Marine Sensor Research Articles

High-performance cylindrical pendulum shaped triboelectric nanogenerators driven by water wave energy for full-automatic and self-powered wireless hydrological monitoring system

Abstract Water wave energy in the ocean, having the characteristics of inexhaustibility, large quantity and wide area, is one of the most prospective renewable energy sources for large-scale application. The emerging triboelectric nanogenerator (TENG) provides a more reliable method for effectively collecting low-frequency ocean wave energy. Here, a high-performance cylindrical pendulum shaped triboelectric nanogenerator (CP-TENG) with arched film structure is presented to harvest water wave energy. The CP-TENG consists of a stator and a rotator mainly and works in a sliding and freestanding mode. The introduction of arched film structure into the CP-TENG is the most innovative, which not only can increase the contact area very much compared with point contact or line contact or soft contact in previous TENGs, but also can decrease the friction force compared with the hard face-face contact in traditional rotational TENGs. Meanwhile, it can realize the two-direction motion of the rotator. The dynamic behavior of the CP-TENG is analyzed theoretically first, and then the optimization is done systematically. The experimental results indicate that the CP-TENG can effectively scavenge multi-direction vibration energy and convert it into electrical energy. When driven by a linear motor, its peak power and average power can reach 22.1 mW and 5.2 mW, respectively. Correspondingly, the peak power density and average power density is 28.2 W m−3 and 6.6 W m−3 respectively, which are 4.0 and 3.8 times of a typical ball-shell structured TENG. The demonstration shows that this high-performance CP-TENG not only can light up 2000 LEDs directly, but also power many small electronic devices. Moreover, a full-automatic hydrological monitoring and wireless data transmitting system were powered by a CP-TENG network successfully. Therefore, the CP-TENG network located in the sea surely has great application in harvesting large-scale blue energy and powering various marine sensors in the ocean in future.

Design, Characterization, and Test of a Versatile Single-Mode Power-Over-Fiber and Communication System for Seafloor Observatories

A power-over-fiber (PoF) and communication system for extending a cabled seafloor observatory is demonstrated in this contribution. The system allows the cabled seafloor observatory to be linked, through a single optical fiber, to a sensor node located 8 km away. The PoF system is based on an optical architecture in which power and data propagate simultaneously on the same single-mode fiber. The Raman scattering effect is exploited to amplify the optical data signals and leads to the minimization of the sensor node power consumption. Versatile low power electronic interfaces have been developed to ensure compatibility with a wide range of marine sensors. A low-consumption field-programmable gate array and an energy-efficient microcontroller are used to develop the electronic interfaces. For an electrical input power of 31 W, up to 190 mW is recovered at the sensor node while a data bitrate of up to 3.6 Mb/s is achieved. The PoF system has been tested and validated for turbidity and acoustic measurement applications. The current study focuses on the electronic development and the validation of the PoF system.

Modified carbon fiber electrodes with enhanced impedance performance for marine sensor

At present, the use of carbon fiber electrodes as marine sensors for marine signal exploration has received much attention. AC impedance is the crucial factor affecting the performance of sensor. Unfortunately, the AC impedance performance of carbon fiber electrodes has been underappreciated up to date. In order to fully understand the influencing factors of AC impedance, the comprehensive and systematic study was carried out in terms of reference electrode type, encapsulated shell of electrode, immersion depth of electrode, obstacles between electrodes, electrode position, initial voltage electrolyte concentration and electrolyte type. Through analog equivalent circuits, the solution impedance R1, charge transfer impedance R2 and diffusion impedance W1 were found to be 1.485, 0.313 and 1.158 Ω, respectively. The smallest impedance of silver-plated carbon fiber electrodes is only 3.49 Ω at 1 Hz. Combined with different influencing factors, we can know that the modified carbon fiber electrodes performance was mainly controlled by diffusion and transfer processes. This study would contribute to the design and application of the silver-plated carbon fiber electrodes as marine electric field sensor.

Sensors to Increase the Security of Underwater Communication Cables: A Review of Underwater Monitoring Sensors.

