Internet Of Underwater Things Research Articles

Position Computation of Underwater Target Using Geodesic Curvature for Sub-Aquatic Internet of Underwater Things

To estimate the accurate position of the target, the geodesic curvature of the transmitter sensor is proposed in the present work. The geodesic curvature of the transmitter sensor traces the motion of a sensor as the shortest distance along a curved path, which measures the optimal distance on a curved path, accurate measurement of moving targets in an environment with complex geodesics, enhances the realism of the simulation, and underwater surveillance. Theorem 2.1 describes the geodesic curvature between the transmitter sensor and the target. A proposed non-cooperative moving target state measurement (NMTSM) algorithm quantifies the degradation of localization performance and mitigates the stratification effect and geodesic spreading losses. The localization error, localization error variance, and localization accuracy with respect to the number of anchor nodes are compared with standard methods. The proposed non-cooperative moving target state measurement (NMTSM) algorithm improves the localization performance by 33.46% and reduces position MSE by 35.46%, and velocity MSE by 38.86% up to 650 meters. It reduces the localization error by 32.23%, the localization error variance by 34.86% and improves the localization accuracy by 42.21%. The proposed algorithm is useful for the sub-aquatic Internet of Underwater Things (IoUT).

Fractional Optimal Control for Malware Propagation in Internet of Underwater Things

Fractional Optimal Control for Malware Propagation in Internet of Underwater Things

Hybrid-Algorithm-Based Full Coverage Search Approach With Multiple AUVs to Unknown Environments in Internet of Underwater Things

Hybrid-Algorithm-Based Full Coverage Search Approach With Multiple AUVs to Unknown Environments in Internet of Underwater Things

Ocean-Mixer: A Deep Learning Approach for Multi-Step Prediction of Ocean Remote Sensing Data

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.

Optimal Data Collection Path Finding for AUV in Internet of Underwater Things

Optimal Data Collection Path Finding for AUV in Internet of Underwater Things

Internet of Underwater Things: A Survey on Simulation Tools and 5G-Based Underwater Networks

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.

Performance analysis of AUV-carried RISs-aided multihop UWOC convergent with RF MRC systems over WGG oceanic turbulence

To establish reliable connection between Internet of Underwater Things (IoUT) devices and terrestrial data centers, this work first proposes a reconfigurable intelligent surface (RIS) aided multihop underwater wireless optical communication (UWOC) convergent with radio frequency (RF) uplink system. Specifically, the RIS carried by an autonomous underwater vehicle (AUV) is introduced into UWOC link to relax the line-of-sight (LOS) requirement and the maximal-ratio combining (MRC) receiver is adopted at the terrestrial data center to mitigate the RF link fading. It is assumed that the underwater thermocline channel is subject to the newly proposed Weibull-generalized gamma (WGG) turbulence distribution and the RF link composite fading follows Fisher-Snedecor F distribution. Additionally, the optical link misalignment is characterized by the zero-boresight pointing errors model. With the decode-and-forward (DF) relaying scheme, the analytical closed-form expressions of the outage probability (OP) and average bit error rate (ABER) of this system are mathematically achieved, and then the impacts of air bubbles, thermohaline gradient, the number of RIS elements, pointing errors, system structure, and the number of receive antennas are further investigated. Meanwhile, the analytical results are verified by Monte Carlo (MC) simulations. Results reveal that this hybrid system performance would degrade with the increased air bubble levels and thermohaline gradients. Notably, RIS can effectively alleviate the impact of underwater turbulence and this effect would be more pronounced as the number of RIS elements increases. This work will benefit the design and research of hybrid UWOC-RF system.

An AUV-Assisted Data Gathering Scheme Based on Deep Reinforcement Learning for IoUT

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.

