The book covers resource management techniques to enhance resource optimization, security mechanisms and predictive computing in fog and edge computing. Machine learning (ML) can leverage the distributed nature of these fog and edge architectures to perform computation and analysis closer to the data source.
The book covers resource management techniques to enhance resource optimization, security mechanisms and predictive computing in fog and edge computing. Machine learning (ML) can leverage the distributed nature of these fog and edge architectures to perform computation and analysis closer to the data source.
Madhusudhan H S is an associate professor in the Department of Computer Science and Engineering at Vidyavardhaka College of Engineering, Mysuru, India. Punit Gupta is an associate professor in the Department of Computer and Communication Engineering at Pandit Deendayal Energy University, Gujarat, India. Dinesh Kumar Saini is a full professor at the School of Computing and Information Technology, Manipal University, Jaipur, India.
Inhaltsangabe
1. Introduction to Resource Optimization in Fog and Edge Computing 2. Artificial Intelligence Inspired Scheduling in Edge Computing 3. Supervised Machine Learning for Load Balancing in Fog Environments 4. Blockchain-Based Secure Data Sharing System in Fog-Edge System 5. Securing IoT System Using ML Models 6. Federated Machine Learning Algorithm Aggregation Strategy for Collaborative Predictive Maintenance 7. Advance Machine Learning Algorithm Aggregation Strategy for Decentralized Collaborative Models 8. Artificial Intelligence and Machine Learning-Based Predictive Maintenance in Fog and Edge Computing Environment 9. Deep Reinforcement Learning-Based Task Scheduling in Edge Computing 10. Secure, Adaptable, and Collaborative AI: Federated Machine Learning Enhanced with Meta-Learning and Differential Privacy 11. EP-MPCHS: Edge Server-Based Cloudlet Offloading Using Multi-Core and Parallel Heap Structures
1. Introduction to Resource Optimization in Fog and Edge Computing 2. Artificial Intelligence Inspired Scheduling in Edge Computing 3. Supervised Machine Learning for Load Balancing in Fog Environments 4. Blockchain-Based Secure Data Sharing System in Fog-Edge System 5. Securing IoT System Using ML Models 6. Federated Machine Learning Algorithm Aggregation Strategy for Collaborative Predictive Maintenance 7. Advance Machine Learning Algorithm Aggregation Strategy for Decentralized Collaborative Models 8. Artificial Intelligence and Machine Learning-Based Predictive Maintenance in Fog and Edge Computing Environment 9. Deep Reinforcement Learning-Based Task Scheduling in Edge Computing 10. Secure, Adaptable, and Collaborative AI: Federated Machine Learning Enhanced with Meta-Learning and Differential Privacy 11. EP-MPCHS: Edge Server-Based Cloudlet Offloading Using Multi-Core and Parallel Heap Structures
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