Underwater communication cables transport large amounts of sensitive information between countries. This fact converts these cables into a critical infrastructure that must be protected. Monitoring the underwater cable environment is rare and any intervention is usually driven by cable faults. In the last few years, several reports raised issues about possible future malicious attacks on such cables. The main objective of this operational research and analysis (ORA) paper is to present an overview of different commercial and already available marine sensor technologies (acoustic, optic, magnetic and oceanographic) that could be used for autonomous monitoring of the underwater cable environment. These sensors could be mounted on different autonomous platforms, such as unmanned surface vehicles (USVs) or autonomous underwater vehicles (AUVs). This paper analyses a multi-threat sabotage scenario where surveying a transatlantic cable of 13,000 km, (reaching water depths up to 4000 m) is necessary. The potential underwater threats identified for such a scenario are: divers, anchors, fishing trawls, submarines, remotely operated vehicles (ROVs) and AUVs. The paper discusses the capabilities of the identified sensors to detect such identified threats for the scenario under study. It also presents ideas on the construction of periodic and permanent surveillance networks. Research study and results are focused on providing useful information to decision-makers in charge of designing surveillance capabilities to secure underwater communication cables.

Development of Marine Equipment for Underwater Stereoscopic Observation

Establishing an underwater stereoscopic observation network to obtain scientific, real-time, and comprehensive data is an important direction for understanding and exploiting the ocean in the future. This study first analyzes the need and necessity of developing the underwater stereoscopic observation equipment, then introduces the development status of the equipment in China and abroad, further analyzes the problems faced by China’s field development, and presents the key breakthrough points of marine sensors which serve as the key link for underwater observation. China has made great progress in technologies regarding marine observation platforms, but it still lags behind the world advanced level in key marine sensors and high-precision sensors. The big data obtained from ocean observation does not match the actual demand, and the ocean sensors lack the support from an improved platform. To guide the long-term development of related fields, we suggest that China should support the efficient transformation of research results of key marine sensors, coordinate the management of the marine equipment for underwater stereoscopic observation, and establish a national public test platform for offshore instruments and equipment.

Smoothed Deep Neural Networks for Marine Sensor Data Prediction

Deep learning has become a mainstream method in marine data processing field. However, the raw marine data, characterized by fluctuations, outliers and noise, is a serious obstacle to its performance improvement. To address this problem, a hybrid deep computing model is developed for marine sensor data prediction, combining smoothing and deep belief echo state network (DBEN). The proposed structure adopts a two-stage data processing mode to deal with the complex characteristics of marine time series. In the preprocessing stage, four smoothing methods are considered for fluctuation reduction and outlier handling, so that purer data conducive to achieving a higher prediction accuracy can be gained. In the prediction stage, DBEN equipped with efficient feature learning serves as a nonlinear approximator. The effectiveness of the constructed hybrid model is tested on real-world marine time series of different sources and traits, and its superiority in prediction accuracy is demonstrated by readout comparisons and statistical measures.

On-line Analysis of Marine Water Quality Data Based on Large Electronic Computer

Cong, W.; Guo, J., and Yu, H., 2019. On-line analysis of marine water quality data based on large electronic computer. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 47–53. Coconut Creek (Florida), ISSN 0749-0208.In order to make full use of marine resources, develop marine economy and build a powerful marine country, in recent years, the state has focused on the on-line analysis technology of marine water. In order to solve the problems of rapid change of marine environment and difficulty in the detection and analysis of water quality data, this paper presents an online analysis method of marine water quality data based on large electronic computer after comparing various methods of marine water quality detection in China and abroad. This method uses a variety of marine water quality sensors to collect marine water quality data synthetically, and then uses multi-sensor fusion technology to fuse the collected water quality data. Finally, it combines multi-sensor D-S evidence theory data fusion technology with computer to analyze water quality data synthetically online. It is concluded that this method can effectively improve the ability of marine pollution early warning and marine ecological environment protection by real-time comprehensive analysis and research of marine water quality data.

Improving the Environmental Compatibility of Marine Sensors by Surface Functionalization with Graphene Oxide.

Improving the durability relating to biofouling resistance is still a major challenge for sensors applied in marine monitoring. Herein, a novel antifouling approach implementing biofouling resistance without compromising the sensor's performance is proposed. A polymeric membrane calcium ion-selective electrode (Ca2+-ISE) is chosen as a model sensor. An antifouling coating based on graphene oxide (GO) can be formed on the sensor's surface via the layer-by-layer technique in a simple and controllable manner. The GO coating works as a protection layer to impede the settlement of marine bacterial cells on the sensor surface due to its dual functionality of both antiadhesive and antimicrobial properties. The assembly of the GO coating does not influence the sensor's performance in terms of linear range and response slope. The biofouling resistance of the proposed sensor to marine bacterial cells is evaluated by using the colony-forming unit (CFU) counting method and confocal laser scanning microscopy analysis. An improved antimicrobial activity and a significant decrease in the adsorption of bacterial cells are observed for the GO-coated Ca2+-ISE. Moreover, negligible change is observed in the analysis performance of the GO-coated Ca2+-ISE after 7 day exposure to a rather high concentration marine bacterial suspension of ∼109 CFU mL-1. This work provides an efficient strategy of developing GO-based antifouling coatings to improve the environmental compatibility of marine sensors.

