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Record Nr. |
UNINA9910366587103321 |
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Autore |
Yang Yang |
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Titolo |
Fog-Enabled Intelligent IoT Systems / / by Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (230 pages) : illustrations |
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Disciplina |
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Soggetti |
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Electrical engineering |
Signal processing |
Image processing |
Speech processing systems |
Application software |
Communications Engineering, Networks |
Signal, Image and Speech Processing |
Information Systems Applications (incl. Internet) |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction -- 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. |
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Sommario/riassunto |
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This 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 |
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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. |
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