Underwater Environmental Monitoring Research Articles

Redefining Accuracy: Underwater Depth Estimation for Irregular Illumination Scenes.

Acquiring underwater depth maps is essential as they provide indispensable three-dimensional spatial information for visualizing the underwater environment. These depth maps serve various purposes, including underwater navigation, environmental monitoring, and resource exploration. While most of the current depth estimation methods can work well in ideal underwater environments with homogeneous illumination, few consider the risk caused by irregular illumination, which is common in practical underwater environments. On the one hand, underwater environments with low-light conditions can reduce image contrast. The reduction brings challenges to depth estimation models in accurately differentiating among objects. On the other hand, overexposure caused by reflection or artificial illumination can degrade the textures of underwater objects, which is crucial to geometric constraints between frames. To address the above issues, we propose an underwater self-supervised monocular depth estimation network integrating image enhancement and auxiliary depth information. We use the Monte Carlo image enhancement module (MC-IEM) to tackle the inherent uncertainty in low-light underwater images through probabilistic estimation. When pixel values are enhanced, object recognition becomes more accessible, allowing for a more precise acquisition of distance information and thus resulting in more accurate depth estimation. Next, we extract additional geometric features through transfer learning, infusing prior knowledge from a supervised large-scale model into a self-supervised depth estimation network to refine loss functions and a depth network to address the overexposure issue. We conduct experiments with two public datasets, which exhibited superior performance compared to existing approaches in underwater depth estimation.

Redefining Accuracy: Underwater Depth Estimation for Irregular Illumination Scenes

Acquiring underwater depth maps is essential as they provide indispensable three-dimensional spatial information for visualizing the underwater environment. These depth maps serve various purposes, including underwater navigation, environmental monitoring, and resource exploration. While most of the current depth estimation methods can work well in ideal underwater environments with homogeneous illumination, few consider the risk caused by irregular illumination, which is common in practical underwater environments. On the one hand, underwater environments with low-light conditions can reduce image contrast. The reduction brings challenges to depth estimation models in accurately differentiating among objects. On the other hand, overexposure caused by reflection or artificial illumination can degrade the textures of underwater objects, which is crucial to geometric constraints between frames. To address the above issues, we propose an underwater self-supervised monocular depth estimation network integrating image enhancement and auxiliary depth information. We use the Monte Carlo image enhancement module (MC-IEM) to tackle the inherent uncertainty in low-light underwater images through probabilistic estimation. When pixel values are enhanced, object recognition becomes more accessible, allowing for a more precise acquisition of distance information and thus resulting in more accurate depth estimation. Next, we extract additional geometric features through transfer learning, infusing prior knowledge from a supervised large-scale model into a self-supervised depth estimation network to refine loss functions and a depth network to address the overexposure issue. We conduct experiments with two public datasets, which exhibited superior performance compared to existing approaches in underwater depth estimation.

Intelligent Bayesian regularization backpropagation neuro computing paradigm for state features estimation of underwater passive object

In underwater environments, the accurate estimation of state features for passive object is a critical aspect of various applications, including underwater robotics, surveillance, and environmental monitoring. This study presents an innovative neuro computing approach for instantaneous state features reckoning of passive marine object following dynamic Markov chains. This paper introduces the potential of intelligent Bayesian regularization backpropagation neuro computing (IBRBNC) for the precise estimation of state features of underwater passive object. The proposed paradigm combines the power of artificial neural network with Bayesian regularization technique to address the challenges associated with noisy and limited underwater sensor data. The IBRBNC paradigm leverages deep neural networks with a focus on backpropagation to model complex relationships in the underwater environment. Furthermore, Bayesian regularization is introduced to incorporate prior knowledge and mitigate overfitting, enhancing the model’s robustness and generalization capabilities. This dual approach results in a highly adaptive and intelligent system capable of accurately estimating the state features of passive object in real-time. To evaluate the efficacy of this intelligent computing approach, a controlled supervised maneuvering trajectory for underwater passive object is constructed. Real-time estimations of location, velocity, and turn rate for dynamic target are scrutinized across five distinct scenarios by varying the Gaussian observed noise’s standard deviation, aiming to minimize mean square errors (MSEs) between real and estimated values. The effectiveness of the proposed IBRBNC paradigm is demonstrated through extensive simulations and experimental trials. Results showcase its superiority over traditional nonlinear filtering methods like interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model unscented Kalman filter (IMMUKF), especially in the presence of noise, incomplete measurements and sparse data.

