Optimizing Edge and Fog Computing Applications with AI and Metaheurist

Description

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... Read more

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.

Read less

1 Introduction to Resource Optimization in Fog and Edge Computing

PRATIK GUPTA AND PRATEEK KUMAR SONI

2 Artificial Intelligence Inspired Scheduling in Edge Computing

KAVITHA D. N., GAYANA J. KUMAR, AND VEDAVATHI N.

3 Supervised Machine Learning for Load Balancing in Fog Environments

PRIYANSHI MISHRA AND YASH JAIN

4 Blockchain- Based Secure Data Sharing System in Fog– Edge System

HARSHITHA KAMAL KANNAN

5 Securing IoT System Using ML Models

ARBAZ ADIB DALWAI, SHIVANI JASWAL, AND ROHIT VERMA

6 Federated Machine Learning Algorithm Aggregation Strategy for Collaborative Predictive Maintenance

BHAVNA SAINI, VIVEK KUMAR VERMA, BHARAT SINGH, AND NIDHI KUSHWAHA

7 Advance Machine Learning Algorithm Aggregation Strategy for Decentralized Collaborative Models

NAYANA SINGH AND PRATEEK KUMAR SONI

8 Artificial Intelligence and Machine Learning- Based Predictive Maintenance in Fog and Edge Computing Environment

SAHIL RAJ, PRADEEP KUMAR RAWAT, AND ANIRUDH NEGI

9 Deep Reinforcement Learning- Based Task Scheduling in Edge Computing

SHRISTI SONAM, PRATIK GUPTA, AND SAANCH SAPRA

10 Secure, Adaptable, and Collaborative AI: Federated Machine Learning Enhanced with Meta- Learning and Differential Privacy

DEEPTI BHAT, ANSH CHAUHAN, CHIRANTH NAGAPRASANNA, CHIRASHWI S., AND SWATHI B. H.

11 EP- MPCHS: Edge Server- Based Cloudlet Offloading Using Multi- Core and Parallel Heap Structures

RAJKUMAR NAGULSAMY, MANISHA GUPTA, AND PUNIT GUPTA

Editor(s)

Biography

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.

Optimizing Edge and Fog Computing Applications with AI and Metaheuristic Algorithms

Description

Table of Contents

Editor(s)

Biography

SOCIAL NETWORKS

Secure Shopping Payment Options