An USV-based laser fluorosensor for oil spill detection

Liu, Deqing; Luan, Xiaoning; Feng, Zhang; Jin, Jiucai; Guo, Jinjia; Zheng, Ronger

doi:10.1109/icsenst.2016.7796237

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…In [ 98 ], a single hidden layer feedforward network was trained, using an extreme-learning model (ELM) to classify MFL signals in pipeline testing. In ELM, the parameters of the hidden layers of a neural network can be fixed, while the weights of the output neurons are resolved using the least square method.…”

Section: Pipeline Failure Detection Methodsmentioning

confidence: 99%

Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems

Wong

McCann

2021

Sensors

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Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention. In order to optimise the spatial use of the pipeline infrastructure, pipelines are either buried underground, or located in submarine environments. Due to the continuous expansion of pipeline networks in locations that are inaccessible to maintenance personnel, research efforts have been ongoing to introduce and develop reliable detection methods for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects. In this paper, a taxonomy of existing pipeline failure detection techniques and technologies was created to comparatively analyse their respective advantages, drawbacks and limitations. This effort has effectively illuminated various unaddressed research challenges that are still present among a wide array of the state-of-the-art detection methods that have been employed in various pipeline domains. These challenges include the extension of the lifetime of a pipeline network for the reduction of maintenance costs, and the prevention of disruptive pipeline failures for the minimisation of downtime. Our taxonomy of various pipeline failure detection methods is also presented in the form of a look-up table to illustrate the suitability, key aspects and data or signal processing techniques of each individual method. We have also quantitatively evaluated the industrial relevance and practicality of each of the methods in the taxonomy in terms of their respective deployability, generality and computational cost. The outcome of the evaluation made in the taxonomy will contribute to our future works involving the utilisation of sensor fusion and data-centric frameworks to develop efficient, accurate and reliable failure detection solutions.

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Section: Pipeline Failure Detection Methodsmentioning

confidence: 99%

Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems

Wong

McCann

2021

Sensors

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“…Unmanned surface vehicle is developed and analysed for performance in [6] by Deqing Liu et.al. Their work concentrates on frequent oil spills that happen at harbors, oil rigs and drilling platforms.…”

Section: Related Workmentioning

confidence: 99%

Secure Data Transmission Framework for Internet of Things based on Oil Spill Detection Application

Abhijith¹,

Rameshbabu²

2021

IJACSA

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Internet of Things (IoT) is a leading technology which can interlink anything to Internet and makes everything to intelligent and smart. IoT is not just a single technology. IoT is a combination of various technologies like communication, data analytics, sensors and actuators, cloud computing, artificial intelligence, machine learning, etc. Applications of IoT are spread across various domains. IoT is most suitable for remote applications like underwater networks. One such application is oil spill detection in ocean. Oil spill in an ocean is a critical challenge that causes damages to marine ecosystem. Detection of oil spills in a real time manner helps to resolve the problem quickly to minimize the damage. IoT can be used to detect the oil spill by making use of sensors deployed at various locations of ocean. With a massive amount of sensors deployed and the huge amount of data associated with it, there remain concerns about the data management. Also amount of data generated in IoT based remote sensing network is usually enormous for the servers to process and many times data generated are redundant. Hence there is a need for designing a framework which addresses both aggregation of data and security related issues at various aggregation points. In this paper we are proposing a secure data transmission framework for detecting oil spill through IoT, which avoids redundant data transmission through data aggregation and ensures secure data transmission through authentication and light weight encryption.

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“…Most works on oil spills detection use either UUVs [107,134], since they try to find the underwater location of the oil spill, or UAVs [135] since it is easier to estimate the amount of oil on the surface. Liu et al [105] affirm that in areas where oil spills frequently occur, such as coastal ports and oil drilling platform surroundings, the use of USV is seemingly more convenient when compared to traditional airborne and shipborne laser fluorosensors. Shipborne fluorosensors are not as agile or versatile as USVs, while airborne ones are more suitable for wide area detection.…”

Section: Use Of Usvs In Environmental Disasters With Contaminantsmentioning

confidence: 99%

“…Liu et al [105] describe the overall scheme of USV-based laser fluorosensor system for oil detection, which consists of a shore-based terminal and a laser fluorosensor mounted on a USV. The laser fluorosensor system sends the collected data to the shore-based terminal by wireless communication.…”

Section: Use Of Usvs In Environmental Disasters With Contaminantsmentioning

confidence: 99%

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

Jorge

Granada

Maidana

et al. 2019

Sensors

134

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Disaster robotics has become a research area in its own right, with several reported cases of successful robot deployment in actual disaster scenarios. Most of these disaster deployments use aerial, ground, or underwater robotic platforms. However, the research involving autonomous boats or Unmanned Surface Vehicles (USVs) for Disaster Management (DM) is currently spread across several publications, with varying degrees of depth, and focusing on more than one unmanned vehicle—usually under the umbrella of Unmanned Marine Vessels (UMV). Therefore, the current importance of USVs for the DM process in its different phases is not clear. This paper presents the first comprehensive survey about the applications and roles of USVs for DM, as far as we know. This work demonstrates that there are few current deployments in disaster scenarios, with most of the research in the area focusing on the technological aspects of USV hardware and software, such as Guidance Navigation and Control, and not focusing on their actual importance for DM. Finally, to guide future research, this paper also summarizes our own contributions, the lessons learned, guidelines, and research gaps.

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An USV-based laser fluorosensor for oil spill detection

Cited by 9 publications

References 9 publications

Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems

Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems

Secure Data Transmission Framework for Internet of Things based on Oil Spill Detection Application

A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions

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