Oceanographic Data Collection Research Articles

Improved Grey Wolf Optimization Based Node Localization Approach in Underwater Wireless Sensor Networks

Abstract Underwater Wireless Sensor Networks (UWSNs) are established by Autonomous Underwater Vehicles (AUVs) or static Sensor Nodes (SN) that collect and transmit information over the underwater environment. Localization plays a vital role in the effective deployment, navigation and coordination of these nodes for many applications, namely underwater surveillance, underwater exploration, oceanographic data collection and environmental monitoring. Due to the unique characteristics of underwater transmission and acquisition, this is a fundamental challenge in underwater networks. However, localization in UWSNs is problematic due to the unique features of underwater transmission and the harsh underwater environment. To address these challenges, this paper presents an Improved Grey Wolf Optimization Based Node Localization Approach in UWSN (IGWONL-UWSN) technique. The presented IGWONL-UWSN technique is inspired by the hunting behavior of grey wolves with the Dimension Learning-based Hunting (DLH) search process. The proposed IGWONL-UWSN technique uses the Improved Grey Wolf Optimization Based (IGWO) algorithm to calculate the optimal location of the nodes in the UWSN. Moreover, the IGWONL-UWSN technique incorporates the DLH search process to improve the convergence and accuracy. The simulation results of the IGWONL-UWSN technique are validated using a set of performance measures. The simulation results show the improvements of the IGWONL-UWSN method over other approaches with respect to various metrics.

An oceanographic data collection scheme using hybrid optimization for leakage detection during oil mining in mobility assisted UWSN

An oceanographic data collection scheme using hybrid optimization for leakage detection during oil mining in mobility assisted UWSN

Improved LEACH Protocol Based on Underwater Energy Propagation Model, Parallel Transmission, and Replication Computing for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

ANALYSIS OF THE OCEANOGRAPHIC CONDITIONS OF MEOSBEKWAN ISLAND (RAJA AMPAT DISTRICT) FOR THE SUITABILITY OF SEAWEED CULTIVATION

Seaweed cultivation is an alternative community livelihood that potentially helping to reduce pressure on coral reefs in the area of Raja Ampat Archipelago. This study was aimed to conduct an oceanographic analysis of the waters of Meosbekwan Island (Raja Ampat Regency) for the suitability of seaweed cultivation. Oceanographic data collection was carried out at three points (M1, M2, and M3) on the western side of Meosbekwan Island. Analysis of suitability for aquaculture was initiated with an analysis of key parameters, i.e., depth, protection and shipping traffic. The suitability analysis was followed by an analysis of the oceanographic parameters of the waters by weighting them using a ranking system. Furthermore, the final stage of suitability analysis was to evaluate the feasibility level of seaweed cultivation. The results showed that the depth of the waters on the western side of Meosbekwan Island ranged from 1.3 m – 5.8 m with an average depth of 1.5 m in the south (M1), 2.2 m in the middle (M2), and 3.9 m to the north (M3). Wave height during the study ranged from 0.1 – 0.3 m with an average value of 0.25 m at all points. The velocity of the water currents ranged from 0.08 – 0.25 m/s. The salinity of the waters was relatively homogeneous (30 - 31 o/oo). Water temperature also has small and relatively homogeneous variations with a range of 30 - 31˚C. The brightness of the waters ranged from 2.5 – 3.5 m. The bottom of the waters on the west side of Meosbekwan Island generally consists of coarse sand, rubble, and seagrass vegetation Enhalus acoroides and Cymodocea sp. In general, the oceanographic conditions of the waters are suitable for the life and growth of seaweed. The results of the location suitability evaluation indicated that the waters on the western side of Meosbekwan Island were technically feasible to serve as a location for seaweed cultivation.

Quantifying Self-Noise of the Seaglider AUV Using a Passive Acoustic Monitor

Abstract The Seaglider, a type of underwater glider, is a relatively quiet vehicle in comparison with propelled autonomous underwater vehicles, making it a desirable acoustic receiving platform. Vehicle operations, such as pumping/bleeding oil to change buoyancy, shifting/rotating the battery to change pitch/roll, and oceanographic data collection, do, however, produce some self-noise. This system performance study analyzes the prevalence, frequency content, duration, and levels of self-noise associated with vehicle operations using data collected with a passive acoustic monitoring system mounted on the body of a Seaglider vehicle. Guidance and control functions, including pitch, roll, and buoyancy changes, were the major source of platform noise, producing broadband noise ranging from less than a second to over 3 min in duration, with sound pressure levels of 120‐145.5 dB re 1μPa. Frequencies below 10 kHz were the most impacted by self-noise, with a maximum 1/3 octave level of 137.5 dB re 1μPa in the 2.5kHz band caused by the pumping of oil in the variable buoyancy device. Guidance and control changes occurred during 4%‐13% of the dive for dives greater than 500 m. The bulk of these operations, however, were performed near the surface and apogee of the dive and typically affected only about 6% of the dive cycle duration for deep dives.

