Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation

The micro-scale and meso-scale ocean dynamic processes which are nonlinear and have large variability, have a significant impact on the fisheries, natural resources, and marine climatology. A rapid, refined and sophisticated observation system is therefore needed in marine scientific research. The maneuverability and controllability of mobile sensor platforms make them a preferred choice to establish ocean observing networks, compared to the static sensor observing platform. In this study, marine vehicles are utilized as the nodes of mobile sensor networks for coverage sampling of a regional ocean area and ocean feature tracking. A synoptic analysis about marine vehicle dynamic control, multi vehicles mission assignment and path planning methods, and ocean feature tracking and observing techniques is given. Combined with the observation plan in the South China Sea, we provide an overview of the mobile sensor networks established with marine vehicles, and the corresponding simulation results

Oceans of Tomorrow sensor interoperability for in-situ ocean monitoring

The Oceans of Tomorrow (OoT) projects, funded by the European Commission’s FP7 program, are developing a new generation of sensors supporting physical, biogeochemical and biological oceanographic monitoring. The sensors range from acoustic to optical fluorometers to labs on a chip. The result is that the outputs are diverse in a variety of formats and communication methodologies. The interfaces with platforms such as floats, gliders and cable observatories are each different. Thus, sensorPeer ReviewedPostprint (author's final draft

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

A multirobot platform based on autonomous surface and underwater vehicles with bio-inspired neurocontrollers for long-term oil spills monitoring

This paper describes the BUSCAMOS-Oil monitoring system, which is a robotic platform consisting of an autonomous surface vessel combined with an underwater vehicle. The system has been designed for the long-term monitoring of oil spills, including the search for the spill, and transmitting information on its location, extent, direction and speed. Both vehicles are controlled by two different types of bio-inspired neural networks: a Self-Organization Direction Mapping Network for trajectory generation and a Neural Network for Avoidance Behaviour for avoiding obstacles. The systems’ resilient capabilities are provided by bio-inspired algorithms implemented in a modular software architecture and controlled by redundant devices to give the necessary robustness to operate in the difficult conditions typically found in long-term oil-spill operations. The efficacy of the vehicles’ adaptive navigation system and long-term mission capabilities are shown in the experimental results.This work was partially supported by the BUSCAMOS Project (ref. 1003211003700) under the program DN8644 COINCIDENTE of the Spanish Defense Ministry, the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia-19895/GERM/15)”, and the Spanish Government’s cDrone (ref. TIN2013-45920-R) and ViSelTR (ref. TIN2012-39279) projects

