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(Ebook) Fog-Enabled Intelligent IoT Systems by Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou ISBN 9783030231842, 9783030231859, 3030231844, 3030231852

  • SKU: EBN-10797852
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Authors:Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou
Pages:0 pages.
Year:2020
Editon:1st ed. 2020
Publisher:Springer International Publishing
Language:english
File Size:8.99 MB
Format:pdf
ISBNS:9783030231842, 9783030231859, 3030231844, 3030231852
Categories: Ebooks

Product desciption

(Ebook) Fog-Enabled Intelligent IoT Systems by Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou ISBN 9783030231842, 9783030231859, 3030231844, 3030231852

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, a fog-enabled service architecture is proposed to address the latency requirements, bandwidth limitations, and computing power issues in realistic cross-domain application scenarios with limited priori domain knowledge, i.e. physical laws, system statuses, operation principles and execution rules. Based on this fog-enabled architecture, a series of data-driven self-learning applications in different industrial sectors and public services are investigated and discussed, such as robot SLAM and formation control, wireless network self-optimization, intelligent transportation system, smart home and user behavior recognition. Finally, the advantages and future directions of fog-enabled intelligent IoT systems are summarized.

  • 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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