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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (30 June 2017) | Viewed by 64620

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Special Issue Editors

Prof. Dr. Wei Song

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Guest Editor

College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Interests: marine big data analysis; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Antonio Liotta

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Guest Editor

1. Intelligent Lighting Institute, Technische Universiteit Eindhoven, Eindhoven, The Netherlands
2. Department of Electrical Engineering, Technische Universiteit Eindhoven, Eindhoven, The Netherlands
Interests: smart lighting; smart sensing; urban informatics; network science; internet of things; machine learning; cognitive networks; network efficiency; energy efficiency
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Ge Chen

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Guest Editor

1. College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2. National Laboratory for Marine Science and Technology, Qingdao 266100, China
Interests: ocean remote sensing; big data oceanography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The oceans cover 71% of the Earth's surface, 95% are unexplored. Oceans contain 99% of the living space on the planet, yet less than 10% of it has been explored thus far. Ocean exploitation and utilization are closely related to human survival and development, from the long-term impact on global climate change and large-scale phenomena, such as hurricanes, to sustainable development of ocean resources and ecosystems, and to sensitive issues in defense and security. Today, ocean research is widely enhanced by marine sensing technologies, including in situ sampling on buoys, floats, underwater vehicles, integrated sensor networks, and observatories, as well as remote sensing, such as airborne and satellite remote sensing (both active and passive). However, how to intelligently sense the ocean still poses numerous challenges.

Marine sensing requires, not only the ability to sense data timely and accurately from multiple dimensions of space, sea surface and deep sea, but also the intelligence to integrate the data from different sensor systems to predict future environmental conditions, and support decision making. This Special Issue intends to cover current technology and environmental limitations, system decision, implementation and application issues, as well as new technology that may be applied to marine sensing problems. Through research on in situ and remote monitoring of the ocean surface, water column, deep sea, bathymetric and benthic features, effective sensor network and communication systems, advanced sensor data processing and analysis techniques, and the related marine sensing applications, we wish to better understand the challenging ocean environments.

This Special Issue aims to bring together members of the industrial and scientific communities, to identify challenges confronted in ocean exploration and understanding, share and exchange novel ideas in developing future marine sensing technologies and applications, and investigate new technologies for solving key problems in marine sensing.

The topics of interest for contributions to this Special Issue include, but are not limited to:

emerging sensing and monitoring techniques for in-situ sampling, real-time observation, remote sensing, and underwater electro-optical sensors and systems
deep sea sensing and operation
intelligent underwater sensor networks and communication
marine Internet
sensor big data management, quality assessment and control
multi-modal sensor data processing, integration and fusion
marine remote sensing image processing and data analysis
multimedia techniques for marine sensing, data processing and data visualization
safety and security of marine sensing
energy efficiency of sensors and sensor communications
marine disaster sensing and forecasting (e.g., storm urge)
marine sensing applications and services in ocean resource and environment protection, such as sea-ice monitoring, marine fishery, marine navigation and marine disaster decision-making support

Prof. Dr. Wei Song
Prof. Dr. Antonio Liotta
Prof. Dr. Ge Chen
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

marine sensing
marine instrumentation
marine data analytics
smart sensing
sensor networks
marine data communication
internet of things
big data
machine learning

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Published Papers (10 papers)

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Research

1737 KiB

Open AccessArticle

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

by Dongmei Huang, Chenyixuan Xu, Danfeng Zhao, Wei Song and Qi He

Sensors 2017, 17(10), 2168; https://doi.org/10.3390/s17102168 - 21 Sep 2017

Cited by 3 | Viewed by 4301

Abstract

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. Full article

(This article belongs to the Special Issue Marine Sensing)

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932 KiB

Open AccessArticle

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

by Lifei Wei, Lei Zhang, Dongmei Huang, Kai Zhang, Liang Dai and Guojian Wu

Sensors 2017, 17(9), 2117; https://doi.org/10.3390/s17092117 - 14 Sep 2017

Cited by 6 | Viewed by 3417

Abstract

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. Full article

(This article belongs to the Special Issue Marine Sensing)