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

Underwater Sensor Nodes and Networks

Sensor technology has matured enough to be used in any type of environment. The appearance of new physical sensors has increased the range of environmental parameters for gathering data. Because of the huge amount of unexploited resources in the ocean environment, there is a need of new research in the field of sensors and sensor networks. This special issue is focused on collecting recent advances on underwater sensors and underwater sensor networks in order to measure, monitor, surveillance of and control of underwater environments. On the one hand, from the sensor node perspective, we will see works related with the deployment of physical sensors, development of sensor nodes and transceivers for sensor nodes, sensor measurement analysis and several issues such as layer 1 and 2 protocols for underwater communication and sensor localization and positioning systems. On the other hand, from the sensor network perspective, we will see several architectures and protocols for underwater environments and analysis concerning sensor network measurements. Both sides will provide us a complete view of last scientific advances in this research field.Lloret, J. (2013). Underwater Sensor Nodes and Networks. Sensors. 13(9):11782-11796. doi:10.3390/s130911782S1178211796139Garcia, M., Sendra, S., Lloret, G., & Lloret, J. (2011). Monitoring and control sensor system for fish feeding in marine fish farms. IET Communications, 5(12), 1682-1690. doi:10.1049/iet-com.2010.0654Martinez, J. J., Myers, J. R., Carlson, T. J., Deng, Z. D., Rohrer, J. S., Caviggia, K. A., … Weiland, M. A. (2011). Design and Implementation of an Underwater Sound Recording Device. Sensors, 11(9), 8519-8535. doi:10.3390/s110908519Ardid, M., Martínez-Mora, J. A., Bou-Cabo, M., Larosa, G., Adrián-Martínez, S., & Llorens, C. D. (2012). Acoustic Transmitters for Underwater Neutrino Telescopes. Sensors, 12(4), 4113-4132. doi:10.3390/s120404113Baronti, F., Fantechi, G., Roncella, R., & Saletti, R. (2012). Wireless Sensor Node for Surface Seawater Density Measurements. Sensors, 12(3), 2954-2968. doi:10.3390/s120302954Mànuel, A., Roset, X., Rio, J. D., Toma, D. M., Carreras, N., Panahi, S. S., … Cadena, J. (2012). Ocean Bottom Seismometer: Design and Test of a Measurement System for Marine Seismology. Sensors, 12(3), 3693-3719. doi:10.3390/s120303693Jollymore, A., Johnson, M. S., & Hawthorne, I. (2012). Submersible UV-Vis Spectroscopy for Quantifying Streamwater Organic Carbon Dynamics: Implementation and Challenges before and after Forest Harvest in a Headwater Stream. Sensors, 12(4), 3798-3813. doi:10.3390/s120403798Won, T.-H., & Park, S.-J. (2012). Design and Implementation of an Omni-Directional Underwater Acoustic Micro-Modem Based on a Low-Power Micro-Controller Unit. Sensors, 12(2), 2309-2323. doi:10.3390/s120202309Sánchez, A., Blanc, S., Yuste, P., Perles, A., & Serrano, J. J. (2012). An Ultra-Low Power and Flexible Acoustic Modem Design to Develop Energy-Efficient Underwater Sensor Networks. Sensors, 12(6), 6837-6856. doi:10.3390/s120606837Shin, S.-Y., & Park, S.-H. (2011). A Cost Effective Block Framing Scheme for Underwater Communication. Sensors, 11(12), 11717-11735. doi:10.3390/s111211717Kim, Y., & Park, S.-H. (2011). A Query Result Merging Scheme for Providing Energy Efficiency in Underwater Sensor Networks. Sensors, 11(12), 11833-11855. doi:10.3390/s111211833Llor, J., & Malumbres, M. P. (2012). Underwater Wireless Sensor Networks: How Do Acoustic Propagation Models Impact the Performance of Higher-Level Protocols? Sensors, 12(2), 1312-1335. doi:10.3390/s120201312Zhang, G., Hovem, J. M., & Dong, H. (2012). Experimental Assessment of Different Receiver Structures for Underwater Acoustic Communications over Multipath Channels. Sensors, 12(2), 2118-2135. doi:10.3390/s120202118Ramezani, H., & Leus, G. (2012). Ranging in an Underwater Medium with Multiple Isogradient Sound Speed Profile Layers. Sensors, 12(3), 2996-3017. doi:10.3390/s120302996Lloret, J., Sendra, S., Ardid, M., & Rodrigues, J. J. P. C. (2012). Underwater Wireless Sensor Communications in the 2.4 GHz ISM Frequency Band. Sensors, 12(4), 4237-4264. doi:10.3390/s120404237Gao, M., Foh, C. H., & Cai, J. (2012). On the Selection of Transmission Range in Underwater Acoustic Sensor Networks. Sensors, 12(4), 4715-4729. doi:10.3390/s120404715Gómez, J. V., Sandnes, F. E., & Fernández, B. (2012). Sunlight Intensity Based Global Positioning System for Near-Surface Underwater Sensors. Sensors, 12(2), 1930-1949. doi:10.3390/s120201930Han, G., Jiang, J., Shu, L., Xu, Y., & Wang, F. (2012). Localization Algorithms of Underwater Wireless Sensor Networks: A Survey. Sensors, 12(2), 2026-2061. doi:10.3390/s120202026Moradi, M., Rezazadeh, J., & Ismail, A. S. (2012). A Reverse Localization Scheme for Underwater Acoustic Sensor Networks. Sensors, 12(4), 4352-4380. doi:10.3390/s120404352Lee, S., & Kim, K. (2012). Localization with a Mobile Beacon in Underwater Acoustic Sensor Networks. Sensors, 12(5), 5486-5501. doi:10.3390/s120505486Mohamed, N., Jawhar, I., Al-Jaroodi, J., & Zhang, L. (2011). Sensor Network Architectures for Monitoring Underwater Pipelines. Sensors, 11(11), 10738-10764. doi:10.3390/s111110738Macias, E., Suarez, A., Chiti, F., Sacco, A., & Fantacci, R. (2011). A Hierarchical Communication Architecture for Oceanic Surveillance Applications. Sensors, 11(12), 11343-11356. doi:10.3390/s111211343Zhang, S., Yu, J., Zhang, A., Yang, L., & Shu, Y. (2012). Marine Vehicle Sensor Network Architecture and Protocol Designs for Ocean Observation. Sensors, 12(1), 373-390. doi:10.3390/s120100373Climent, S., Capella, J. V., Meratnia, N., & Serrano, J. J. (2012). Underwater Sensor Networks: A New Energy Efficient and Robust Architecture. Sensors, 12(1), 704-731. doi:10.3390/s120100704Min, H., Cho, Y., & Heo, J. (2012). Enhancing the Reliability of Head Nodes in Underwater Sensor Networks. Sensors, 12(2), 1194-1210. doi:10.3390/s120201194Yoon, S., Azad, A. K., Oh, H., & Kim, S. (2012). AURP: An AUV-Aided Underwater Routing Protocol for Underwater Acoustic Sensor Networks. Sensors, 12(2), 1827-1845. doi:10.3390/s120201827Caiti, A., Calabrò, V., Dini, G., Lo Duca, A., & Munafò, A. (2012). Secure Cooperation of Autonomous Mobile Sensors Using an Underwater Acoustic Network. Sensors, 12(2), 1967-1989. doi:10.3390/s120201967Wu, H., Chen, M., & Guan, X. (2012). A Network Coding Based Routing Protocol for Underwater Sensor Networks. Sensors, 12(4), 4559-4577. doi:10.3390/s120404559Navarro, G., Huertas, I. E., Costas, E., Flecha, S., Díez-Minguito, M., Caballero, I., … Ruiz, J. (2012). Use of a Real-Time Remote Monitoring Network (RTRM) to Characterize the Guadalquivir Estuary (Spain). Sensors, 12(2), 1398-1421. doi:10.3390/s120201398Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., & Hernández, L. (2012). Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors. Sensors, 12(2), 1468-1481. doi:10.3390/s12020146

