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Fog Computing

Author Assad Abbas , Samee U. Khan , Albert Y. Zomaya
Release Date: 2020/04/01
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Book Description

Summarizes the current state and upcoming trends within the area of fog computing

Written by some of the leading experts in the field, Fog Computing: Theory and Practice focuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth.

Presented in two parts—Fog Computing Systems and Architectures, and Fog Computing Techniques and Application—this book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricing estimation, and virtualization in the fog environments.

  • Includes chapters on deep learning, mobile edge computing, smart grid, and intelligent transportation systems beyond the theoretical and foundational concepts
  • Explores real-time traffic surveillance from video streams and interoperability of fog computing architectures
  • Presents the latest research on data quality in the IoT, privacy, security, and trust issues in fog computing

Fog Computing: Theory and Practice provides a platform for researchers, practitioners, and graduate students from computer science, computer engineering, and various other disciplines to gain a deep understanding of fog computing.

Table of Contents

  1. Cover
  2. List of Contributors
  3. Acronyms
  4. Part I: Fog Computing Systems and Architectures
    1. 1 Mobile Fog Computing
      1. 1.1 Introduction
      2. 1.2 Mobile Fog Computing and Related Models
      3. 1.3 The Needs of Mobile Fog Computing
      4. 1.4 Communication Technologies
      5. 1.5 Nonfunctional Requirements
      6. 1.6 Open Challenges
      7. 1.7 Conclusion
      8. Acknowledgment
      9. References
    2. 2 Edge and Fog: A Survey, Use Cases, and Future Challenges
      1. 2.1 Introduction
      2. 2.2 Edge Computing
      3. 2.3 Fog Computing
      4. 2.4 Fog and Edge Illustrative Use Cases
      5. 2.5 Future Challenges
      6. 2.6 Conclusion
      7. Acknowledgment
      8. References
    3. 3 Deep Learning in the Era of Edge Computing: Challenges and Opportunities
      1. 3.1 Introduction
      2. 3.2 Challenges and Opportunities
      3. 3.3 Concluding Remarks
      4. References
    4. 4 Caching, Security, and Mobility in Content-centric Networking
      1. 4.1 Introduction
      2. 4.2 Caching and Fog Computing
      3. 4.3 Mobility Management in CCN
      4. 4.4 Security in Content-centric Networks
      5. 4.5 Caching
      6. 4.6 Conclusions
      7. References
    5. 5 Security and Privacy Issues in Fog Computing
      1. 5.1 Introduction
      2. 5.2 Trust in IoT
      3. 5.3 Authentication
      4. 5.4 Authorization
      5. 5.5 Privacy
      6. 5.6 Web Semantics and Trust Management for Fog Computing
      7. 5.7 Discussion
      8. 5.8 Conclusion
      9. References
    6. 6 How Fog Computing Can Support Latency/Reliability-sensitive IoT Applications: An Overview and a Taxonomy of State-of-the-art Solutions
      1. 6.1 Introduction
      2. 6.2 Fog Computing for IoT: Definition and Requirements
      3. 6.3 Fog Computing: Architectural Model
      4. 6.4 Fog Computing for IoT: A Taxonomy
      5. 6.5 Comparisons of Surveyed Solutions
      6. 6.6 Challenges and Recommended Research Directions
      7. 6.7 Concluding Remarks
      8. References
    7. 7 Harnessing the Computing Continuum for Programming Our World
      1. 7.1 Introduction and Overview
      2. 7.2 Research Philosophy
      3. 7.3 A Goal-oriented Approach to Programming the Computing Continuum
      4. 7.4 Summary
      5. References
    8. 8 Fog Computing for Energy Harvesting-enabled Internet of Things
      1. 8.1 Introduction
      2. 8.2 System Model
      3. 8.3 Tradeoffs in EH Fog Systems
      4. 8.4 Future Research Challenges
      5. Acknowledgment
      6. References
    9. 9 Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control
      1. 9.1 Introduction
      2. 9.2 Background
      3. 9.3 Related Topics
      4. 9.4 Design Challenges
      5. 9.5 IoT System Architecture
      6. 9.6 Fog-assisted Runtime Energy Management in Wearable Sensors
      7. 9.7 Conclusions
      8. Acknowledgment
      9. References
    10. 10 Latency Minimization Through Optimal Data Placement in Fog Networks