Coverage Enhancement of Light-Emitting Diode Array in Underwater Internet of Things over Optical Channels

The construction of the underwater Internet of Things (UIoT) is crucial to marine resource development, environmental observation, and tactical surveillance. The underwater optical wireless communication (UOWC) system with its large bandwidth and wide coverage facilitates the high-capacity information interconnection within the UIoT networks over short and medium ranges. To enhance the coverage characteristics of the UOWC system, an optimized lemniscate-compensated layout of light-emitting diode (LED) array is proposed in this paper, which can ameliorate the received optical power and reliability at the receiving terminal. Compared with traditional circular and rectangular layouts, the received optical power and bit error rate (BER) performance of the proposed system are analyzed based on the Monte Carlo simulation method. The analysis results show that the proposed LED array achieves a smaller peak power deviation and mean square error of the received optical power under three typical seawater environments. Furthermore, the proposed LED-array scheme supports a better BER performance of the UOWC system. For example, in turbid seawater with a transmission depth of 9.5 m, the BER of the proposed LED array layout is 1 × 10−7, which is better than the BER of 3.5 × 10−6 and 1 × 10−4 under the other two traditional light source layouts.

Impact of moving target on underwater positioning by using state measurement

The localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) is inaccurate due to the various underwater forces (viscous, hydrodynamic forces, perturbation of underwater). The false measurements in the sensor network cause position errors and velocity errors which disrupt the localization of the moving target. A randomly fluctuated spillover effect is introduced in the present paper. The absorption losses generated due to the spillover effect cause false measurements of the moving target. Theorem 1 describes the genesis of these absorption losses and their consequences in UAWSN. The measurements from each moving target in the presence of absorption losses are formulated in the elliptical region. A joint probabilistic data association (JPDA) method is proposed to quantify the false measurements in the elliptical region. A moving target state estimation (MTSE) algorithm is proposed to eliminate the false measurements from the moving targets and to measure the localization of moving targets with the help of the propagation speed of targets. The theoretical measurements of position RMSE and velocity RMSE are verified with standard methods. The proposed MTSE method improves the localization performance of the moving targets by 29.42 % and reduces 32.16 % of position errors and 36.23 % of velocity errors up to 550 ​m. The proposed algorithm will be useful for the sub-aquatic Internet of underwater things (IoUT).

Energy Efficient CH Selection Scheme Based on ABC and Q-Learning Approaches for IoUT Applications

Nowadays, the Internet of Underwater Things (IoUT) provides many marine 5G applications. However, it has some issues with energy efficiency and network lifetime. The network clustering approach is efficient for optimizing energy consumption, especially for underwater acoustic communications. Recently, many algorithms have been developed related to clustering-based underwater communications for energy efficiency. However, these algorithms have drawbacks when considered for heterogeneous IoUT applications. Clustering efficiency in heterogeneous IoUT is influenced by the uniform distribution of cluster heads (CHs). As a result, conventional schemes are inefficient when CHs are arranged in large and dense nodes since they are unable to optimize the right number of CHs. Consequently, the clustering approach cannot improve the IoUT network, and many underwater nodes will rapidly consume their energies and be exhausted because of the large number of clusters. In this paper, we developed an efficient clustering scheme to effectively select the best CHs based on artificial bee colony (ABC) and Q-learning optimization approaches. The proposed scheme enables an effective selection of the CHs based on four factors, the residual energy level, the depth and the distance from the base station, and the signal quality. We first evaluate the most suitable swarm algorithms and their impact on improving the CH selection mechanism. The evaluated algorithms are generic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and ABC. Then, the ABC algorithm process is improved by using the Q-learning approach to improve the process of ABC and its fitness function to optimize the CH selection. We observed from the simulation performance result that an improved ABC-QL scheme enables efficient selection of the best CHs to increase the network lifetime and reduce average energy consumption by 40% compared to the conventional ABC.

