Optimizing Edge and Fog Computing Applications with AI and Metaheuristic Algorithms (Hardcover)

Madhusudhan H.S.

ISBN 10: 1041003544 / ISBN 13: 9781041003540

Edité par Taylor & Francis Ltd, London, 2025

A propos de cet article

Hardcover. 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 resourcesResource-aware models using ML and deep leaning models that can adapt their complexity based on available resources and balancing the load, allowing for better scalabilityImplementing secure ML algorithms and models to prevent adversarial attacks and ensure data privacySecuring the communication channels between edge devices, fog nodes, and the cloud to protect model updates and inferencesKubernetes container orchestration for fog computingFederated learning that enables model training across multiple edge devices without the need to share raw dataThe 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

N° de réf. du vendeur 9781041003540

Détails bibliographiques

• Titre: Optimizing Edge and Fog Computing Applications with AI and Metaheuristic Algorithms (Hardcover)
• Éditeur: Taylor & Francis Ltd, London
• Date de parution: 2025
• Langue: anglais
• ISBN à 10 chiffres: 1041003544
• ISBN à 13 chiffres: 9781041003540
• Reliure: Hardcover
• État de l'article: new

A propos de ce titre

Synopsis

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 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.

À propos de l?auteur

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.