Remote 3D Imaging and Classification of Pelagic Microorganisms with A Short‐Range Multispectral Confocal LiDAR

AbstractPlankton is essential to maintain healthy aquatic ecosystems since it influences the biological carbon pump globally. However, climate change‐induced alterations to oceanic properties threaten planktonic communities. It is therefore crucial to monitor their abundance to assess the health status of marine ecosystems. In situ optical tools unlock high‐resolution measurements of sub‐millimeter specimens, but state‐of‐the‐art underwater imaging techniques are limited to fixed and small close‐range volumes, requiring the instruments to be vertically dived. Here, a novel scanning multispectral confocal light detection and ranging (LiDAR) system for short‐range volumetric sensing in aquatic media is introduced. The system expands the inelastic confocal principle to multiple wavelength channels, allowing the acquisition of 4D point clouds combining near‐diffraction limited morphological and spectroscopic data that is used to train artificial intelligence (AI) models. Volumetric mapping and classification of microplastics is demonstrated to sort them by color and size. Furthermore, in vivo autofluorescence is resolved from a community of free‐swimming zooplankton and microalgae, and accurate spectral identification of different genera is accomplished. The deployment of this photonic platform alongside AI models overcomes the complex and subjective task of manual plankton identification and enables non‐intrusive sensing from fixed vantage points, thus constituting a unique tool for underwater environmental monitoring.

Optical Design and Stray Light Analysis of Underwater Spectral Radiometer

Underwater spectral detection plays an important role in the study of the underwater environment, ecology, oceanography, and environmental monitoring. A kind of underwater spectral radiometer that can observe the distribution of underwater spectral radiation bidirectionally has been developed. The flat-field concave holographic grating is used as the only optical component, the optical design parameters are optimized, and the system is miniaturized. The mechanism of stray light generation in the spectrometer is studied, and a method of suppressing stray light is proposed and simulated. The level of stray light in the system is suppressed to the order of 10−4, the signal-to-noise ratio of the system is improved, and the ability to detect weak light is enhanced. The wavelength calibration experiment was completed, and the experimental results show that the wavelength resolution of the underwater spectral radiometer is better than 3 nm. Finally, the quantitative relationship between spectral irradiance and digital output is obtained through radiation in the calibration of the system.

Marine Object Detection using YOLOv4 Adapted Convolutional Neural Network

This research presents an innovative application of the YOLOv4 object detection model for the identification and classification of marine objects within a dataset encompassing seven distinct classes. The study focuses on enhancing the robustness and accuracy of object detection in challenging marine environments, leveraging the unique capabilities of YOLOv4. Pre-processing steps involve resizing raw images, applying data augmentations, and normalizing pixel values to ensure optimal model training. Specifically tailored for underwater scenarios, additional color space transformations address variations in lighting conditions. The model is trained to detect marine objects such as fish, corals, and underwater structures, contributing to advancements in underwater exploration, environmental monitoring, and marine resource management. Experimental results demonstrate the effectiveness of the proposed approach, showcasing YOLOv4's ability to accurately identify and classify marine objects across the specified seven classes. This research not only expands the applicability of YOLOv4 in the marine domain but also provides valuable insights for the development of intelligent systems for underwater object detection.

Design and Implementation of a Multi-Function Hydrophone for Underwater Acoustic Application

In recent years, underwater acoustic applications have attracted much attention, for example, for underwater environmental monitoring, underwater exploration, etc. Hydrophones play a particularly important role. Although hydrophone design has been in multifarious application forms, it still needs to consider increasing demand for low-cost, low-consumption, and multiple-function devices, as well as issues around miniaturization, lossless data collection, etc. In this paper, we design a compact underwater acoustic device that has the capability of underwater acoustic signal storage, underwater acoustic signal transmission via the Internet, and decoding based on the direct sequences spread spectrum (DSSS). The key problem is how to implement multiple functions in only one micro-controller unit (MCU). The hardware and software of the proposed multi-function hydrophone are described in detail. In particular, the MCU, the pre-amplifier with gain control, and the analog-to-digital integrated chip are introduced. Moreover, underwater acoustic data storage, underwater acoustic transmission, and the DSSS receiver are depicted in terms of software. The different functions of the hydrophone are verified in sea trial experiments. The results show that the proposed multi-function hydrophone is able to sample underwater acoustic data at high quality. In addition, to demonstrate configurable parameters, the DSSS receiver with different carrier frequencies is provided. The proposed multi-function hydrophone realizes zero bit error rate (BER) when carrier frequency fc=9 kHz, and the BER with 10−3 order of magnitude when carrier frequency fc=15.5 kHz. The results show that the proposed multi-function hydrophone has great potential to explore the ocean.