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5754 KiB

Open AccessArticle

Position, Orientation and Velocity Detection of Unmanned Underwater Vehicles (UUVs) Using an Optical Detector Array

by Firat Eren, Shachak Pe’eri, May-Win Thein, Yuri Rzhanov, Barbaros Celikkol and M. Robinson Swift

Sensors 2017, 17(8), 1741; https://doi.org/10.3390/s17081741 - 29 Jul 2017

Cited by 35 | Viewed by 6487

Abstract

This paper presents a proof-of-concept optical detector array sensor system to be used in Unmanned Underwater Vehicle (UUV) navigation. The performance of the developed optical detector array was evaluated for its capability to estimate the position, orientation and forward velocity of UUVs with respect to a light source fixed in underwater. The evaluations were conducted through Monte Carlo simulations and empirical tests under a variety of motion configurations. Monte Carlo simulations also evaluated the system total propagated uncertainty (TPU) by taking into account variations in the water column turbidity, temperature and hardware noise that may degrade the system performance. Empirical tests were conducted to estimate UUV position and velocity during its navigation to a light beacon. Monte Carlo simulation and empirical results support the use of the detector array system for optics based position feedback for UUV positioning applications. Full article

(This article belongs to the Special Issue Marine Sensing)

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4783 KiB

Open AccessArticle

A Novel Detection Method for Underwater Moving Targets by Measuring Their ELF Emissions with Inductive Sensors

by Jinhong Wang, Bin Li, Lianping Chen and Li Li

Sensors 2017, 17(8), 1734; https://doi.org/10.3390/s17081734 - 28 Jul 2017

Cited by 19 | Viewed by 6080

Abstract

In this article, we propose a novel detection method for underwater moving targets by detecting their extremely low frequency (ELF) emissions with inductive sensors. The ELF field source of the targets is modeled by a horizontal electric dipole at distances more than several times of the targets’ length. The formulas for the fields produced in air are derived with a three-layer model (air, seawater and seafloor) and are evaluated with a complementary numerical integration technique. A proof of concept measurement is presented. The ELF emissions from a surface ship were detected by inductive electronic and magnetic sensors as the ship was leaving a harbor. ELF signals are of substantial strength and have typical characteristic of harmonic line spectrum, and the fundamental frequency has a direct relationship with the ship’s speed. Due to the high sensitivity and low noise level of our sensors, it is capable of resolving weak ELF signals at long distance. In our experiment, a detection distance of 1300 m from the surface ship above the sea surface was realized, which shows that this method would be an appealing complement to the usual acoustic detection and magnetic anomaly detection capability. Full article

(This article belongs to the Special Issue Marine Sensing)

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1108 KiB

Open AccessArticle

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

by Mengzhao Yang, Wei Song and Haibin Mei

Sensors 2017, 17(7), 1693; https://doi.org/10.3390/s17071693 - 23 Jul 2017

Cited by 7 | Viewed by 5386

Abstract

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient. Full article

(This article belongs to the Special Issue Marine Sensing)

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6343 KiB

Open AccessArticle

Towards a Cognitive Radar: Canada’s Third-Generation High Frequency Surface Wave Radar (HFSWR) for Surveillance of the 200 Nautical Mile Exclusive Economic Zone

by Anthony Ponsford, Rick McKerracher, Zhen Ding, Peter Moo and Derek Yee

Sensors 2017, 17(7), 1588; https://doi.org/10.3390/s17071588 - 7 Jul 2017

Cited by 18 | Viewed by 7362

Abstract

Canada’s third-generation HFSWR forms the foundation of a maritime domain awareness system that provides enforcement agencies with real-time persistent surveillance out to and beyond the 200 nautical mile exclusive economic zone (EEZ). Cognitive sense-and-adapt technology and dynamic spectrum management ensures robust and resilient operation in the highly congested High Frequency (HF) band. Dynamic spectrum access enables the system to simultaneously operate on two frequencies on a non-interference and non-protected basis, without impacting other spectrum users. Sense-and-adapt technologies ensure that the system instantaneously switches to a new vacant channel on the detection of another user or unwanted jamming signal. Adaptive signal processing techniques mitigate against electrical noise, interference and clutter. Sense-and-adapt techniques applied at the detector and tracker stages maximize the probability of track initiation whilst minimizing the probability of false or otherwise erroneous track data. Full article

