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Machine-Learning-Aided Mission-Critical Internet of Underwater Things

  • Xiangwang Hou*
  • , Jingjing Wang*
  • , Zhengru Fang
  • , Xin Zhang
  • , Shenghui Song
  • , Xudong Zhang
  • , Yong Ren
  • *此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

With people paying more attention to marine resources, the Internet of Things (IoT) has been extended to underwater, promoting the development of the Internet of Underwater Things (IoUT). Various compelling IoUT applications bring a new age to maritime activities. However, some mis-sion-critical maritime activities, including ocean earthquake forecasting, underwater navigation, and so on, pose a substantial challenge on existing IoUT architectures and relevant techniques. Therefore, in this article, to empower these implacable maritime activities, we conceive the concept of mission-critical IoUT and highlight its key features and challenges. Furthermore, to satisfy the stringent requirements of mission-critical IoUT, we propose a future maritime network architecture and machine-learning-aided key techniques in terms of information sensing, transmission, and processing. Moreover, we present our recent research on reliable and low-latency underwater information transmission. Finally, we provide the open issues and potential research trends for future mission-critical IoUT.

源语言英语
文章编号9520368
页(从-至)160-166
页数7
期刊IEEE Network
35
4
DOI
出版状态已出版 - 1 7月 2021
已对外发布

联合国可持续发展目标

此成果有助于实现下列可持续发展目标:

  1. 可持续发展目标 14 - 水下生物
    可持续发展目标 14 水下生物

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