Artificial Muscles for Underwater Soft Robots: Materials and Their Interactions

Underwater soft robots are typically constructed from soft and flexible materials, which enable them to adapt to aquatic environments where the terrain can be complex. They are often inspired by soft-bodied aquatic animals and can be used for a range of tasks, such as underwater exploration, environmental monitoring, and rescue operations. However, the design of these robots presents significant challenges, as it requires soft materials and systems that can withstand the harsh and varied conditions of ocean environments. This review delves into the physics of soft materials and outlines the constitutive models for such materials. Through an exploration of the muscle structures in aquatic creatures like octopuses and stingrays, we highlight the interplay between the materials that make up artificial muscles and how these muscles interact with their external surroundings. Finally, we conclude by outlining unresolved challenges and providing potential avenues for future research.

Node deployment optimization of underwater wireless sensor networks using intelligent optimization algorithm and robot collaboration

This study aims to optimize the node deployment of underwater wireless sensor networks (UWSNs) using intelligent optimization algorithms and robot collaboration technology to enhance network performance and coverage. The study employs the chemical reaction optimization (CRO) algorithm, which combines the advantages of genetic algorithms, simulated annealing algorithms, and ant colony algorithms. The CRO algorithm is enhanced through a structure correction function to determine the optimal node deployment scheme to achieve effective and optimal coverage control of the UWSN. Additionally, the flexibility and autonomy of robots are leveraged to improve the efficiency of node deployment and address the unique challenges posed by the underwater environment. Furthermore, the study conducts a comparative analysis of different intelligent optimization algorithms and demonstrates the effectiveness and advantages of the enhanced CRO algorithm in optimizing node deployment for UWSNs. The study findings reveal that the improved algorithm achieves an average coverage rate of 95.66%, significantly outperforming traditional intelligent optimization algorithms. The coverage of UWSNs can be significantly improved by utilizing the enhanced CRO algorithm and robot collaboration technology for node deployment optimization, which offers an effective approach for achieving optimal node deployment. Moreover, the rational deployment of nodes enhances the monitoring capability, resource utilization efficiency, and accuracy of environmental monitoring in underwater networks. The results of this study hold great practical significance for underwater environment monitoring, marine resource exploration, and marine scientific research.

DSW-YOLOv8n: A New Underwater Target Detection Algorithm Based on Improved YOLOv8n

Underwater target detection is widely used in various applications such as underwater search and rescue, underwater environment monitoring, and marine resource surveying. However, the complex underwater environment, including factors such as light changes and background noise, poses a significant challenge to target detection. We propose an improved underwater target detection algorithm based on YOLOv8n to overcome these problems. Our algorithm focuses on three aspects. Firstly, we replace the original C2f module with Deformable Convnets v2 to enhance the adaptive ability of the target region in the convolution check feature map and extract the target region’s features more accurately. Secondly, we introduce SimAm, a non-parametric attention mechanism, which can deduce and assign three-dimensional attention weights without adding network parameters. Lastly, we optimize the loss function by replacing the CIoU loss function with the Wise-IoU loss function. We named our new algorithm DSW-YOLOv8n, which is an acronym of Deformable Convnets v2, SimAm, and Wise-IoU of the improved YOLOv8n(DSW-YOLOv8n). To conduct our experiments, we created our own dataset of underwater target detection for experimentation. Meanwhile, we also utilized the Pascal VOC dataset to evaluate our approach. The mAP@0.5 and mAP@0.5:0.95 of the original YOLOv8n algorithm on underwater target detection were 88.6% and 51.8%, respectively, and the DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 can reach 91.8% and 55.9%. The original YOLOv8n algorithm was 62.2% and 45.9% mAP@0.5 and mAP@0.5:0.95 on the Pascal VOC dataset, respectively. The DSW-YOLOv8n algorithm mAP@0.5 and mAP@0.5:0.95 were 65.7% and 48.3%, respectively. The number of parameters of the model is reduced by about 6%. The above experimental results prove the effectiveness of our method.

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.

