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Marine Wireless Sensor Networks: Applications and New Challenges
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Marine Wireless Sensor Networks: Applications and New Challenges

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (15 December 2021) | Viewed by 5190

Special Issue Editors

Prof. Dr. Joaquin Aranda

E-Mail Website
Guest Editor
Department of Computer Science and Automatic Control, National University Distance Education (UNED), Juan del Rosal 16, 28040 Madrid, Spain
Interests: Localization, underwater systems, sensor networks, multiple objective optimization, marine systems control
Dr. Ernesto Aranda-Escolastico

E-Mail Website
Guest Editor
Department of Software and Systems Engineering, Universidad Nacional de Educación a Distancia (UNED), 28040 Madrid, Spain
Interests: nonlinear systems, underwater vehicles, underwater communication, networked control systems, multi-agent systems, target tracking

Special Issue Information

Dear Colleagues,

The development of wireless sensor networks is creating significant impact in different fields, including marine systems, where hardware costs, specific topologies, or energy consumption are especially relevant. Recent advances in sensor networks are increasing their scientific and commercial applications in marine environments. However, ocean exploration and utilization still pose numerous challenges. In this context, efficient, robust, and intelligent sensor networks may be necessary.

This Special Issue aims to collect research papers on the development and applications of marine sensor networks, including architecture and topology problems, tracking and localization algorithms, marine vehicles control, and the identification of new challenges.

Prof. Dr. Joaquin Aranda
Dr. Ernesto Aranda-Escolastico
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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 wireless sensor networks
  • underwater sensors
  • floating sensors
  • marine instrumentation
  • underwater communication
  • architecture and topology of wireless sensor networks
  • target tracking and detection
  • control of marine systems
  • applications with marine sensor networks
  • new challenges of UWSNs

Published Papers (2 papers)

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18 pages, 7624 KiB  
Article
LQG Control for Dynamic Positioning of Floating Caissons Based on the Kalman Filter
by Jose Joaquin Sainz, Elías Revestido Herrero, Jose Ramon Llata, Esther Gonzalez-Sarabia, Francisco J. Velasco, Alvaro Rodriguez-Luis, Sergio Fernandez-Ruano and Raul Guanche
Sensors 2021, 21(19), 6496; https://doi.org/10.3390/s21196496 - 29 Sep 2021
Cited by 5 | Viewed by 2563
Abstract
This paper presents the application of an linear quadratic gaussian (LQG) control strategy for concrete caisson deployment for marine structures. Currently these maneuvers are carried out manually with the risk that this entails. Control systems for these operations with classical regulators have begun [...] Read more.
This paper presents the application of an linear quadratic gaussian (LQG) control strategy for concrete caisson deployment for marine structures. Currently these maneuvers are carried out manually with the risk that this entails. Control systems for these operations with classical regulators have begun to be implemented. They try to reduce risks, but they still need to be optimized due to the complexity of the dynamics involved during the sinking process and the contact with the sea bed. A linear approximation of the dynamic model of the caisson is obtained and an LQG control strategy is implemented based on the Kalman filter (KF). The results of the proposed LQG control strategy are compared to the ones given by a classic controller. It is noted that the proposed system is positioned with greater precision and accuracy, as shown in the different simulations and in the Monte Carlo study. Furthermore, the control efforts are less than with classical regulators. For all the reasons cited above, it is concluded that there is a clear improvement in performance with the control system proposed. Full article
(This article belongs to the Special Issue Marine Wireless Sensor Networks: Applications and New Challenges)
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15 pages, 1922 KiB  
Article
Deep Learning for Efficient and Optimal Motion Planning for AUVs with Disturbances
by Juan Parras, Patricia A. Apellániz and Santiago Zazo
Sensors 2021, 21(15), 5011; https://doi.org/10.3390/s21155011 - 23 Jul 2021
Cited by 4 | Viewed by 1934
Abstract
We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools—namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton–Jacobi–Bellman PDE that can be used to solve continuous time [...] Read more.
We use the recent advances in Deep Learning to solve an underwater motion planning problem by making use of optimal control tools—namely, we propose using the Deep Galerkin Method (DGM) to approximate the Hamilton–Jacobi–Bellman PDE that can be used to solve continuous time and state optimal control problems. In order to make our approach more realistic, we consider that there are disturbances in the underwater medium that affect the trajectory of the autonomous vehicle. After adapting DGM by making use of a surrogate approach, our results show that our method is able to efficiently solve the proposed problem, providing large improvements over a baseline control in terms of costs, especially in the case in which the disturbances effects are more significant. Full article
(This article belongs to the Special Issue Marine Wireless Sensor Networks: Applications and New Challenges)
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