Techno-economic Optimization of an Offshore Hybrid Power System: Argentine Basin Case Study

Ocean observation and monitoring relies on data gathered from multiple sources including gliders, cabled observatories, underwater vehicles, and moored buoys. Moored buoys, augmented by resident autonomous underwater vehicles, are potentially well-suited to long-term oceanographic data collection with the resolution and scope of data collection increased by utilizing in-situ power generation. While power needs could be provided by a single generation source (e.g., diesel generator, solar photovoltaics, or wave energy converter) with battery backup, a hybrid power system can potentially smooth seasonal variations that would otherwise require large generation and storage capacities, and be tailored to a locations’ resource availability. By reducing system size through hybridization, we have the potential to reduce overall system cost. To this end, we developed an optimization model that defines the generator and storage capacities of the cost-optimal hybrid power system for a given location and load profile. The model considers generation by wave energy converters, wind turbines, solar photovoltaics, diesel generators, and current turbines with a battery for energy storage. The model uses the defined load profile and location specific time series of resource availability for a time-domain simulation of the power generated and battery state of charge. Based on the power system size, mooring, and mission specific maintenance schedule, the model calculates capital and operating costs for each potential combination of generation and storage capacities. The optimization model searches the 6-dimensional design space to find the lowest cost system that can satisfy the load requirements. In this paper, we focus on a case study of a hybrid power system serving an ocean observation buoy with a resident autonomous underwater vehicle located at the Mid-Atlantic Shelf Break. Diagnostic metrics such as the cost-breakdown and generator capacity factors are evaluated to understand cost drivers and draw comparisons to the costs of single generation systems. Additionally, the model sensitivity to key assumptions such as battery life, maintenance vessel cost, survival loads, and required persistence are discussed.

Integrating fishers’ knowledge with oceanographic observations to understand changing ocean conditions in the Northeast United States

Recent warming in the Northeast United States continental shelf ecosystem has raised several concerns about the impacts on the ecosystem and commercial fisheries. In 2014, researchers from the Commercial Fisheries Research Foundation and Woods Hole Oceanographic Institution founded the Shelf Research Fleet to involve fishers in monitoring the rapidly changing ocean environment and encourage sharing of ecological knowledge. The Shelf Research Fleet is a transdisciplinary, cooperative program that trains commercial fishers to collect oceanographic information by deploying conductivity, temperature, and depth (CTD) instruments while commercially fishing. A total of 806 CTD profiles have been collected by the Shelf Research Fleet through December 2022. Participating vessels can view the conductivity and temperature water column profiles they collect in real-time. These profiles help inform their fishing practices and give insights when unexpected species appear in their gear or if their catch composition changes from previous years. The data collected by the Shelf Research Fleet are shared with and processed by researchers from numerous partnering institutions. The Shelf Research Fleet data have been used by researchers to better understand oceanographic phenomena including marine heatwaves, shelf-break exchange processes, warm core rings, and salinity maximum intrusions onto the continental shelf. The scope of the Shelf Research Fleet has grown over time to include efforts to more directly link oceanographic results with biological observations to better understand how changing ocean conditions are affecting commercially important species. This article describes the approach, successes, challenges, and future directions of the Shelf Research Fleet and aims to outline a framework for a cost-effective research program that engages fishers in the collection of oceanographic data, strengthening partnerships between fishing industry members and the scientific community.

