Optimizing Edge and Fog Computing Applications with AI and Metaheuristic Algorithms [Hardcover]

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.?

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

Fog and edge computing are two paradigms that have emerged to address the challenges associated with processing and managing data in the era of the Internet of Things (IoT). Both models involve moving computation and data storage closer to the source of data generation, but they have subtle differences in their architectures and scopes. These differences are one of the subjects covered in Optimizing Edge and Fog Computing Applications with AI and Metaheuristic Algorithms. Other subjects covered in the book include:

• Designing machine learning (ML) algorithms that are aware of the resource constraints at the edge and fog layers ensures efficient use of computational resourclSx