanetary File System (IPFS) for decentralized storage, and AI-enhanced security measures, all adapted to each application’s needs. Future research will need to prioritize scalability, energy efficiency, and interoperability for effective BIoT deployment. Full article

This research introduces a hybrid approach combining bio- and nature-inspired metaheuristic algorithms to enhance scheduling efficiency and minimize energy consumption in Underwater Acoustic Sensor Networks (UASNs). Five hybridized algorithms are designed to efficiently schedule nodes, reducing energy costs compared to existing methods, and addressing the challenge of unscheduled nodes within the communication network. The hybridization techniques such as Elephant Herding Optimization (EHO) with Genetic Algorithm (GA), Firefly Algorithm (FA), Levy Firefly Algorithm (LFA), Bacterial Foraging Algorithm (BFA), and Binary Particle Swarm Optimization (BPSO) are used for optimization. To implement these optimization techniques, the Scheduled Routing Algorithm for Localization (SRAL) is introduced, aiming to enhance node scheduling and localization. This framework is crucial for improving data delivery, optimizing Route REQuest (RREQ) and Routing Overhead (RO), while minimizing Average End-to-End (AE2E) delays and localization errors. The challenges of node localization, RREQ reconstruction at the beacon level, and increased RO, along with End-to-End delays and unreliable data forwarding, have a significant impact on overall communication in underwater environments. The proposed framework, along with the hybridized metaheuristic algorithms, show great potential in improving node localization, optimizing scheduling, reducing energy costs, and enhancing reliable data delivery in the Internet of Underwater Things (IoUT)-based network. Full article

The rapid growth of marine industries has emphasized the focus on environmental impacts for all industries, as well as the influence of key environmental parameters on, for instance, offshore wind or aquaculture performance, animal welfare and structural integrity of different constructions. Development of automatized sensors together with efficient communication and information systems will enhance surveillance and monitoring of environmental processes and impact. We have developed a modular Smart Ocean observatory, in this case connected to a large-scale marine aquaculture research facility. The first sensor rigs have been operational since May 2022, transmitting environmental data in near real-time. Key components are Acoustic Doppler Current Profilers (ADCPs) for measuring directional wave and current parameters, and CTDs for redundant measurement of depth, temperature, conductivity and oxygen. Communication is through 4G network or cable. However, a key purpose of the observatory is also to facilitate experiments with acoustic wireless underwater communication, which are ongoing. The aim is to expand the system(s) with demersal independent sensor nodes communicating through an “Internet of Underwater Things (IoUT)”, covering larger areas in the coastal zone, as well as open waters, of benefit to all ocean industries. The observatory also hosts experiments for sensor development, biofouling control and strategies for sensor self-validation and diagnostics. The close interactions between the experiments and the infrastructure development allow a holistic approach towards environmental monitoring across sectors and industries, plus to reduce the carbon footprint of ocean observation. This work is intended to lay a basis for sophisticated use of smart sensors with communication systems in long-term autonomous operation in remote as well as nearshore locations. Full article

The Internet of Underwater Things (IoUT) represents an emerging and innovative field with the potential to revolutionize underwater exploration and monitoring. Despite its promise, IoUT faces significant challenges related to reliability and security, which hinder its development and deployment. A particularly critical issue is the establishment of trustworthy communication networks, necessitating the adaptation and enhancement of existing models from terrestrial and marine systems to address the specific requirements of IoUT. This work explores the problem of dishonest recommendations within trust modelling systems, a critical issue that undermines the integrity of trust modelling in IoUT networks. The unique environmental and operational constraints of IoUT exacerbate the severity of this issue, making current detection methods insufficient. To address this issue, a recommendation evaluation method that leverages both filtering and weighting strategies is proposed to enhance the detection of dishonest recommendations. The model introduces a filtering technique that combines outlier detection with deviation analysis to make initial decisions based on both majority outcomes and personal experiences. Additionally, a belief function is developed to weight received recommendations based on multiple criteria, including freshness, similarity, trustworthiness, and the decay of trust over time. This multifaceted weighting strategy ensures that recommendations are evaluated from different perspectives to capture deceptive acts that exploit the complex nature of IoUT to the advantage of dishonest recommenders. To validate the proposed model, extensive comparative analyses with existing trust evaluation methods are conducted. Through a series of simulations, the efficacy of the model in capturing dishonest recommendation attacks and improving the accuracy rate of detecting more sophisticated attack scenarios is demonstrated. These results highlight the potential of the model to significantly enhance the trustworthiness of IoUT establishments. Full article