Energy‐efficient selection of relay for UWSNs in the Internet of underwater things

SummaryInternet of underwater things (IoUT) for underwater monitoring is known worldwide for smart interlinked underwater things that exhibit the capacity to monitor the vast unexplored waters of the oceans. Concept of IoUT has been derived from Internet of Things (IoT) in order to acquire the exquisite benefits of networking in underwater environment. IOT standards and technologies do not work well in underwater environment, such as infrared, Wi‐Fi, and radio frequency (RF) due to high channel errors and limited range up to few meters. Acoustic waves, however, can be used to communicate both in shallow and deep oceans due to their low frequency (kHZ) signal. In context of IoUT, communication based on acoustic links enables different applications such as underwater exploration, environmental monitoring, and disaster prevention even without availability of GPS facility like free space environment. In unpredictable and changing underwater environment, energy efficiency becomes a major challenge during data routing along multiple devices. Batteries of the sensor nodes, autonomous underwater vehicle (AUV), and remotely operated vehicle (ROV) cannot be removed with easiness and difficult to recharge, and the only way out is efficient sensor node selection for relaying to save massive amount of energy. Energy aware channel routing protocol (ECARP) does not consider the depth of the node while selecting the relay nodes to forward the data. Relay node selection in underwater Internet of things (IOUT) is a primary problem addressed in this research based on channel state information (CSI) for establishing best path to relay information among IOUT devices. Our major focus was to develop better technique for the relay node selection using a CSI and select relay node by looking at its depth from ocean surface and residual energy in the proposed ED‐CARP. Simulation results validate that proposed ED‐CARP can decrease the communication cost and increase the network lifetime.

Chaotic map based multi-factor authentication protocol for underwater environment monitoring

Chaotic map based multi-factor authentication protocol for underwater environment monitoring

Design and Implementation of a Miniature Jellyfish-Inspired Robot

With the development of the global marine industry, the demand to investigate underwater environments using robots is increasing. Inspired by the unique structure and excellent swimming ability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Aurelia aurita</i> , a miniature jellyfish-inspired robot is developed. The robot comprises a headshell, six tentacles, a primary drive component, two flywheel steering components, a lightweight foam block, a dsPIC control system, and an energy supply component. The tentacles are covered with a 0.1-mm-thick silicone membrane and modeled in the shape of an umbrella. The membrane is driven by a gear motor to mimic the contraction and expansion of the interior cavity of a jellyfish; it exhibits the swimming behavior via water jet propulsion. Three-dimensional swimming is achieved by the flywheel steering components following the conservation of angular momentum. A built-in LoRa communication module (433 MHz) enables a robust, real-time connection between the robot and a terrestrial console. The small-sized, lightweight jellyfish robot has a diameter of 110 mm and a height of 140 mm; it weighs 318 g and achieves autonomous swimming with low power consumption. The robot can be used for underwater signal relay, environmental monitoring, and geological investigations.

An Underwater Target Tracking Algorithm Based on Extended Kalman Filter

The technology of ocean monitoring is more advanced while the continuous development of industrial Internet. Unmanned underwater vehicle (UUV) is one of major ways for underwater environment monitoring, which makes high-precision positioning, and tracking of it is one of the key problems and needs to be solved urgently. An underwater acoustic positioning and tracking algorithm based on multiple beacons is proposed to reduce the positioning error of underwater acoustic positioning system caused by uncertain sound speed. The system consists of multiple GPS intelligent buoys floated on the sea surface and acoustic signal generator installed on the UUV. The effective sound speeds between the UUV and different buoys are considered to be unequal and estimated as the state parameters, together with the kinematic parameters of the UUV. Based on the kinematic equations of the UUV, the tracking model is obtained under the framework of the extended Kalman filter. Simulation results show that the proposed algorithm can correct the sound speed and improve the stability and accuracy of underwater acoustic positioning system.

Drift Reduction in Underwater Egomotion Computation by Axial Camera Modeling

Localization of autonomous underwater vehicles is essential for underwater exploration, environmental monitoring, and infrastructure inspections, as only a few examples. As wirelessly transmitted data cannot be used in underwater navigation, and equivalent sensors are costly and not readily deployable, the use of cameras as a localization aid offers an attractive alternative. Nonetheless, the utilization of cameras for underwater navigation is hindered by medium-induced visual deterioration and refraction of the incident ray. While most research has been focusing on addressing the radiometric deterioration effects, we demonstrate that the introduction of a physically aware model of the refraction effect can significantly improve the platform localization. Specifically, we develop a refraction-aware continuous formulation of the egomotion model, which computes the translational and rotational velocities using optical flow measurements. We also demonstrate that a linear model can be reached despite the nonlinear ray's path. Results show an improved navigation solution echoed by a significant reduction in the drift with more than tenfold improvement in the estimated position and angular quantities compared to state-of-the-art methods. They demonstrate the benefit of exact modeling in underwater navigation.

A survey on opportunistic routing protocols in the Internet of Underwater Things

Compared with traditional ocean monitoring technologies, such as cable seabed observation networks, the Internet of Underwater Things (IoUT) can provide real-time underwater environment monitoring and improve efficiency of data collection. Data collection is one of the most essential functions of IoUT, while it is very challenging to achieve efficient data transmission in IoUT. Opportunistic routing is considered to have great advantages and potential in mitigating the drawbacks of underwater communication and improving the performance of data transmission. Therefore, in this paper, the opportunistic routing protocols for IoUT are reviewed and summarized. Taking advantage of the broadcast characteristic of wireless underwater communication, opportunistic routing selects a group of suitable neighbors as potential forwarders instead of selecting only one or a few specific next-hop nodes as in conventional routing, thus improving the success rate of data packet delivery. Critical to routing performance is which nodes participate in data forwarding as forwarders. The nodes act as forwarders are determined by user-defined routing parameters, which are referred to as route metrics in this paper. First, route metrics of opportunistic routing are analyzed and a comprehensive classification of opportunistic routing protocols is discussed based on the route metrics. Then, opportunistic routing protocols for IoUT are further investigated and compared in terms of the adopted communication technique, data delivery ratio, transmission delay efficiency, energy efficiency, buffer efficiency, reliability, etc. Based on the comprehensive classification and comparison, existing problems and future research issues regarding opportunistic routing in IoUT are finally presented.

Hydrophobic and multifunctional strain, pressure and temperature sensor based on TPU/SiO2-ILs ionogel for human motion monitoring, liquid drop monitoring, underwater applications

Ionogel-based flexible electronic devices have attracted extensive attention from industry and academia due to their unique advantages in wearable electronic devices, human motion sensing, etc. However, the internal ionic liquids (ILs) or polymers have weak resistance to liquid interference, which limits the development of ionogel in the actual environment. In this paper, TPU/SiO2-ILs (Thermoplastic polyurethane/SiO2-Ionic liquids) ionogel with micro-nano structures on the surface was constructed by sandpaper template method and further modified by 1 H, 1 H, 2 H, 2 H-perfluorooctyltrichlorosilane to enhance its hydrophobicity. The ionogel sensor has a high sensitivity, a fast strain response time (346 ms), and excellent mechanical properties. In addition, the sensor can still show a stable response signal after 1000 s of strain and compression cycle testing. The ionogel sensor’s excellent hydrophobic properties allow it to be used not only in wearable electronic devices to detect human physiological activity in dry or underwater environments and for writing recognition systems, but also to monitor tiny response signals caused by toy fish wiggling and droplet dripping. Most importantly, compared with other sensors that can only monitor a single stimulus, the TPU/SiO2-ILs ionogel sensor has a wide and multifunctional detection range for stimuli in different environmental such as strain (0.1–200%), pressure (0.6–25 kPa) and temperature (30–100 °C). Thus, the TPU/SiO2-ILs ionogel shows great potential for applications in areas such as smart electronic skin, human motion monitoring, underwater communication and underwater environmental monitoring.

Guest Editorial: Emerging Trends and Challenges in Internet-of-Underwater-Things

Internet-of-Underwater-Things (IoUT) is a network of smart interconnected underwater objects. Recently, IoUT has shown its applicability in numerous scenarios such as marine life monitoring, off-shore exploration, underwater lost-treasure discovery, sports, assisted navigation, location awareness, environmental monitoring (e.g., monitoring of water quality, water pollution, water pressure or water temperature), water-based disasters (e.g., tsunami or nuclear accident), defense systems (e.g., surveillance systems or submarine detection) and several other applications. Even though IoUT systems are key enabler in underwater communication, they face challenges due to unreliable transmission medium, unstable radio signals, limited range, low bandwidth, inborn noise, low transmission rate, slow propagation speed, node mobility, lower resources and limited battery capacity. These challenges cause issues in channel modeling, optimal routing, security, privacy, communication overhead, congestion control, packet error rate, packet latency, energy consumption etc. In this respect, this Special Issue (SI) aims to address some of the aforementioned challenges.

Review on energy-saving key technologies of underwater bionic robot swarm

Underwater Bionic Robot swarm has the advantages of high redundancy, flexible mobility and wide range of task execution, which can be applied to underwater environment monitoring, underwater target monitoring, resource exploration and other fields. For a long time, the problem of energy consumption is the focus on the application of Underwater Bionic Robot swarm engineering. This article draws on the sports advantages of biological clusters, from the perspective of hydrodynamic analysis, summarizes the domestic and foreign research status of key energy-saving technologies of underwater bionic robot clusters. The main content includes a summary on the development status of the three key technologies of biological cluster movement energy saving mechanism, underwater bionic robot vortex field information perception and cluster cooperative control, and analysis and summary of the development trend of key energy-saving technologies of underwater bionic robot cluster.