Reinforcement Learning for Maritime Communications
This book demonstrates that the reliable and secure communication performance of maritime communications can be significantly improved by using intelligent reflecting surface (IRS) aided communication, privacy-aware Internet of Things (IoT) communications, intelligent resource management and locatio...
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| Main Authors: | , , , |
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| Autor Corporativo: | |
| Formato: | Recurso Electrónico livro electrónico |
| Idioma: | inglês |
| Publicado em: |
Cham :
Springer International Publishing : Imprint: Springer,
2023.
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| Edição: | 1st ed. 2023. |
| Colecção: | Wireless Networks,
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| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1007/978-3-031-32138-2 |
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| 100 | 1 | |a Xiao, Liang. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Reinforcement Learning for Maritime Communications |h [electronic resource] / |c by Liang Xiao, Helin Yang, Weihua Zhuang, Minghui Min. |
| 250 | |a 1st ed. 2023. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2023. | |
| 300 | |a 1 online resource (IX, 146 p. 50 illus.). |b 49 illus. in color. | ||
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| 341 | 0 | |b Table of contents navigation |2 onix | |
| 341 | 0 | |b Single logical reading order |2 onix | |
| 341 | 0 | |b Short alternative textual descriptions |2 onix | |
| 341 | 0 | |b Use of color is not sole means of conveying information |2 onix | |
| 341 | 0 | |b Use of high contrast between text and background color |2 onix | |
| 341 | 0 | |b Next / Previous structural navigation |2 onix | |
| 341 | 0 | |b All non-decorative content supports reading without sight |2 onix | |
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| 490 | 1 | |a Wireless Networks, |x 2366-1445 | |
| 505 | 0 | |a Introduction -- Intelligent Internet of Things Networking Architecture -- Intelligent IoT Network Awareness -- Intelligent Traffic Control -- Intelligent Resource Scheduling -- Mobile Edge Computing Enabled Intelligent IoT -- Blockchain Enabled Intelligent IoT -- Conclusions and Future Challenges. | |
| 520 | |a This book demonstrates that the reliable and secure communication performance of maritime communications can be significantly improved by using intelligent reflecting surface (IRS) aided communication, privacy-aware Internet of Things (IoT) communications, intelligent resource management and location privacy protection. In the IRS aided maritime communication system, the reflecting elements of IRS can be intelligently controlled to change the phase of signal, and finally enhance the received signal strength of maritime ships (or sensors) or jam maritime eavesdroppers illustrated in this book. The power and spectrum resource in maritime communications can be jointly optimized to guarantee the quality of service (i.e., security and reliability requirements), and reinforcement leaning is adopted to smartly choose the resource allocation strategy. Moreover, learning based privacy-aware offloading and location privacy protection are proposed to intelligently guarantee the privacy-preserving requirements of maritime ships or (sensors). Therefore, these communication schemes based on reinforcement learning algorithms can help maritime communication systems to improve the information security, especially in dynamic and complex maritime environments. This timely book also provides broad coverage of the maritime wireless communication issues, such as reliability, security, resource management, and privacy protection. Reinforcement learning based methods are applied to solve these issues. This book includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students. Practitioners seeking solutions to maritime wireless communication and security related issues will benefit from this book as well. | ||
| 532 | 8 | |a Accessibility summary: This PDF does not fully comply with PDF/UA standards, but does feature limited screen reader support, described non-text content (images, graphs), bookmarks for easy navigation and searchable, selectable text. Users of assistive technologies may experience difficulty navigating or interpreting content in this document. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. | |
| 532 | 8 | |a No reading system accessibility options actively disabled | |
| 532 | 8 | |a Publisher contact for further accessibility information: accessibilitysupport@springernature.com | |
| 588 | |a Print version record. | ||
| 650 | 0 | |a Computer networks . | |
| 650 | 0 | |a Wireless communication systems. | |
| 650 | 0 | |a Mobile communication systems. | |
| 650 | 0 | |a Machine learning. | |
| 650 | 1 | 4 | |a Computer Communication Networks. |
| 650 | 2 | 4 | |a Wireless and Mobile Communication. |
| 650 | 2 | 4 | |a Machine Learning. |
| 700 | 1 | |a Yang, Helin. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Zhuang, Weihua. |e author. |0 (orcid)0000-0003-0488-511X |1 https://orcid.org/0000-0003-0488-511X |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 700 | 1 | |a Min, Minghui. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
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| 776 | 0 | 8 | |i Printed edition: |z 9783031321375 |
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| 830 | 0 | |a Wireless Networks, |x 2366-1445 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-32138-2 |
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