Huge waves caused by typhoons often induce severe disasters along coastal areas, making the effective prediction of typhoon-induced waves a crucial research issue for researchers. In recent years, the development of the Internet of Underwater Things (IoUT) has rapidly increased the prediction of oceanic environmental disasters. Past studies have utilized meteorological data and feedforward neural networks (e.g., BPNN) with static network structures to establish short lead time (e.g., 1 h) typhoon wave prediction models for the coast of Taiwan. However, sufficient lead time for prediction remains essential for preparedness, early warning, and response to minimize the loss of lives and properties during typhoons. The aim of this research is to construct a novel long lead time typhoon-induced wave prediction model using Long Short-Term Memory (LSTM), which incorporates a dynamic network structure. LSTM can capture long-term information through its recurrent structure and selectively retain necessary signals using memory gates. Compared to earlier studies, this method extends the prediction lead time and significantly improves the learning and generalization capability, thereby enhancing prediction accuracy markedly. Full article

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions. Full article

Underwater localization is one of the key techniques for positioning, navigation, timing (PNT) services that could be widely applied in disaster warning, underwater rescues and resource exploration. One of the reasons why it is difficult to achieve accurate positioning for underwater targets is due to the influence of uneven distribution of underwater sound velocity. The current sound-line correction positioning method mainly aims at scenarios with known target depth. However, for nodes that are non-cooperative nodes or lack depth information, sound-line tracking strategies cannot work well due to non-unique positional solutions. To solve this problem, we propose an iterative ray tracing 3D underwater localization (IRTUL) method for stratification compensation. To demonstrate the feasibility of fast stratification compensation, we first derive the signal path as a function of initial |grazing angle, and then prove that the signal propagation time and horizontal propagation distance are monotonic functions of the initial grazing angle, which guarantees the fast achievement of ray tracing. Simulation results indicate that IRTUL has the most significant correction effect in the depth direction, and the average accuracy has been improved by about 3 m compared to a localization model with constant sound velocity. Full article

The development of intelligent oceans requires exploration and an understanding of the various characteristics of the oceans. The emerging Internet of Underwater Things (IoUT) is an extension of the Internet of Things (IoT) to underwater environments, and the ability of IoUT to be combined with deep learning technologies is a powerful technology for realizing intelligent oceans. The underwater acoustic (UWA) communication network is essential to IoUT. The thermocline with drastic temperature and density variations can significantly limit the connectivity and communication performance between IoUT nodes. To more accurately capture the complexity and variability of ocean remote sensing data, we first sample and analyze ocean remote sensing datasets and provide sufficient evidence to validate the temporal redundancy properties of the data. We propose an innovative deep learning approach called Ocean-Mixer. This approach consists of three modules: an embedding module, a mixer module, and a prediction module. The embedding module first processes the location and attribute information of the ocean water and then passes it to the subsequent modules. In the mixing module, we apply a temporal decomposition strategy to eliminate redundant information and capture temporal and channel features through a self-attention mechanism and a multilayer perceptron (MLP). The prediction module ultimately discerns and integrates the temporal and channel relationships and interactions among various ocean features, ensuring precise forecasting. Numerous experiments on ocean temperature and salinity datasets show that Mixer-Ocean performs well in improving the accuracy of time series prediction. Mixer-Ocean is designed to support multi-step prediction and capture the changes in the ocean environment over a long period, thus facilitating efficient management and timely decision-making for innovative ocean-oriented applications, which has far-reaching significance for developing and conserving marine resources. Full article

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. Full article

The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use of the autonomous underwater vehicles (AUV) as mobile collectors to build reliable collection systems, while the value of information (VoI) is used as the primary measure of information quality. This paper first builds a realistic model to characterize the behavior of sensor nodes and the AUV together with challenging environments. Then, improved deep reinforcement learning (DRL) is used to dynamically plan the AUV’s navigation route by jointly considering the location of nodes, the data value of nodes, and the status of the AUV to maximize the data collection efficiency of the AUV. The results of the simulation show the dynamic data collection scheme is superior to the traditional path planning scheme, which only considers the node location, and greatly improves the efficiency of AUV data collection. Full article