Periodic ultraviolet-C illumination for marine sensor antifouling

Ultraviolet light has intriguing potential as a marine antifoulant, targeting almost any species and applicable to almost any surface, while not accumulating in the environment. This study field-tested the effects of periodic ultraviolet-C illumination on marine macrofouling. Across four experiments, several UV illumination duty cycles were tested against controls with no illumination. Duty cycles between 1:2 (time with UV:total time per cycle) and 1:20 were all similarly effective, inhibiting almost all macrofouling at three different temperate Northeast Pacific and Northwest Atlantic sites. Susceptible taxa included barnacles, bryozoans, tunicates (colonial and solitary), and, to a slightly lesser extent, mussels. Duty cycles of 1:30 and 1:60 reduced but did not eliminate biofouling. Measurements of ultraviolet illumination on oceanographic sensors showed similar results. The results suggest further investigation of ultraviolet light as an antifoulant for marine sensors, including susceptibility of other taxa, optimizing illumination patterns, and exploring the potential for evolved resistance.

Particle and salinity sensing for the marine environment via deep learning using a Raspberry Pi

The identification of mixtures of particles in a solution via analysis of scattered light can be a complex task, due to the multiple scattering effects between different sizes and types of particles. Deep learning offers the capability for solving complex problems without the need for a physical understanding of the underlying system, and hence offers an elegant solution. Here, we demonstrate the application of convolutional neural networks for the identification of the concentration of microparticles (silicon dioxide and melamine resin) and the solution salinity, directly from the scattered light. The measurements were carried out in real-time using a Raspberry Pi, light source, camera, and neural network computation, hence demonstrating a portable and low-cost environmental marine sensor.

Collective Efficacy of Support Vector Regression With Smoothness Priority in Marine Sensor Data Prediction

Marine data prediction plays an increasingly important role in marine environmental monitoring. The support vector machine (SVM) is viewed as a useful machine learning tool in marine data processing, whereas it is not completely suitable for the abruptly fluctuating, multi-noise, non-stationary, and abnormal data. To address this issue, this paper proposes a novel machine learning framework for marine sensor data prediction, i.e., a support vector regression architecture with smoothness priority. This is a united and consistent system with functions of data acquisition, smoothness, and nonlinear approximation. Here, the smoothness is used to process the outliers and noises of the acquired marine sensor data. Whereafter, a nonlinear approximator based on the SVM is constructed for marine time series prediction. This architecture is the first attempt to consider the collective efficacy of smoother and SVM in marine data processing tasks. The experimental results show that our model significantly surpasses the single SVM in the real-world marine data prediction. Besides, standard statistical evaluation methods, such as QQPlot, PDF, CDF, and BoxPlot, are utilized to verify its superior nonlinear approximation capacity.

Removal of Microparticles by Ciliated Surfaces—an Experimental Study

AbstractBiological cilia are versatile hair‐like organelles that are very efficient in manipulating particles for, e.g., feeding, antifouling, and cell transport. Inspired by the versatility of cilia, this paper experimentally demonstrates active particle‐removal by self‐cleaning surfaces that are fully or partially covered with micromolded magnetic artificial cilia (MAC). Actuated by a rotating magnet, the MAC can perform a tilted conical motion, which leads to the removal of spherical particles of different sizes in water, as well as irregular‐shaped sand grains both in water and in air. These findings can contribute to the development of novel particulate manipulation and self‐cleaning/antifouling surfaces, which can be applied, e.g., to prevent fouling of (bio)sensors in lab‐on‐a‐chip devices, and to prevent biofouling of submerged surfaces such as marine sensors and water quality analyzers.

