LEADER 04240nam 22006135 450 001 9910736016503321 005 20251008153428.0 010 $a3-031-32138-3 024 7 $a10.1007/978-3-031-32138-2 035 $a(MiAaPQ)EBC30667424 035 $a(Au-PeEL)EBL30667424 035 $a(DE-He213)978-3-031-32138-2 035 $a(PPN)272253138 035 $a(CKB)27865545400041 035 $a(EXLCZ)9927865545400041 100 $a20230725d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReinforcement Learning for Maritime Communications /$fby Liang Xiao, Helin Yang, Weihua Zhuang, Minghui Min 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (ix, 146 pages) $cillustrations (chiefly color) 225 1 $aWireless Networks,$x2366-1445 311 08$aPrint version: Xiao, Liang Reinforcement Learning for Maritime Communications Cham : Springer International Publishing AG,c2023 9783031321375 320 $aIncludes bibliographical references. 327 $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. 330 $aThis book demonstrates that the reliable and secure communication performance of maritime communications can be significantly improved by using intelligent reflecting surface (IRS) aided communication, privacy-aware Internet of Things (IoT) communications, intelligent resource management and location privacy protection. In the IRS aided maritime communication system, the reflecting elements of IRS can be intelligently controlled to change the phase of signal, and finally enhance the received signal strength of maritime ships (or sensors) or jam maritime eavesdroppers illustrated in this book. The power and spectrum resource in maritime communications can be jointly optimized to guarantee the quality of service (i.e., security and reliability requirements), and reinforcement leaning is adopted to smartly choose the resource allocation strategy. Moreover, learning based privacy-aware offloading and location privacy protection are proposed to intelligently guarantee the privacy-preserving requirements of maritime ships or (sensors). Therefore, these communication schemes based on reinforcement learning algorithms can help maritime communication systems to improve the information security, especially in dynamic and complex maritime environments. This timely book also provides broad coverage of the maritime wireless communication issues, such as reliability, security, resource management, and privacy protection. Reinforcement learning based methods are applied to solve these issues. This book includes four rigorously refereed chapters from prominent international researchers working in this subject area. The material serves as a useful reference for researchers, graduate students. Practitioners seeking solutions to maritime wireless communication and security related issues will benefit from this book as well. 410 0$aWireless Networks,$x2366-1445 606 $aComputer networks 606 $aWireless communication systems 606 $aMobile communication systems 606 $aMachine learning 606 $aComputer Communication Networks 606 $aWireless and Mobile Communication 606 $aMachine Learning 615 0$aComputer networks. 615 0$aWireless communication systems. 615 0$aMobile communication systems. 615 0$aMachine learning. 615 14$aComputer Communication Networks. 615 24$aWireless and Mobile Communication. 615 24$aMachine Learning. 676 $a297.05 700 $aXiao$b Liang$f1980-$01423771 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910736016503321 996 $aReinforcement learning for maritime communications$93552203 997 $aUNINA