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Mission-Critical Application Driven Intelligent Maritime Networks [electronic resource] /

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in Computer SciencePublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2020Edition: 1st ed. 2020Description: VIII, 78 p. 36 illus., 34 illus. in color. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789811544125
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 621.384 23
LOC classification:
  • TK5103.2-.4885
Online resources:
Contents:
Chapter 1. Introduction -- Chapter 2. Background and Literature Survey -- Chapter 3. Transmission Scheduling Based on Deep Reinforcement Learning in Software-Defined Maritime Communication Networks -- Chapter 4. Multi-vessel Computation Offloading in Maritime Mobile Edge Computing Network -- Chapter 5. The Application of Software-Defined Maritime Communication Networks:Maritime Search and Rescue -- Chapter 6. Conclusions and Future Directions. .
In: Springer Nature eBookSummary: This book shares valuable insights into high-efficiency data transmission scheduling and into a group intelligent search and rescue approach for artificial intelligence (AI)-powered maritime networks. Its goal is to highlight major research directions and topics that are critical for those who are interested in maritime communication networks, equipping them to carry out further research in this field. The authors begin with a historical overview and address the marine business, emerging technologies, and the shortcomings of current network architectures (coverage, connectivity, reliability, etc.). In turn, they introduce a heterogeneous space/air/sea/ground maritime communication network architecture and investigate the transmission scheduling problem in maritime communication networks, together with solutions based on deep reinforcement learning. To accommodate the computation demands of maritime communication services, the authors propose a multi-vessel offloadingalgorithm for maritime mobile edge computing networks. In closing, they discuss the applications of swarm intelligence in maritime search and rescue.
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Chapter 1. Introduction -- Chapter 2. Background and Literature Survey -- Chapter 3. Transmission Scheduling Based on Deep Reinforcement Learning in Software-Defined Maritime Communication Networks -- Chapter 4. Multi-vessel Computation Offloading in Maritime Mobile Edge Computing Network -- Chapter 5. The Application of Software-Defined Maritime Communication Networks:Maritime Search and Rescue -- Chapter 6. Conclusions and Future Directions. .

This book shares valuable insights into high-efficiency data transmission scheduling and into a group intelligent search and rescue approach for artificial intelligence (AI)-powered maritime networks. Its goal is to highlight major research directions and topics that are critical for those who are interested in maritime communication networks, equipping them to carry out further research in this field. The authors begin with a historical overview and address the marine business, emerging technologies, and the shortcomings of current network architectures (coverage, connectivity, reliability, etc.). In turn, they introduce a heterogeneous space/air/sea/ground maritime communication network architecture and investigate the transmission scheduling problem in maritime communication networks, together with solutions based on deep reinforcement learning. To accommodate the computation demands of maritime communication services, the authors propose a multi-vessel offloadingalgorithm for maritime mobile edge computing networks. In closing, they discuss the applications of swarm intelligence in maritime search and rescue.

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