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Sensors and Underwater Robotics Network
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Sensors and Underwater Robotics Network

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

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 6706

Special Issue Editor

Prof. Dr. Jingjing Wang

E-Mail Website
Guest Editor
School of Cyber Science and Technology, Beihang University, Beijing 100191, China
Interests: underwater acoustic communication; routing protocols; medium access control; UAV networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the face of deeply exploiting marine resources, underwater sensors and underwater robotics networks have drawn great attention considering their flexibility and easy deployment. However, given the hostile underwater environment, it is critical to design efficient information collection because of limited power supply and constraint communication resources. As a promising solution for underwater exploration, underwater sensors and robotics usually equipped with diverse payloads for acoustic communications energy supply and information processing can be viewed as Internet of Underwater Things (IoUT) nodes relying on dedicated deployment and trajectory design. Therefore, based on these underwater units, we can significantly facilitate the energy efficiency and communication coverage of IoUT networks.

This Special Issue will focus on the use of underwater sensors (fixed or mobile) to address underwater information collection and processing. The purpose of this Special Issue is to solicit original research papers on all aspects of IoUT, including but not limited to:

  • Underwater sensor deployment;
  • Underwater robot/vehicle trajectory design;
  • Underwater multisource sensor fusion;
  • Underwater network optimization and resource allocation;
  • Underwater routing protocol;
  • Machine-learning-aided IoUT networks;
  • Localization in underwater sensor networks;
  • Underwater information collection systems;
  • Underwater information fusion;

Dr. Jingjing Wang
Guest Editor

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

  • Internet of Underwater Things
  • underwater sensing networks
  • underwater sensors and robots
  • underwater network optimization

Published Papers (3 papers)

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Research

16 pages, 751 KiB  
Article
Efficient Asynchronous Federated Learning for AUV Swarm
by Zezhao Meng, Zhi Li, Xiangwang Hou, Jun Du, Jianrui Chen and Wei Wei
Sensors 2022, 22(22), 8727; https://doi.org/10.3390/s22228727 - 11 Nov 2022
Cited by 1 | Viewed by 1366
Abstract
The development of automatic underwater vehicles (AUVs) has brought about unprecedented profits and opportunities. In order to discover the hidden valuable data detected by an AUV swarm, it is necessary to aggregate the data detected by AUV swarm to generate a powerful machine [...] Read more.
The development of automatic underwater vehicles (AUVs) has brought about unprecedented profits and opportunities. In order to discover the hidden valuable data detected by an AUV swarm, it is necessary to aggregate the data detected by AUV swarm to generate a powerful machine learning model. Traditional centralized machine learning generates a large number of data exchanges and faces problems of enormous training data, large-scale models, and communication. In underwater environments, radio waves are strongly absorbed, and acoustic communication is the only feasible technology. Unlike electromagnetic wave communication on land, the bandwidth of underwater acoustic communication is extremely limited, with the transmission rate being only 1/105 of the electromagnetic wave. Therefore, traditional centralized machine learning cannot support underwater AUV swarm training. In recent years, federated learning could only interact with model parameters without interacting with data, which greatly reduced communication costs. Therefore, this paper introduces federated learning into the collaboration of an AUV swarm. In order to further reduce the constraints of underwater scarce communication resources on federated learning and alleviate the straggler effect, in this work, we designed an asynchronous federated learning method. Finally, we constructed the optimization problem of minimizing the weighted sum of delay and energy consumption, relying on jointly optimizing the AUV CPU frequency and signal transmission power. In order to solve this complex optimization problem of high-dimensional non-convex time series accumulation, we transformed the problem into a Markov decision process (MDP) and use the proximal policy optimization 2 (PPO2) algorithm to solve this problem. The simulation results demonstrate the effectiveness and superiority of our method. Full article
(This article belongs to the Special Issue Sensors and Underwater Robotics Network)
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20 pages, 8700 KiB  
Article
Design and Implementation of a Modular UUV Simulation Platform
by Zekai Zhang, Weishi Mi, Jun Du, Ziyuan Wang, Wei Wei, Yuang Zhang, Yutong Yang and Yong Ren
Sensors 2022, 22(20), 8043; https://doi.org/10.3390/s22208043 - 21 Oct 2022
Cited by 3 | Viewed by 2919
Abstract
The complex and time-varying marine environment puts forward demanding requirements for the structural design and algorithm development of unmanned underwater vehicles (UUVs). It is inevitable to repeatedly evaluate the feasibility of autonomy schemes to enhance the intelligence and security of the UUV before [...] Read more.
The complex and time-varying marine environment puts forward demanding requirements for the structural design and algorithm development of unmanned underwater vehicles (UUVs). It is inevitable to repeatedly evaluate the feasibility of autonomy schemes to enhance the intelligence and security of the UUV before putting it into use. Considering the high cost of the UUV hardware platform and the high risk of underwater experiments, this study aims to evaluate and optimize autonomy schemes in the manner of software-in-loop (SIL) simulation efficiently. Therefore, a self-feedback development framework is proposed and a multi-interface, programmable modular simulation platform for UUV based on a robotic operating system (ROS) is designed. The platform integrates the 3D marine environment, UUV models, sensor plugins, motion control plugins in a modular manner, and reserves programming interfaces for users to test various algorithms. Subsequently, we demonstrate the simulation details with cases, such as single UUV path planning, task scheduling, and multi-UUV formation control, and construct underwater experiments to confirm the feasibility of the simulation platform. Finally, the extensibility of the simulation platform and the related performance analysis are discussed. Full article
(This article belongs to the Special Issue Sensors and Underwater Robotics Network)
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15 pages, 2299 KiB  
Article
Research on Multi-AUVs Data Acquisition System of Underwater Acoustic Communication Network
by Chunxian Gao, Wenwen Hu and Keyu Chen
Sensors 2022, 22(14), 5090; https://doi.org/10.3390/s22145090 - 06 Jul 2022
Cited by 4 | Viewed by 1776
Abstract
In order to meet the needs of large-scale underwater operations, the underwater acoustic communication network emerged, marking a historic moment. At the same time, the development of artificial intelligence has promoted the application of intelligent underwater robots in large-scale underwater operations, and the [...] Read more.
In order to meet the needs of large-scale underwater operations, the underwater acoustic communication network emerged, marking a historic moment. At the same time, the development of artificial intelligence has promoted the application of intelligent underwater robots in large-scale underwater operations, and the research on related algorithms has been gradually promoted. Due to the complexity of underwater operations and the difficulty of replacing batteries, the energy efficiency of intelligent underwater robots is particularly important in multi-AUVs data acquisition systems. In view of the energy consumption of multi-AUVs data acquisition systems in water acoustic cluster networks, this paper proposed the AE (A*-Energy) algorithm for multi-AUVs task assignment and path planning. Through the simulation experiment, it was proved that the AE algorithm proposed in this paper can effectively reduce the energy consumption of multi-AUVs data acquisition systems and has good energy efficiency. Full article
(This article belongs to the Special Issue Sensors and Underwater Robotics Network)
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