Arctic Domain Awareness Center DHS Center of Excellence (COE): Project Work Plan

As stated by the DHS Science &Technology Directorate, “The increased and diversified use of maritime spaces in the Arctic - including oil and gas exploration, commercial activities, mineral speculation, and recreational activities (tourism) - is generating new challenges and risks for the U.S. Coast Guard and other DHS maritime missions.” Therefore, DHS will look towards the new ADAC for research to identify better ways to create transparency in the maritime domain along coastal regions and inland waterways, while integrating information and intelligence among stakeholders. DHS expects the ADAC to develop new ideas to address these challenges, provide a scientific basis, and develop new approaches for U.S. Coast Guard and other DHS maritime missions. ADAC will also contribute towards the education of both university students and mid-career professionals engaged in maritime security. The US is an Arctic nation, and the Arctic environment is dynamic. We have less multi-year ice and more open water during the summer causing coastal villages to experience unprecedented storm surges and coastal erosion. Decreasing sea ice is also driving expanded oil exploration, bringing risks of oil spills. Tourism is growing rapidly, and our fishing fleet and commercial shipping activities are increasing as well. There continues to be anticipation of an economic pressure to open up a robust northwest passage for commercial shipping. To add to the stresses of these changes is the fact that these many varied activities are spread over an immense area with little connecting infrastructure. The related maritime security issues are many, and solutions demand increasing maritime situational awareness and improved crisis response capabilities, which are the focuses of our Work Plan. UAA understands the needs and concerns of the Arctic community. It is situated on Alaska’s Southcentral coast with the port facility through which 90% of goods for Alaska arrive. It is one of nineteen US National Strategic Seaports for the US DOD, and its airport is among the top five in the world for cargo throughput. However, maritime security is a national concern and although our focus is on the Arctic environment, we will expand our scope to include other areas in the Lower 48 states. In particular, we will develop sensor systems, decision support tools, ice and oil spill models that include oil in ice, and educational programs that are applicable to the Arctic as well as to the Great Lakes and Northeast. The planned work as detailed in this document addresses the DHS mission as detailed in the National Strategy for Maritime Security, in particular, the mission to Maximize Domain Awareness (pages 16 and 17.) This COE will produce systems to aid in accomplishing two of the objectives of this mission. They are: 1) Sensor Technology developing sensor packages for airborne, underwater, shore-based, and offshore platforms, and 2) Automated fusion and real-time simulation and modeling systems for decision support and planning. An integral part of our efforts will be to develop new methods for sharing of data between platforms, sensors, people, and communities.United States Department of Homeland SecurityCOE ADAC Objective/Purpose / Methodology / Center Management Team and Partners / Evaluation and Transition Plans / USCG Stakeholder Engagement / Workforce Development Strategy / Individual Work Plan by Projects Within a Theme / Appendix A / Appendix B / Appendix

Underwater Vehicles

For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties