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001 978-3-030-23185-9
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008 191016s2020 sz | s |||| 0|eng d
020 _a9783030231859
_9978-3-030-23185-9
024 7 _a10.1007/978-3-030-23185-9
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2thema
082 0 4 _a621.382
_223
100 1 _aYang, Yang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_934544
245 1 0 _aFog-Enabled Intelligent IoT Systems
_h[electronic resource] /
_cby Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou.
250 _a1st ed. 2020.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2020.
300 _aXVIII, 217 p. 72 illus., 58 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- IoT technologies and applications -- Fog computing architecture and technologies -- Challenges and solutions for cross-domain applications -- Fog-enabled intelligent transportation system -- Fog-enabled smart home and user behavior recognition -- Fog-enabled industrial 4.0 -- Fog-enabled wireless network self-optimization -- Conclusion.
520 _aThis book first provides a comprehensive review of state-of-the-art IoT technologies and applications in different industrial sectors and public services. The authors give in-depth analyses of fog computing architecture and key technologies that fulfill the challenging requirements of enabling computing services anywhere along the cloud-to-thing continuum. Further, in order to make IoT systems more intelligent and more efficient, two fog-enabled frameworks with detailed technical approaches are proposed for realistic application scenarios with no or limited priori domain knowledge, i.e. physical laws, system statuses, operation principles and execution rules. Based on these fog-enabled frameworks, a series of data-driven self-learning applications in different industrial sectors and public services are investigated and discussed, such as Intelligent Transportation System, Smart Home, Industrial 4.0, Wireless Network Self-Optimization, and User Behavior Recognition. Finally, the advantages and future directions of fog-enabled intelligent IoT systems are summarized in terms of service flexibility, scalability, quality, maintainability, cost efficiency, as well as latency. Provides a comprehensive review of state-of-the-art IoT technologies and applications in different industrial sectors and public services Presents a fog-enabled service architecture with detailed technical approaches for realistic cross-domain application scenarios with limited prior domain knowledge Outlines a series of data-driven self-learning applications (with new algorithms) in different industrial sectors and public services.
650 0 _aTelecommunication.
_910437
650 0 _aSignal processing.
_94052
650 0 _aApplication software.
_934545
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aComputer and Information Systems Applications.
_934546
700 1 _aLuo, Xiliang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_934547
700 1 _aChu, Xiaoli.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_934548
700 1 _aZhou, Ming-Tuo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_934549
710 2 _aSpringerLink (Online service)
_934550
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030231842
776 0 8 _iPrinted edition:
_z9783030231866
776 0 8 _iPrinted edition:
_z9783030231873
856 4 0 _uhttps://doi.org/10.1007/978-3-030-23185-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c75630
_d75630