Improved BINN-Based Underwater Topography Scanning Coverage Path Planning for AUV in Internet of Underwater Things

Deep understanding the special nature of underwater topography plays an important role for Internet of Underwater Things (IoUT). Nowadays, underwater topography scanning with Autonomous Underwater Vehicle (AUV) has been becoming the chief methodology of knowing Seabed topography and geomorphology. How to design topography scanning trajectory can be mathematically described as a full Coverage Path Planning (CPP) problem. In this paper, facing the complete coverage path planning problem of mobile AUV, a new strategy based on Bio-Inspired Neural Network (BINN) algorithm with improved activity value of each neuron is discussed in detail. The original activity value function in BINN is instead of a piecewise linear function to reduce computational complexity. In addition, to overcome traditional dead-zone problem, an A* path planning based dead-zone escape method along the shorter path as early as possible to the recently uncovered area is described in deep. Extensive simulation results and practical experiments verify the performance of proposed Improved Bio-Inspired Neural Network (IBINN in short) based algorithm.

Vortex‐induced vibration effects in underwater acoustic wireless sensor networks

AbstractA randomly fluctuated vortex‐induced vibration (VIV) affects the localization of moving targets in an underwater acoustic wireless sensor network (UAWSN) to cause an offset time and time synchronization error which disturbs the localization of the non‐cooperative moving target. Definition 1, Theorem 1 has been introduced in the present work to describe the genesis and consequences of the VIV effect in UAWSN. An elliptic localization introduces the direct and indirect path measurements of the moving target. The standard Crammer Rao lower bound parameter quantifies the VIV effect. The compensation of vortex‐induced vibrations (CVV) algorithm is proposed to quantify the degradation of localization performance and to estimate the position of the moving target. The theoretical measurements of position error, velocity error with respect to the variation of noise, number of receiver sensors, and probability of line of sight errors are compared to the standard methods. The proposed CVV algorithm improves the localization performance by 32.24% and reduces 34.36% of position errors, and 37.46% of velocity errors up to 600 m. It can be useful for the sub‐aquatic internet of underwater things.

Spectrally Efficient Non-Orthogonal Discrete Multi-Tone Transmission for Underwater Wireless Optical Communication With Low-Complexity High Performance ICI Mitigation

In this paper, a novel spectrally efficient non-orthogonal discrete multi-tone (NODMT) system is proposed for the bandwidth-starved underwater wireless optical communication (UWOC) to compensate for the insufficient bandwidth of components. Different from other non-orthogonal systems with complex-valued correlation matrices, the designed NODMT system has a real-valued correlation matrix, which is significant in reducing the complexity of decoders, e.g., the complexity of the proposed NODMT with iterative detection (ID) algorithm can be reduced by 50% as compared to the conventional non-orthogonal system with ID. However, in circumstances of higher spectrum efficiency (SE), the non-orthogonality is intensified and inter-carrier interference (ICI) becomes severer, leading to the sharp deterioration of ID decoding capability. The traditional sphere decoding (SD) algorithm cannot solve this issue because Cholesky decomposition only works with the positive definite matrix. Therefore, we propose a novel SD algorithm based on QR decomposition, named QRSD, which effectively compensates for the insufficient decoding capability of the ID algorithm in the case of higher SE. By combining ID and QRSD methods, over 40% complexity reduction and nearly identical BER performance with QRSD are achieved. Through a proof-of-concept UWOC experiment, a 3.31-Gbps NODMT transmission with up to 24.44% data rate promotion is demonstrated, and the feasibility / effectiveness of the proposed ID-QRSD algorithm in different turbidity water is verified. Moreover, we have experimentally realized 75-m/2.66-Gbps and 60-m/3.02-Gbps underwater transmission with 15% and 25% bandwidth savings, respectively. This UWOC system with high SE shows great potential in the bandwidth-limited UWOC and underwater internet of things (UIoT).

