000			03598nam a22003975i 4500
999			_c398518 _d398518 _x1
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003			ES-MaUEC
005			20240314174535.0
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007			cr nn 008mamaa
008			230725s2023 sz | o |||| 0|eng d
020			_a9783031321382
024	7		_a10.1007/978-3-031-32138-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aVM325 _b2023 EB
100	1		_aXiao, Liang, _eautor _9670373 _d1980-
245	1	0	_aReinforcement Learning for Maritime Communications _cby Liang Xiao, Helin Yang, Weihua Zhuang, Minghui Min
250			_a1st ed 2023
264		1	_aCham _bSpringer International Publishing _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aWireless Networks _x2366-1445
505	0		_aIntroduction -- 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			_aThis 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.
988			_aSpringer_Computer_2023
650		7	_2embne _9138948 _aBarcos _xEquipamiento eléctrico
830		0	_aWireless Networks, _x2366-1445
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-32138-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2024 _dz _eu _zSI