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Fog-Enabled Intelligent IoT Systems

  • Yang Yang
  • Xiliang Luo
  • Xiaoli Chu
  • Ming-Tuo Zhou
Book

Table of contents

  1. Front Matter
    Pages i-xviii
  2. Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou
    Pages 1-37
  3. Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou
    Pages 39-60
  4. Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou
    Pages 61-98
  5. Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou
    Pages 99-131
  6. Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou
    Pages 133-161
  7. Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou
    Pages 163-184
  8. Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou
    Pages 185-210
  9. Yang Yang, Xiliang Luo, Xiaoli Chu, Ming-Tuo Zhou
    Pages 211-212
  10. Back Matter
    Pages 213-217

About this book

Introduction

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 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

Keywords

IoT and Fog Computing Fog Computing Intelligent Systems Fog-enabled systems Fog-Enabled Intelligent IoT Systems Fog-enabled applications Data-driven self-learning applications

Authors and affiliations

  • Yang Yang
    • 1
  • Xiliang Luo
    • 2
  • Xiaoli Chu
    • 3
  • Ming-Tuo Zhou
    • 4
  1. 1.ShanghaiTech UniversityShanghai Institute of Fog Computing Technology (SHIFT), School of Information Science and TechnologyShanghaiChina
  2. 2.ShanghaiTech UniversityShanghai Institute of Fog Computing Technology (SHIFT), School of Information Science and TechnologyShanghaiChina
  3. 3.Department of Electronic & Electrical EngineeringUniversity of SheffieldSheffieldUK
  4. 4.Chinese Academy of SciencesShanghai Institute of Microsystem and Information TechnologyShanghaiChina

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-23185-9
  • Copyright Information Springer Nature Switzerland AG 2020
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-030-23184-2
  • Online ISBN 978-3-030-23185-9
  • Buy this book on publisher's site