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Optimizing Edge And Fog Computing Applications With Ai And Metaheuristic Algorithms Madhusudhan H S

  • SKU: BELL-238857906
Optimizing Edge And Fog Computing Applications With Ai And Metaheuristic Algorithms Madhusudhan H S
$ 35.00 $ 45.00 (-22%)

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Optimizing Edge And Fog Computing Applications With Ai And Metaheuristic Algorithms Madhusudhan H S instant download after payment.

Publisher: Taylor & Francis Group
File Extension: PDF
File size: 7.08 MB
Pages: 380
Author: Madhusudhan H S, Punit Gupta, Dinesh Kumar Saini
ISBN: 978104100354, 978104100500, 9781003610168, 1041003544, 1041005008, 1003610161
Language: English
Year: 2025

Product desciption

Optimizing Edge And Fog Computing Applications With Ai And Metaheuristic Algorithms Madhusudhan H S by Madhusudhan H S, Punit Gupta, Dinesh Kumar Saini 978104100354, 978104100500, 9781003610168, 1041003544, 1041005008, 1003610161 instant download after payment.

data analytics, Kubernetes, IoT, app optimization, predictive computing
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 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 da

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