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Record Nr. |
UNISA996465460503316 |
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Autore |
Yang Tingting |
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Titolo |
Mission-Critical Application Driven Intelligent Maritime Networks [[electronic resource] /] / by Tingting Yang, Xuemin (Sherman) Shen |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (VIII, 78 p. 36 illus., 34 illus. in color.) |
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Collana |
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SpringerBriefs in Computer Science, , 2191-5768 |
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Disciplina |
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Soggetti |
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Wireless communication systems |
Mobile communication systems |
Computer communication systems |
Electrical engineering |
Wireless and Mobile Communication |
Computer Communication Networks |
Communications Engineering, Networks |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. . |
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Sommario/riassunto |
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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 |
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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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