Sensor Network Architectures for Monitoring Underwater Pipelines

This paper develops and compares different sensor network architecture designs that can be used for monitoring underwater pipeline infrastructures. These architectures are underwater wired sensor networks, underwater acoustic wireless sensor networks, RF (Radio Frequency) wireless sensor networks, integrated wired/acoustic wireless sensor networks, and integrated wired/RF wireless sensor networks. The paper also discusses the reliability challenges and enhancement approaches for these network architectures. The reliability evaluation, characteristics, advantages, and disadvantages among these architectures are discussed and compared. Three reliability factors are used for the discussion and comparison: the network connectivity, the continuity of power supply for the network, and the physical network security. In addition, the paper also develops and evaluates a hierarchical sensor network framework for underwater pipeline monitoring

Performance enhancement of sensor network architecture for monitoring underwater oil pipeline

In this paper, a deployment mechanism is designed to distribute heterogeneous nodes to optimally cover the pipeline where the mechanism helps locate each node on the wall of the oil pipeline where the number of nodes can be increased depending on this mechanism. The six-layer network hierarchy includes basic sensor nodes (BSN), aggregation relay node (ARN) that added to the network hierarchy, data relay nodes (DRN), data dissemination node (DDN), base station (sinks), and network control center (NCC). This network relies on the improved smart redirect or jump algorithm (SRJ) by sending packets depend on the active relay nodes in both directions that are within the transmission range of the ARNs instead of relying on the number of hops adopted by the SRJ algorithm to reduce the network delay, the energy consumed in relay nodes, and the number of times the DRNs increased transmission range. The OMNeT++ and MATLAB programs were used to implement the simulation scenario. The results showed superiority in terms of the average overhead communication, energy consumption, and end to the end delay with network delay in some cases rely on the number of active relay nodes

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

Autonomous pipeline monitoring and maintenance system: a RFID-based approach

Pipeline networks are one of the key infrastructures of our modern life. Proactive monitoring and frequent inspection of pipeline networks are very important for sustaining their safe and efficient functionalities. Existing monitoring and maintenance approaches are costly and inefficient because pipelines can be installed in large scale and in an inaccessible and hazardous environment. To overcome these challenges, we propose a novel Radio Frequency IDentification (RFID)-based Autonomous Maintenance system for Pipelines, called RAMP, which combines robotic, sensing, and RFID technologies for efficient and accurate inspection, corrective reparation, and precise geo-location information. RAMP can provide not only economical and scalable remedy but also safe and customizable solution. RAMP also allows proactive and corrective monitoring and maintenance of pipelines. One prominent advantage of RAMP is that it can be applied to a large variety of pipeline systems including water, sewer, and gas pipelines. Simulation results demonstrate the feasibility and superior performance of RAMP in comparison to the existing pipeline monitoring systems

Sensor-based autonomous pipeline monitoring robotic system

The field of robotics applications continues to advance. This dissertation addresses the computational challenges of robotic applications and translations of actions using sensors. One of the most challenging fields for robotics applications is pipeline-based applications which have become an indispensable part of life. Proactive monitoring and frequent inspections are critical in maintaining pipeline health. However, these tasks are highly expensive using traditional maintenance systems, knowing that pipeline systems can be largely deployed in an inaccessible and hazardous environment. Thus, we propose a novel cost effective, scalable, customizable, and autonomous sensor-based robotic system, called SPRAM System (Sensor-based Autonomous Pipeline Monitoring Robotic System). It combines robot agent based technologies with sensing technologies for efficiently locating health related events and allows active and corrective monitoring and maintenance of the pipelines. The SPRAM System integrates RFID systems with mobile sensors and autonomous robots. While the mobile sensor motion is based on the fluid transported by the pipeline, the fixed sensors provide event and mobile sensor location information and contribute efficiently to the study of health history of the pipeline. In addition, it permits a good tracking of the mobile sensors. Using the output of event analysis, a robot agent gets command from the controlling system, travels inside the pipelines for detailed inspection and repairing of the reported incidents (e.g., damage, leakage, or corrosion). The key innovations of the proposed system are 3-fold: (a) the system can apply to a large variety of pipeline systems; (b) the solution provided is cost effective since it uses low cost powerless fixed sensors that can be setup while the pipeline system is operating; (c) the robot is autonomous and the localization technique allows controllable errors. In this dissertation, some simulation experiments described along with prototyping activities demonstrate the feasibility of the proposed system

Leak Detection and Location Technology Assessment for Aerospace Applications

Micro Meteoroid and Orbital Debris (MMOD) and other impacts can cause leaks in the International Space Station and other aerospace vehicles. The early detection and location of leaks is paramount to astronaut safety. Therefore this document surveys the state of the art in leak detection and location technology for aerospace vehicles

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