Information technologies for automation of hyddrophisical situation molelling and forecasting in the Black Sea region

The article offers a solution to the scientific and applied problem of automating the acquisition and analysis of a certain set of hydrophysical indicators with the subsequent formation of a forecast of the hydrophysical situation in the Black Sea water area. This topic is of great relevance both in peacetime and in the conditions of the current armed aggression of the Russian Federation against Ukraine. The solved task is an important element of solving the more complex problem of operational obtaining of oceanographic data for highlighting the hydrographic situation in the Black Sea water area, collection, accumulation and visualization of oceanographic data in the interests of navigation and hydrographic support of users of such information. To solve this problem, a software environment was created for automatic search and selection of hydrophysical data, as well as their automatic input into specialized software for modeling and forecasting the hydrophysical situation with subsequent calculation of forecasts of hydrophysical fields. A set of the most important data was formed, which includes indicators of salinity, temperature, circulation. Automatic search and selection of meteorological and hydrophysical data takes place from international GFS and COPERNICUS profile databases. Also, to increase the effect of the simulations and forecasts, a specialized interface was developed for searching, visualization, and the possibility of downloading by the user the results of the forecast module of the mathematical modeling of the spatio-temporal variability of the hydrophysical fields of the northwestern part of the Black Sea. The developed scientific and practical approaches and toolkit are designed to function within the framework of the distributed and centralized Bank of Oceanographic Data of the National Academy of Sciences of Ukraine, part of which is an intellectual information system for highlighting the hydrographic situation in the Black Sea water area. The obtained results are also aimed at increasing the level of environmental safety in the Black Sea water area and can be used to monitor nature management in this region.

Review of Localization and Clustering in USV and AUV for Underwater Wireless Sensor Networks

Oceanographic data collection, disaster prevention, aided navigation, critical observation sub-missions, contaminant screening, and seaward scanning are just a few of the submissions that use underwater sensor hubs. Unmanned submerged vehicles (USVs) or autonomous acoustic underwater vehicles (AUVs) through sensors would similarly be able to explore unique underwater resources and gather data when utilized in conjunction with integrated screen operations. The most advanced technological method of oceanic observation is wireless information routing beneath the ocean or generally underwater. Water bottoms are typically observed using oceanographic sensors that collect data at certain ocean zones. Most research on UWSNs focuses on physical levels, even though the localization level, such as guiding processes, is a more recent zone. Analyzing the presenting metrics of the current direction conventions for UWSNs is crucial for considering additional enhancements in a procedure employing underwater wireless sensor networks for locating sensors (UWSNs). Due to their severely constrained propagation, radio frequency (RF) transmissions are inappropriate for underwater environments. This makes it difficult to maintain network connectivity and localization. This provided a plan for employing adequate reliability and improved communication and is used to locate the node exactly using a variety of methods. In order to minimize inaccuracies, specific techniques are utilized to calculate the distance to the destination. It has a variety of qualities, such as limited bandwidth, high latency, low energy, and a high error probability. Both nodes enable technical professionals stationed on land to communicate data from the chosen oceanic zones rapidly. This study investigates the significance, uses, network architecture, requirements, and difficulties of undersea sensors.

Underwater Cognitive Acoustic Networks Architecture, Development and Deployment

To develop eco-friendly Underwater Cognitive Acoustic Networks (UCANs) with high spectrum utilization. A huge number of sensor networks are installed Underwater to enable applications for oceanographic data collection, pollution, and corrosion monitoring, offshore exploration, disaster prevention, assisted navigation, and tactical surveillance applications. For feasible applications, there is a need to enable underwater communications among the underwater devices. The latest developments in wireless communications cause spectrum shortage problems in the oceans. Cognitive Acoustic (CA) is the enabling technology for supporting dynamic spectrum access techniques in wireless communication. The CA provides the solution for the spectrum scarcity problem that is encountered in many countries. In oceans, both natural acoustic systems and artificial acoustic systems use acoustic signals for communication. To efficiently utilize the spectrum without interference with other acoustic systems, a smart UAN should be designed with awareness of the surrounding environment and the ability to reconfigure their operation parameters such as frequency band, modulation schemes, and transmission power.

Long-Term Monitoring of Diel and Seasonal Rhythm of Dentex dentex at an Artificial Reef

Behavioral rhythms are a key aspect of species fitness, since optimize ecological activities of animals in response to a constantly changing environment. Cabled observatories enable researchers to collect long-term biological and environmental data in real-time, providing relevant information on coastal fishes’ ecological niches and their temporal regulation (i.e., phenology). In this framework, the platform OBSEA (an EMSO Testing-Site in the NW coastal Mediterranean) was used to monitor the 24-h and seasonal occurrence of an ecologically iconic (i.e., top-predator) coastal fish species, the common dentex (Dentex dentex). By coupling image acquisition with oceanographic and meteorological data collection at a high-frequency (30 min), we compiled 8-years’ time-series of fish counts, showing daytime peaks by waveform analysis. Peaks of occurrence followed the photophase limits as an indication of photoperiodic regulation of behavior. At the same time, we evidenced a seasonal trend of counts variations under the form of significant major and minor increases in August and May, respectively. A progressive multiannual trend of counts increase was also evidenced in agreement with the NW Mediterranean expansion of the species. In GLM and GAM modeling, counts not only showed significant correlation with solar irradiance but also with water temperature and wind speed, providing hints on the species reaction to projected climate change scenarios. Grouping behavior was reported mostly at daytime. Results were discussed assuming a possible link between count patterns and behavioral activity, which may influence video observations at different temporal scales.

Fine-Scale Velocity Measurement on the Wirewalker Wave-Powered Profiler

Abstract The Wirewalker (WW) ocean-wave-powered vertical profiling system allows the collection of high-resolution oceanographic data due to its rapid profiling, hydrodynamically quiet operation, and long endurance. We have assessed the potential for measuring fine-scale ocean velocities from the Wirewalker platform using commercially available acoustic velocimeters. Although the vertical profiling speed is relatively steady, platform motion affects the velocity measurements and requires correction. We present an algorithm to correct our velocity estimates using platform motion calculated from the inertial sensors—accelerometer, gyroscope, and magnetometer—on a Nortek Signature1000 acoustic Doppler current profiler (ADCP). This correction, carried out ping by ping, was effective in removing the vehicle motion from the measured velocities. The motion-corrected velocities contain contributions from surface wave orbital velocities, especially near the surface, and the background currents. To proceed, we use an averaging approach that leverages both the vertical platform profiling of the system and the ∼15–20 m vertical profiling range resolution of the down-looking ADCP to separate the surface wave orbital velocities and the background flow. The former can provide information on the wave conditions. From the latter, we are able to estimate fine-scale velocity and shear with spectral wavenumber rolloff at vertical scales around 3 m, a vertical resolution several times finer than that possible from modern shipboard or fixed ADCPs with similar profiling range, and similar to recent glider measurements. When combined with a continuous time series of buoy drift calculated from the onboard GPS, a highly resolved total velocity field is obtained, with a unique combination of space and time resolution.

Energy Efficient and Quality-of-Service Aware Routing using Underwater Wireless Sensor Networks

In current years, there has been an increasing attention in Underwater wireless sensor networks (UWSNs). Underwater sensor networks (USNs) can be applied for many various purposes. To address the routing issue, the Cuckoo Search Optimization Algorithm with Energy Efficient and QOS Aware (CSOA-EQ) based routing methods have been proposed in this chapter. Every application is important in its own right, but some of them can help improve sea investigation to meet a variety of underwater applications, such as a catastrophic event alert system (such as torrent and seismic monitoring), supported navigation, oceanographic data collection, and underwater surveillance, ecological applications (such as the nature of organic water and contamination monitoring), modern applications (such as marine investigation), and so on. For example, sensors can assess specific metrics, such as base intensity and securing pressure, to monitor the auxiliary nature of the securing environment in offshore engineering applications. UASNs have also improved our understanding of underwater environments, such as climate change, underwater creature life, and the number of inhabitants in coral reefs.

An Efficient Void Aware Framework for Enabling Internet of Underwater Things

The Internet of Underwater Things (IoUT) is an emerging area in marine science and engineering. It has witnessed significant research and development attention from both academia and industries due to its growing underwater use cases in oceanographic data collection, pollution monitoring, seismic monitoring, tactical surveillance, and assisted navigation for waterway transport. Information dissemination in the underwater network environment is very critical considering network dynamism, unattainable nodes, and limited resources of the tiny IoUT devices. Existing techniques are majorly based on location-centric beacon messages, which results in higher energy consumption, and wastage of computing resources in tiny IoUT devices. Towards this end, this paper presents an efficient void aware (EVA) framework for information dissemination in IoUT environment. Network architecture is modeled considering potential void region identification in the underwater network environment. An efficient void aware (EVA) information dissemination framework is proposed focusing on detecting void network region, and intelligent void aware data forwarding. The comparative performance evaluation attests to the benefits of the proposed framework in terms of energy consumption, network lifetime, packet delivery ratio, and end-to-end delay for information dissemination in IoUT.

Energy-Efficient Mobility Prediction Routing Protocol for Freely Floating Underwater Acoustic Sensor Networks

Recently, there has been an increasing interest in monitoring and exploring underwater environments for scientific applications such as oceanographic data collection, marine surveillance, and pollution detection. Underwater acoustic sensor networks (UASNs) have been proposed as the enabling technology to observe, map, and explore the ocean. The unique characteristics of underwater aquatic environments such as low bandwidth, long propagation delays, and high energy consumption make the data forwarding process very difficult. Moreover, the mobility of the underwater sensors is considered an additional constraint for the success of the data forwarding process. That being said, most of the data forwarding protocols do not realistically consider the dynamic topology of underwater environment as sensor nodes move with the water currents, which is a natural phenomenon. In this research, we propose a mobility prediction optimal data forwarding (MPODF) protocol for UASNs based on mobility prediction. Indeed, by considering a realistic, physically inspired mobility model, our protocol succeeds to forward every generated data packet through one single best path without the need to exchange notification messages, thanks to the mobility prediction module. Simulation results show that our protocol achieves a high packet delivery ratio, high energy efficiency, and reduced end-to-end delay.

Correlation Between the Phytoplankton Distribution with the Oceanographic Parameters of the Deep-Sea Surface of Sangihe-Talaud, North Sulawesi, Indonesia

Phytoplankton is the primary producer and plays an important role in the food chain process, especially in the oceans. This study aims to describe the phytoplankton distribution and correlations with oceanographic parameters in the deep-sea surface, Sangihe-Talaud, North Sulawesi. Research methodologies include; oceanographic data collection (salinity, temperature, dissolved oxygen, and density), field methods, and phytoplankton samples processing. Data analysis includes abundance, diversity and domination index, PCA analysis, and Similarity index. The results showed that the phytoplankton composition in the sea surface layer of the Sangihe-Talaud waters has three classes: Bacillariophyceae and 86% with ten generai.e., Chaetoceros, Coscinodiscus, Dactyliosolen, Eucampia, Guinardia, Hemiaulus, Leptocylindrus, Pleurosigma, Rhizoselenia, and Skeletonema. The class of Dinophyceae 8%, as many as 17 genera, i.e., Actiniscus, Amphisolenia, Ceratium, Ceratocorys, Gambierdiscus, Gonyaulax, Gymnodinium, Heterodinium, Mesoporos, Noctiluca, Ornithocercus, Oxyphysis, Podolampas, Prorocentrum, Protoperidinium, Pyrocystis, and Pyrophacus. The class of Cyanophyceae 6% and found only one genus Trichodesmium. Phytoplankton abundance was calculated as 65.63 to 1,071.88 cells L−1, with an average of 419.03 cells L−1. The distribution is exhibited that the higher abundance in the southern part near the mainland of the Sulawesi and Maluku channels compared to the northern part, which is directly connected with the Pacific Ocean. The diversity and dominance index of phytoplankton are categorized as generally moderate and no species domination. The correlation between oceanographic parameters with abundance and diversity of phytoplankton is characterized by the identifiers using salinity, density, and temperature; and indicated a strong similarity index.

A Novel Profiler Driven by Tidal Energy for Long Term Oceanographic Measurements in Offshore Areas

In this paper, an innovative profiler driven by tidal energy for long-term oceanographic measurements in offshore areas with abundant tidal resources is investigated. The profiler is mainly composed of an oceanographic data collection system equipped with various sensors and a cross-plate that can make an upward or downward movement under the impact of tidal currents. Theoretical research is carried out through static analysis and numerical simulation, mainly studying the hydrodynamic characteristics of the cross-plate and its dynamic response to the current velocity. The theoretical model is verified by comparison with experiments. The research results show that tidal energy can be used as a kind of energy to drive the profiler’s ascent and descent motion and to continuously measure ocean parameters without using electric energy. The theoretical model established in this study can roughly predict the position of the profiler observation platform in the vertical direction under various current velocities. Furthermore, by studying the relationship between the current velocities and the lift and drag forces of the cross-plate in the fluid, it is recognized that the current velocity is an important factor affecting the stability of the system’s motion. It is hoped that this research will contribute to the development of profilers.

Advancing Offshore Wind Resource Characterization Using Buoy-Based Observations

AbstractAs countries continue to implement sustainable and renewable energy goals, the need for affordable low-carbon technologies, including those related to offshore wind energy, is accelerating. The U.S. federal government recognizes the environmental and economic benefits of offshore wind development and is taking the necessary steps to overcome critical challenges facing the industry to realize these benefits. The U.S. Department of Energy (DOE) is investing in buoy-mounted lidar systems to facilitate offshore measurement campaigns that will advance our understanding of the offshore environment and provide the observational data needed for model validation, particularly at hub height where offshore observations are particularly lacking. On behalf of the DOE, the Pacific Northwest National Laboratory manages a Lidar Buoy Program that facilitates meteorological and oceanographic data collection using validated methods to support the U.S. offshore wind industry. Since being acquired in 2014, two DOE lidar buoys have been deployed on the U.S. east and west coasts, and their data represent the first publicly available multi-seasonal hub height data to be collected in U.S. waters. In addition, the buoys have undergone performance testing, significant upgrades, and a lidar validation campaign to ensure the accuracy and reliability of the lidar data needed to support wind resource characterization and model validation (the lidars were validated against a reference lidar installed on the Air-Sea Interaction Tower operated by the Woods Hole Oceanographic Institution). The Lidar Buoy Program is providing valuable offshore data to the wind energy community, while focusing data collection on areas of acknowledged high priority.

Seabirds, environmental features and the Argentine anchovy Engraulis anchoita in the southwestern Atlantic Ocean

As top predators in marine environments, seabirds frequently respond to the presence of their main prey, and both predators and prey are usually associated with specific environmental features. We investigated the variability in the presence and density of flying seabirds (mostly Procellariiformes and Charadriiformes) and Magellanic penguinsSpheniscus magellanicusalong the southwestern (SW) Atlantic continental shelf. Five acoustic assessment surveys were conducted to determine the biomass of the Argentine anchovyEngraulis anchoita, and seabird counts and the collection of oceanographic data were conducted simultaneously with the surveys between June and October 2010. Generalized linear mixed models (GLMMs) were fitted to test the effect of anchovy density and environmental variables on seabird density. Sea surface temperature was significant for the presence of flying seabirds. Bottom water temperature and anchovy density were key variables affecting the presence and density of penguins, while bottom water salinity was also important for penguin presence. Based on Akaike’s information criterion, the most important factor explaining the density of flying seabirds was the difference between surface and bottom salinity (ΔS), while for penguin density, the most important factor was anchovy density. These results highlight that the subtropical shelf front in the SW Atlantic Ocean is a key feature influencing the aggregation of flying seabirds and confirm the close association of penguins and anchovies. Bottom water intrusion, originating from the sub-Antarctic shelf water, is an important factor explaining the presence of penguins, which tend to aggregate in areas with high anchovy densities on the SW Atlantic continental shelf.

Oceanids C2: An Integrated Command, Control, and Data Infrastructure for the Over-the-Horizon Operation of Marine Autonomous Systems

Long-range Marine Autonomous Systems (MAS), operating beyond the visual line-of-sight of a human pilot or research ship, are creating unprecedented opportunities for oceanographic data collection. Able to operate for up to months at a time, periodically communicating with a remote pilot via satellite, long-range MAS vehicles significantly reduce the need for an expensive research ship presence within the operating area. Heterogeneous fleets of MAS vehicles, operating simultaneously in an area for an extended period of time, are becoming increasingly popular due to their ability to provide an improved composite picture of the marine environment. However at present, the expansion of the size and complexity of these multi-vehicle operations is limited by a number of factors: (1) custom control-interfaces require pilots to be trained in the use of each individual vehicle, with limited cross-platform standardisation; (2) the data produced by each vehicle are typically in a custom vehicle-specific format, making the automated ingestion of observational data for near-real-time analysis and assimilation into operational ocean models very difficult; (3) the majority of MAS vehicles do not provide machine-to-machine interfaces, limiting the development and usage of common piloting tools, multi-vehicle operating strategies, autonomous control algorithms and automated data delivery. In this paper, we describe a novel piloting and data management system (C2) which provides a unified web-based infrastructure for the operation of long-range MAS vehicles within the UK's National Marine Equipment Pool. The system automates the archiving, standardisation and delivery of near-real-time science data and associated metadata from the vehicles to end-users and Global Data Assembly Centres mid-mission. Through the use and promotion of standard data formats and machine interfaces throughout the C2 system, we seek to enable future opportunities to collaborate with both the marine science and robotics communities to maximise the delivery of high-quality oceanographic data for world-leading science.