Topical Collection on "Role of IoT in Enhancing Autonomous Underwater Vehicle (AUV) Operations for Marine Research"

Deadline for manuscript submissions: 06 January 2025

Collection Editors

Dr. Muhammad Zakarya

Faculty of Computing and Information Technology, Sohar University, Oman
E-Mail: MZakarya@su.edu.om/muha.zakarya@gmail.com
Interests: Platforms and infrastructures; Edge computing; Deep learning; Energy efficiency; Internet of Things

 

Dr. Santosh Tirunagari

Department of Computer Science, School of Science and Technology, Middlesex University, UK
E-Mail: s.tirunagari@mdx.ac.uk
Interests: Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Dimensionality Reduction

 

Prof. Dr. Jinguang Han

School of Cyber Science and Engineering, Southeast University, China
E-Mail: jghan@seu.edu.cn
Interests: Cryptography, Access Control, Privacy

 

Dr. Peiying Zhang

China University of Petroleum (East China), China
E-Mail: zhangpeiying@upc.edu.cn
Interests: Semantic Computing, Future Network Architecture

 

Topical Collection Information

Dear Colleagues,

The development of an intelligent ocean now depends on the advent of the Internet of Underwater Things (IoUT), a potent technological advancement. The method relies heavily on autonomous underwater vehicles (AUVs) because of their mobility and extended energy storage. The problems with AUVs need to be sufficiently resolved for AUV technology. The Internet of Underwater Things, a new and expanded category of the Internet of Things (IoT), is gaining popularity as a means of monitoring and using the ocean. The current application to support the idea of IoUT, however, has a lot of information-gathering issues. This suggests an autonomous underwater vehicle-aided hierarchical information-gathering system made up of an In assessing the state of the environment today, a long-term and efficient monitoring program is required. Increasing quantities of external inputs and contaminants are being introduced into marine ecosystems specifically, which could have a significant impact on their variety and the frequency of their techniques, with implications for the entire system.

Autonomous underwater vehicles are often less expensive and take less time to complete than traditional techniques; they are widely employed for exploration and monitoring tasks. Actually, AUVs have been extensively used in a variety of marine sectors in recent years. Observing forest fires, conducting aerial surveys, conducting military and surveillance missions, and, of course, observing the environment are a few of the primary examples. The AUVs' beneficial properties include their capacity to track and adjust their trajectory, as well as their ability to equip the vehicle with a wide range of instruments and sensors, making them ideal for gathering various types of data. AUV formation is a coordinated control strategy that centers on directing many AUVs to travel together as a collective to complete tasks. Multi-AUV formations offer greater efficiency and stability compared to an individual AUV in several applications, including hydrographic assessments, warfare, and the oil and industry sectors. Enhanced formation can potentially be attained through many critical elements, such as AUV efficiency, formation management, and communication proficiency. However, development control techniques are the primary focus of the majority of efforts in this aspect of AUV formation.

The general facts about autonomous underwater vehicles are presented in this special issue. The related concepts significantly influence factors that go into designing fully autonomous vehicles. AUV motion layer and a marine stationary sensor layer to address these problems. In contrast to the majority of current data collection methods, this disregards the angle manipulation and energy usage of underwater nodes for sensors.

Collection Editors
Dr. Muhammad Zakarya
Dr. Santosh Tirunagari
Prof. Dr. Jinguang Han
Dr. Peiying Zhang

Keywords:

  • Autonomous Underwater Vehicle
  • Marine Research
  • Ocean science
  • Locating and gathering data
  • Environment system