A Constellation Diagram Learning-Based Adaptive Sparse Nonorthogonal Wavelet Division Multiplexing for Sonar Image Underwater Acoustic Transmission

In underwater Internet of Things (IoT) applications, efficient and reliable transmission is demanding. Recently, the non-orthogonal multicarrier (MC) modulation is utilized as a promising spectrum-efficient solution in the UWA channel, where generally, the constellation mapping (CM) methods (such as 8-PSK and 16-QAM) are utilized to further increase the transmission rate. In this paper, we focus on the transmission of sonar images among IoT nodes and a constellation diagram learning-based adaptive sparse non-orthogonal wavelet division multiplexing (CDLA-SNOWDM) system is proposed to combat complex channel states between IoT nodes. We found that different CM methods contribute to unique time-frequency characteristics in constellation diagrams. We first utilize the time-frequency characteristics in designing the non-orthogonal subcarriers. The complex-valued CM signals are regarded as a new type of image. We introduce dictionary learning (DL) to non-orthogonal subcarrier designing, with the idea that designing CM signal adaptive non-orthogonal subcarriers can be regarded as designing an adaptive subcarrier dictionary. The CDLA-SNOWDM modulation is performed as a projection of the CM image to the subcarriers with an adaptive dictionary. The learned adaptive subcarriers can represent constellation diagrams more efficiently and improve transmission reliability. Both simulations and experiments show that the proposed scheme improved the reliability in transmitting sonar images over other orthogonal and non-orthogonal modulation schemes in UWA scenarios.

Hybrid harvesting of wind and wave energy based on triboelectric-piezoelectric nanogenerators

Triboelectric nanogenerator (TENG) is an emerging energy harvesting technology that can effectively convert various forms of environmental mechanical energy, including oceanic wind, wave energy, seawater temperature difference energy and others. However, the ocean environment is unstable and lacks a long-term steady state of energy. This leads to low energy harvesting efficiency for existing devices based on triboelectric nanogenerators. To solve this challenge, a hybrid harvesting device based on triboelectric-piezoelectric nanogenerators has been proposed to harvest both wind and wave energy. The device comprises three energy harvesting modules: wind energy harvesting triboelectric nanogenerator (WD-TENG), wind energy harvesting pizeoelectric nanogenerator (WD-PENG) and wave energy harvesting triboelectric nanogenerator (WE-TENG), all of which can work together or independently. This provides a significant improvement in the technical reliability of micro-nano energy harvesting technology in the ocean and other unstable environments. In a simulated environment, each module generates 3.975 mW, 1.160 mW and 0.2925 mW of electricity and the corresponding power density is 5.064 W/m3, 1.478 W/m3 and 1.092 W/m3. With the help of an energy collection circuit, the device powers LEDs, temperature and humidity sensors. This innovative solution offers new possibilities for self-sustaining ocean sensors and electronics, and may be widely applied in the next generation of the Internet of Underwater Things (IoUT).

SPACNet: A Simulation Platform of an Acoustic Cognitive Network

Originating from the concept of cognitive networks (CNs), which are becoming popular in wireless terrestrial communication scenarios, underwater acoustic cognitive networks (UACNs) are drawing more and more attention in the field of the Internet of Underwater Things (IoUT). However, as the implementation of cognitive mechanisms in underwater acoustic networks is different from that of wireless scenarios, it is impossible or difficult for traditional simulation platforms to carry out simulations of UACNs. There is a lack of specialized simulation tools in terms of UACNs. To enable the quantitative evaluation of the effectiveness and performance enhancement of a UACNs in an adverse underwater environment, a simulation platform of acoustic cognitive networks (SPACNet) was designed and investigated in this article. First, based on a state machine-based protocol programming framework, the SPACNet is capable of supporting the implementation of different state-transform types associated with cognitive networking protocols. Moreover, to facilitate the realization of cognitive function at comprehensive levels of signal, information, and link, an underwater acoustic channel model with an environmental parameter input is integrated in SPACNet to generate underwater environment-driven multiple-aspect behaviors. Moreover, a simplified collision model consisting of an environment factor, channel response, and node location is used to reduce the complexity of the simulation of UACNs signal reception. A simulation was carried out to verify the effectiveness of SPACNet in evaluating the cognitive capabilities of UACNs. Finally, a field UACNs experiment was performed to validate the general consistency between the conclusion obtained with the SPACNet-based simulation and that from the field test.

