Fog Computing. Группа авторов
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Part II: Fog Computing Techniques and Applications
12 Distributed Machine Learning for IoT Applications in the Fog
12.1 Introduction
12.2 Challenges in Data Processing for IoT
12.3 Computational Intelligence and Fog Computing
12.4 Challenges for Running Machine Learning on Fog Devices
12.5 Approaches to Distribute Intelligence on Fog Devices
12.6 Final Remarks
Acknowledgments
References
13 Fog Computing-Based Communication Systems for Modern Smart Grids
13.1 Introduction
13.2 An Overview of Communication Technologies in Smart Grid
13.3 Distribution Management System (DMS) Based on Fog/Cloud Computing
13.4 Real-time Simulation of the Proposed Feeder-based Communication Scheme Using MATLAB and ThingSpeak
13.5 Conclusion
References
14 An Estimation of Distribution Algorithm to Optimize the Utility of Task Scheduling Under Fog Computing Systems
14.1 Introduction
14.2 Estimation of Distribution Algorithm
14.3 Related Work
14.4 Problem Statement
14.5 Details of Proposed Algorithm
14.6 Simulation
14.7 Conclusion
References
15 Reliable and Power-Efficient Machine Learning in Wearable Sensors
15.1 Introduction
15.2 Preliminaries and Related Work
15.3 System Architecture and Methods
15.4 Data Collection and Experimental Procedures
15.5 Results
15.6 Discussion and Future Work
15.7 Summary
References
16 Insights into Software-Defined Networking and Applications in Fog Computing
16.1 Introduction
16.2 OpenFlow Protocol
16.3 SDN-Based Research Works
16.4 SDN in Fog Computing
16.5 SDN in Wireless Mesh Networks
16.6 SDN in Wireless Sensor Networks
16.7 Conclusion
References
17 Time-Critical Fog Computing for Vehicular Networks
17.1 Introduction
17.2 Applications and Timeliness Guarantees and Perturbations
17.3 Coping with Perturbation to Meet Timeliness Guarantees
17.4 Research Gaps and Future Research Directions
17.5 Conclusion
References
18 A Reliable and Efficient Fog-Based Architecture for Autonomous Vehicular Networks
18.1 Introduction
18.2