Butterfly‐Inspired Triboelectric Nanogenerators with Spring‐Assisted Linkage Structure for Water Wave Energy Harvesting

AbstractThe exploration of water wave energy to generate electric power is a promising and valuable way to solve global energy crisis. Triboelectric nanogenerators (TENGs) are considered as one of the most promising approaches for harvesting water wave energy. Here, a butterfly‐inspired triboelectric nanogenerator (B‐TENG) with spring‐assisted four‐bar linkage is fabricated to acquire multidirectional water wave energy from different types of movements generated by the device. The arc‐shaped outer shell can efficiently absorb the impact force of water wave and inner spring‐assisted four‐bar linkage can induce multiple contact–separation motion of TENG module, both of which allow the B‐TENG to harvest energy from different types of low‐frequency waves. The short‐circuit current and open‐circuit voltage of this B‐TENG device can reach 75.35 µA and 707.01 V, while a maximum output power density of 9.559 W m−3 can be achieved and 180 light‐emitting diodes can be directly lightened up by this B‐TENG. The energy generated from this B‐TENG can also be stored in a capacitor and to drive some marine sensor devices, such as an electronic thermometer. This proposed B‐TENG device is specially designed for multidirectional water energy harvesting and it may have good application prospects for ocean information monitoring and power supply to oceanic islands.

Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web.

The study of global phenomena requires the combination of a considerable amount of data coming from different sources, acquired by different observation platforms and managed by institutions working in different scientific fields. Merging this data to provide extensive and complete data sets to monitor the long-term, global changes of our oceans is a major challenge. The data acquisition and data archival procedures usually vary significantly depending on the acquisition platform. This lack of standardization ultimately leads to information silos, preventing the data to be effectively shared across different scientific communities. In the past years, important steps have been taken in order to improve both standardization and interoperability, such as the Open Geospatial Consortium’s Sensor Web Enablement (SWE) framework. Within this framework, standardized models and interfaces to archive, access and visualize the data from heterogeneous sensor resources have been proposed. However, due to the wide variety of software and hardware architectures presented by marine sensors and marine observation platforms, there is still a lack of uniform procedures to integrate sensors into existing SWE-based data infrastructures. In this work, a framework aimed to enable sensor plug and play integration into existing SWE-based data infrastructures is presented. First, an analysis of the operations required to automatically identify, configure and operate a sensor are analysed. Then, the metadata required for these operations is structured in a standard way. Afterwards, a modular, plug and play, SWE-based acquisition chain is proposed. Finally different use cases for this framework are presented.

Input quality aware convolutional LSTM networks for virtual marine sensors

The harsh environmental conditions in a marine area make continuous observations of it challenging. To temporally or permanently replace faulty hardware sensors, reliable virtual sensors are getting more and more important. This paper introduces a deep learning architecture for a marine virtual sensor application that is able to utilize input quality information. We propose a novel input quality based dropout layer to take advantage of Information about the quality of the input sensors. The virtual sensor models are built upon convolutional and recurrent long short-term memory layers. We apply a time dimensionality reduction method called exPAA that retains finer details from recent values, but past information with less details are also available to the model. As interpretable reliability is important virtual sensors, we include predictive uncertainty based on dropout and Monte Carlo predictions into our neural network. Experimental results show that we perform better than the baseline and our input quality based dropout layer improves on these results even further. We also provide insights on the learned uncertainty intervals as well as the utilized linear and non-linear correlations of the first convolutional layer.

Designing a transparent organogel layer with self-repairing property for the inhibition of marine biofouling

The presence of a biofilm on an optical window of a sensor is one of the main threats to the continued successful operation of marine sensors. A self-repairable and transparent organogel layer (OG) was designed by swelling cross-linked polydimethylsiloxane (PDMS) with the nontoxic n-alkanes to restrict the biofilm attachment. Compared with the traditional slippery liquid-infused porous surfaces in the marine biofouling inhibition, the as-prepared OG has a self-repairing property and can maintain high underwater transmittance. Its self-repairing property was verified by the optical microscope images of releasing n-alkanes. Meanwhile, the potential of OG to inhibit biofilm formation was verified by a bacterial settlement experiment in a stimulated marine environment. Compared with the bare glassBG and PDMS, the as-prepared OG can efficiently inhibit the bacterial attachment and maintain high underwater transmittance under the static and dynamic conditions. The effect of the chain length of n-alkanes on bacterial inhibition was also studied, and results showed that the OG with n-alkanes of shorter chain exhibited better performance in bacterial inhibition. This study demonstrates that the as-prepared OG has the potential to protect the marine optical sensor from biofouling.

A Multi-Objective Partition Method for Marine Sensor Networks Based on Degree of Event Correlation.