Submarine optical fiber communication provides an unrealized deep-sea observation network

Oceans are crucial to human survival, providing natural resources and most of the global oxygen supply, and are responsible for a large portion of worldwide economic development. Although it is widely considered a silent world, the sea is filled with natural sounds generated by marine life and geological processes. Man-made underwater sounds, such as active sonars, maritime traffic, and offshore oil and mineral exploration, have significantly affected underwater soundscapes and species. In this work, we report on a joint optical fiber-based communication and sensing technology aiming to reduce noise pollution in the sea while providing connectivity simultaneously with a variety of underwater applications. The designed multifunctional fiber-based system enables two-way data transfer, monitoring marine life and ship movement near the deployed fiber at the sea bottom and sensing temperature. The deployed fiber is equally harnessed to transfer energy that the internet of underwater things (IoUTs) devices can harvest. The reported approach significantly reduces the costs and effects of monitoring marine ecosystems while ensuring data transfer and ocean monitoring applications and providing continuous power for submerged IoUT devices.

An Opportunistic Routing Based on Directional Transmission in the Internet of Underwater Things

The Internet of Underwater Things (IoUT) has attracted a lot of attention because of its promising applications in underwater environmental monitoring; however, the characteristics of acoustic communication, e.g., long propagation delay and high attenuation, pose great challenges for efficient and reliable underwater data transmission. Currently, opportunistic routing is regarded as a key technology to improve the packet delivery ratio, because it can dynamically choose several forwarding nodes and leverage their cooperative forwarding to increase network throughput. However, choosing an excessive number of forwarding nodes may result in energy waste and lengthy communication delays. Therefore, an Opportunistic Routing based on Directional Transmission (ORDT) is studied to improve packet delivery timeliness and reliability and lower energy consumption. ORDT mainly contains three phases: forwarding area division, candidate forwarder selection, and candidate forwarder coordination. The forwarding region is first established based on directional transmission. Only neighbors located in the forwarding region can forward packets. Following that, candidate forwarders in the forwarding region are chosen depending on their forwarding capability. Additionally, the chosen candidate’s time of holding packets is specified so that they might collaborate to reduce redundant data transmission and transmit packets to gateway nodes. Then, an Autonomous Underwater Vehicle (AUV) is employed to gather packets from gateway nodes. Simulation findings demonstrate that ORDT performs better than existing opportunistic routing in terms of packet delivery success rate, transmission latency, and energy usage.

Trajectory Optimization for Target Localization Using Time Delays and Doppler Shifts in Bistatic Sonar-Based Internet of Underwater Things

Efficient target localization is critical to many marine applications in the Internet of Underwater Things (IoUT). Doppler effect becomes more predominant in the underwater environment when the relative speed of the moving object, i.e., the observer, to the signal propagation speed in water is much larger than that in the air. This along with the observer-target geometry will bring in a notable impact on the localization performance. In this paper, we derive the Cram´er–Rao lower bound (CRLB) and formulate the A-optimality criterion-based observer trajectory optimization problem to improve the localization performance based on time delay and Doppler shift measurements. We show that in the worst-case scenario, there will be a conflict between the non-accessible zone constraint and the observer dynamics constraint, which will lead to an erroneous result. To address this problem, we propose a warning zone-based augmented Lagrange multiplier method (ALMM) where the non-accessible zone constraint is relaxed to resolve the conflict and ensure the non-accessible requirement of the targeted zone is maintained. Performance evaluations are conducted through extensive simulations for different scenarios, and the results are compared to other methods with or without trajectory optimization. We demonstrate that trajectory optimization using time delays and Doppler shifts can greatly improve the target localization accuracy in underwater networks.