      1. 10.1 Introduction
      2. 10.2 Related Work
      3. 10.3 Problem Statement
      4. 10.4 Delay Minimization Without Replication
      5. 10.5 Delay Minimization with Replication
      6. 10.6 Performance Evaluation
      7. 10.7 Conclusion
      8. Acknowledgement
      9. References
    11. 11 Modeling and Simulation of Distributed Fog Environment Using FogNetSim++
      1. 11.1 Introduction
      2. 11.2 Modeling and Simulation
      3. 11.3 FogNetSim++: Architecture
      4. 11.4 FogNetSim++: Installation and Environment Setup
      5. 11.5 Conclusion
      6. References
  5. Part II: Fog Computing Techniques and Applications
    1. 12 Distributed Machine Learning for IoT Applications in the Fog
      1. 12.1 Introduction
      2. 12.2 Challenges in Data Processing for IoT
      3. 12.3 Computational Intelligence and Fog Computing
      4. 12.4 Challenges for Running Machine Learning on Fog Devices
      5. 12.5 Approaches to Distribute Intelligence on Fog Devices
      6. 12.6 Final Remarks
      7. Acknowledgments
      8. References
    2. 13 Fog Computing-Based Communication Systems for Modern Smart Grids
      1. 13.1 Introduction
      2. 13.2 An Overview of Communication Technologies in Smart Grid
      3. 13.3 Distribution Management System (DMS) Based on Fog/Cloud Computing
      4. 13.4 Real-time Simulation of the Proposed Feeder-based Communication Scheme Using MATLAB and ThingSpeak
      5. 13.5 Conclusion
      6. References
    3. 14 An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems
      1. 14.1 Introduction
      2. 14.2 Estimation of Distribution Algorithm
      3. 14.3 Related Work
      4. 14.4 Problem Statement
      5. 14.5 Details of Proposed Algorithm
      6. 14.6 Simulation
      7. 14.7 Conclusion
      8. References
    4. 15 Reliable and Power-Efficient Machine Learning in Wearable Sensors
      1. 15.1 Introduction
      2. 15.2 Preliminaries and Related Work
      3. 15.3 System Architecture and Methods
      4. 15.4 Data Collection and Experimental Procedures
      5. 15.5 Results
      6. 15.6 Discussion and Future Work
      7. 15.7 Summary
      8. References
    5. 16 Insights into Software-Defined Networking and Applications in Fog Computing
      1. 16.1 Introduction
      2. 16.2 OpenFlow Protocol
      3. 16.3 SDN-Based Research Works
      4. 16.4 SDN in Fog Computing
      5. 16.5 SDN in Wireless Mesh Networks
      6. 16.6 SDN in Wireless Sensor Networks
      7. 16.7 Conclusion
      8. References
    6. 17 Time-Critical Fog Computing for Vehicular Networks
      1. 17.1 Introduction
      2. 17.2 Applications and Timeliness Guarantees and Perturbations
      3. 17.3 Coping with Perturbation to Meet Timeliness Guarantees
      4. 17.4 Research Gaps and Future Research Directions
      5. 17.5 Conclusion
      6. References
    7. 18 A Reliable and Efficient Fog-Based Architecture for Autonomous Vehicular Networks
      1. 18.1 Introduction
      2. 18.2 Proposed Methodology
      3. 18.3 Hypothesis Formulation
      4. 18.4 Simulation Design
      5. 18.5 Conclusions
      6. References
    8. 19 Fog Computing to Enable Geospatial Video Analytics for Disaster-incident Situational Awareness
      1. 19.1 Introduction
      2. 19.2 Computer Vision Application Case Studies and FCC Motivation
      3. 19.3 Geospatial Video Analytics Data Collection Using Edge Routing
      4. 19.4 Fog/Cloud Data Processing for Geospatial Video Analytics Consumption
      5. 19.5 Concluding Remarks
      6. References
    9. 20 An Insight into 5G Networks with Fog Computing
      1. 20.1 Introduction
      2. 20.2 Vision of 5G
      3. 20.3 Fog Computing with 5G Networks
      4. 20.4 Architecture of 5G
      5. 20.5 Technology and Methodology for 5G
      6. 20.6 Applications
      7. 20.7 Challenges
      8. 20.8 Conclusion
      9. References
    10. 21 Fog Computing for Bioinformatics Applications
      1. 21.1 Introduction
      2. 21.2 Cloud Computing
      3. 21.3 Cloud Computing Applications in Bioinformatics
      4. 21.4 Fog Computing
      5. 21.5 Fog Computing for Bioinformatics Applications
      6. 21.6 Conclusion
      7. References
  6. Index
  7. End User License Agreement
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