Optimizing Edge and Fog Computing Applications with AI and Metaheuristic Algorithms

• Designing machine learning (ML) algorithms that are aware of the resource constraints at the edge and fog layers ensures efficient use of computational resources
• Resource-aware models using ML and deep leaning models that can adapt their complexity based on available resources and balancing the load, allowing for better scalability
• Implementing secure ML algorithms and models to prevent adversarial attacks and ensure data privacy
• Securing the communication channels between edge devices, fog nodes, and the cloud to protect model updates and inferences
• Kubernetes container orchestration for fog computing
• Federated learning that enables model training across multiple edge devices without the need to share raw data

The book discusses how resource optimization in fog and edge computing is crucial for achieving efficient and effective processing of data close to the source. It explains how both fog and edge computing aim to enhance system performance, reduce latency, and improve overall resource utilization. It examines the combination of intelligent algorithms, effective communication protocols, and dynamic management strategies required to adapt to changing conditions and workload demands. The book explains how security in fog and edge computing requires a combination of technological measures, advanced techniques, user awareness, and organizational policies to effectively protect data and systems from evolving security threats. Finally, it looks forward with coverage of ongoing research and development, which are essential for refining optimization techniques and ensuring the scalability and sustainability of fog and edge computing environments.