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Mission-criticial application drivin...

Shen, Xuemin (Sherman).

Mission-criticial application driving intelligent maritime networks

Record Type:	Electronic resources : Monograph/item
Title/Author:	Mission-criticial application driving intelligent maritime networksby Tingting Yang, Xuemin (Sherman) Shen.
Author:	Yang, Tingting.
other author:	Shen, Xuemin (Sherman).
Published:	Singapore :Springer Singapore :2020.
Description:	viii, 78 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Wireless communication systems.
Online resource:	https://doi.org/10.1007/978-981-15-4412-5
ISBN:	9789811544125$q(electronic bk.)

Mission-criticial application driving intelligent maritime networks
Yang, Tingting.

Mission-criticial application driving intelligent maritime networks[electronic resource] /by Tingting Yang, Xuemin (Sherman) Shen. - Singapore :Springer Singapore :2020. - viii, 78 p. :ill., digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

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 offloading algorithm for maritime mobile edge computing networks. In closing, they discuss the applications of swarm intelligence in maritime search and rescue.

ISBN: 9789811544125$q(electronic bk.)

Standard No.: 10.1007/978-981-15-4412-5doiSubjects--Topical Terms:

182744
Wireless communication systems.

LC Class. No.: TK5103.2 / .Y35 2020

Dewey Class. No.: 621.382

Mission-criticial application driving intelligent maritime networks
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505 0 $a 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.
520 $a 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 offloading algorithm for maritime mobile edge computing networks. In closing, they discuss the applications of swarm intelligence in maritime search and rescue.
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