(This article belongs to the Special Issue Marine Sensing)

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1213 KiB

Open AccessArticle

Doppler Navigation System with a Non-Stabilized Antenna as a Sea-Surface Wind Sensor

by Alexey Nekrasov, Alena Khachaturian, Vladimir Veremyev and Mikhail Bogachev

Sensors 2017, 17(6), 1340; https://doi.org/10.3390/s17061340 - 9 Jun 2017

Cited by 11 | Viewed by 5619

Abstract

We propose a concept of the utilization of an aircraft Doppler Navigation System (DNS) as a sea-surface wind sensor complementary to its normal functionality. The DNS with an antenna, which is non-stabilized physically to the local horizontal with x-configured beams, is considered. We consider the wind measurements by the DNS configured in the multi-beam scatterometer mode for a rectilinear flight scenario. The system feasibility and the efficiency of the proposed wind algorithm retrieval are supported by computer simulations. Finally, the associated limitations of the proposed approach are considered. Full article

(This article belongs to the Special Issue Marine Sensing)

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31763 KiB

Open AccessArticle

Underwater Depth and Temperature Sensing Based on Fiber Optic Technology for Marine and Fresh Water Applications

by Dinesh Babu Duraibabu, Gabriel Leen, Daniel Toal, Thomas Newe, Elfed Lewis and Gerard Dooly

Sensors 2017, 17(6), 1228; https://doi.org/10.3390/s17061228 - 27 May 2017

Cited by 57 | Viewed by 12684

Abstract

Oceanic conditions play an important role in determining the effects of climate change and these effects can be monitored through the changes in the physical properties of sea water. In fact, Oceanographers use various probes for measuring the properties within the water column. CTDs (Conductivity, Temperature and Depth) provide profiles of physical and chemical parameters of the water column. A CTD device consists of Conductivity (C), Temperature (T) and Depth (D) probes to monitor the water column changes with respect to relative depth. An optical fibre-based point sensor used as a combined pressure (depth) and temperature sensor and the sensor system are described. Measurements accruing from underwater trials of a miniature sensor for pressure (depth) and temperature in the ocean and in fresh water are reported. The sensor exhibits excellent stability and its performance is shown to be comparable with the Sea-Bird Scientific commercial sensor: SBE9Plus. Full article

(This article belongs to the Special Issue Marine Sensing)

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3890 KiB

Open AccessArticle

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data

by Yanling Han, Jue Li, Yun Zhang, Zhonghua Hong and Jing Wang

Sensors 2017, 17(5), 1124; https://doi.org/10.3390/s17051124 - 15 May 2017

Cited by 26 | Viewed by 5028

Abstract

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection. Full article

(This article belongs to the Special Issue Marine Sensing)

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2560 KiB

Open AccessArticle

Performance Evaluation and Analysis for Gravity Matching Aided Navigation

by Lin Wu, Hubiao Wang, Hua Chai, Lu Zhang, Houtse Hsu and Yong Wang

Sensors 2017, 17(4), 769; https://doi.org/10.3390/s17040769 - 5 Apr 2017

Cited by 27 | Viewed by 5278

Abstract

Simulation tests were accomplished in this paper to evaluate the performance of gravity matching aided navigation (GMAN). Four essential factors were focused in this study to quantitatively evaluate the performance: gravity database (DB) resolution, fitting degree of gravity measurements, number of samples in matching, and gravity changes in the matching area. Marine gravity anomaly DB derived from satellite altimetry was employed. Actual dynamic gravimetry accuracy and operating conditions were referenced to design the simulation parameters. The results verified that the improvement of DB resolution, gravimetry accuracy, number of measurement samples, or gravity changes in the matching area generally led to higher positioning accuracies, while the effects of them were different and interrelated. Moreover, three typical positioning accuracy targets of GMAN were proposed, and the conditions to achieve these targets were concluded based on the analysis of several different system requirements. Finally, various approaches were provided to improve the positioning accuracy of GMAN. Full article

(This article belongs to the Special Issue Marine Sensing)

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