LEADER 03976nam  22006015  450 
001 996465460503316
005 20231116215153.0
010   $a981-15-4412-3
024 7 $a10.1007/978-981-15-4412-5
035   $a(CKB)4100000011034847
035   $a(DE-He213)978-981-15-4412-5
035   $a(MiAaPQ)EBC6175315
035   $a(PPN)243759150
035   $a(EXLCZ)994100000011034847
100   $a20200413d2020      u|             0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMission-Critical Application Driven Intelligent Maritime Networks$b[electronic resource] /$fby Tingting Yang, Xuemin (Sherman) Shen
205   $a1st ed. 2020.
210  1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2020.
215   $a1 online resource (VIII, 78 p. 36 illus., 34 illus. in color.)
225 1 $aSpringerBriefs in Computer Science,$x2191-5768
311   $a981-15-4411-5 
327   $aChapter 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. .
330   $aThis book shares valuable insights into high-efficiency data transmission scheduling and into a group intelligent search and rescue approach for artificial intelligence (AI)-powered maritime networks. Its goal is to highlight major research directions and topics that are critical for those who are interested in maritime communication networks, equipping them to carry out further research in this field. The authors begin with a historical overview and address the marine business, emerging technologies, and the shortcomings of current network architectures (coverage, connectivity, reliability, etc.). In turn, they introduce a heterogeneous space/air/sea/ground maritime communication network architecture and investigate the transmission scheduling problem in maritime communication networks, together with solutions based on deep reinforcement learning. To accommodate the computation demands of maritime communication services, the authors propose a multi-vessel offloading algorithm for maritime mobile edge computing networks. In closing, they discuss the applications of swarm intelligence in maritime search and rescue.
410  0$aSpringerBriefs in Computer Science,$x2191-5768
606   $aWireless communication systems
606   $aMobile communication systems
606   $aComputer communication systems
606   $aElectrical engineering
606   $aWireless and Mobile Communication$3https://scigraph.springernature.com/ontologies/product-market-codes/T24100
606   $aComputer Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13022
606   $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035
615  0$aWireless communication systems.
615  0$aMobile communication systems.
615  0$aComputer communication systems.
615  0$aElectrical engineering.
615 14$aWireless and Mobile Communication.
615 24$aComputer Communication Networks.
615 24$aCommunications Engineering, Networks.
676   $a621.384
700   $aYang$b Tingting$4aut$4http://id.loc.gov/vocabulary/relators/aut$0977499
702   $aShen$b X$g(Xuemin),$f1958-$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a996465460503316
996   $aMission-Critical Application Driven Intelligent Maritime Networks$92227017
997   $aUNISA