Existing marine sensor networks acquire data from sea areas that are geographically divided, and store the data independently in their affiliated sea area data centers. In the case of marine events across multiple sea areas, the current network structure needs to retrieve data from multiple data centers, and thus severely affects real-time decision making. In this study, in order to provide a fast data retrieval service for a marine sensor network, we use all the marine sensors as the vertices, establish the edge based on marine events, and abstract the marine sensor network as a graph. Then, we construct a multi-objective balanced partition method to partition the abstract graph into multiple regions and store them in the cloud computing platform. This method effectively increases the correlation of the sensors and decreases the retrieval cost. On this basis, an incremental optimization strategy is designed to dynamically optimize existing partitions when new sensors are added into the network. Experimental results show that the proposed method can achieve the optimal layout for distributed storage in the process of disaster data retrieval in the China Sea area, and effectively optimize the result of partitions when new buoys are deployed, which eventually will provide efficient data access service for marine events.

PSDAAP: Provably Secure Data Authenticated Aggregation Protocols Using Identity-Based Multi-Signature in Marine WSNs.

Data authenticated aggregation is always a significant issue for wireless sensor networks (WSNs). The marine sensors are deployed far away from the security monitoring. Secure data aggregation for marine WSNs has emerged and attracted the interest of researchers and engineers. A multi-signature enables the data aggregation through one signature to authenticate various signers on the acknowledgement of a message, which is quite fit for data authenticated aggregation marine WSNs. However, most of the previous multi-signature schemes rely on the technique of bilinear pairing involving heavy computational overhead or the management of certificates, which cannot be afforded by the marine wireless sensors. Combined with the concept of identity-based cryptography, a few pairing-free identity-based multi-signature (IBMS) schemes have been designed on the basis of the integer factorization problem. In this paper, we propose two efficient IBMS schemes that can be used to construct provably secure data authenticated aggregation protocols under the cubic residue assumption, which is equal to integer factorization. We also employ two different methods to calculate a cubic root for the cubic residue number during the signer’s private key extraction. The algorithms are quite efficient compared to the previous work, especially for the algorithms of the multi-signature generation and its verification.

Analysis of data characterizing tide and current fluxes in coastal basins

Abstract. Many coastal monitoring programmes have been carried out to investigate in situ hydrodynamic patterns and correlated physical processes, such as sediment transport or spreading of pollutants. The key point is the necessity to transform this growing amount of data provided by marine sensors into information for users. The present paper aims to outline that it is possible to recognize the recurring and typical hydrodynamic processes of a coastal basin, by conveniently processing some selected marine field data. The illustrated framework is made up of two steps. Firstly, a sequence of analysis with classic methods characterized by low computational cost was executed in both time and frequency domains on detailed field measurements of waves, tides, and currents. After this, some indicators of the hydrodynamic state of the basin were identified and evaluated. Namely, the assessment of the net flow through a connecting channel, the time delay of current peaks between upper and bottom layers, the ratio of peak ebb and peak flood currents and the tidal asymmetry factor exemplify results on the vertical structure of the flow, on the correlation between currents and tide and flood/ebb dominance. To demonstrate how this simple and generic framework could be applied, a case study is presented, referring to Mar Piccolo, a shallow water basin located in the inner part of the Ionian Sea (southern Italy).

Evaluation of marine pH sensors under controlled and natural conditions for the Wendy Schmidt Ocean Health XPRIZE

The annual anthropogenic ocean carbon uptake of 2.6 ± 0.5 Gt C is changing ocean composition (e.g., pH) at unprecedented rates, but our ability to track this trend effectively across various ocean ecosystems is limited by the availability of low-cost, high-quality autonomous pH sensors. The Wendy Schmidt Ocean Health XPRIZE was a year-long competition to address this scientific need by awarding $2 million to developers who could improve the performance and reduce the cost of pH sensors. Contestants' sensors were deployed in a series of trials designed to test their accuracy, repeatability, and stability in laboratory, coastal, and open-ocean settings. This report details the validation efforts behind the competition, which included designing the sensor evaluation trials, providing the conventional true pH values against which sensors were judged, and quantifying measurement uncertainty. Expanded uncertainty (coverage factor k = 2, corresponding to 95% confidence) of validation measurements throughout the competition was approximately 0.01 pH units. A custom tank was designed for the coastal trials to expose the sensors to natural conditions, including temporal variability and biofouling, in a spatially homogenous environment. The competition prioritized the performance metrics of accuracy, repeatability, and stability over specific applications such as high-frequency measurements. Although the XPRIZE competition focused on pH sensors, it highlights considerations for testing other marine sensors and measuring seawater carbonate chemistry.