Machine Learning for Edge Computing: Frameworks, Patterns and Best Practices

This book explores the intersection of AI and edge computing,providing optimal solutions to key concerns through effective AI technologies. It discusses machine learning algorithms for edge computing and the future needs and potential of the technology. The target audience includes academics, research scholars, industrial experts, scientists, and postgraduate students working in IoT or edge computing.

Format: Hardback
Length: 190 pages
Publication date: 29 July 2022
Publisher: Taylor & Francis Ltd

This comprehensive book delves into the realm of edge intelligence, encompassing two distinct aspects: AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). Its primary objective is to offer optimal solutions to the critical challenges faced in edge computing by harnessing the power of effective AI technologies. The authors delve into the intricacies of building AI models, encompassing tasks such as model training and inference, on the edge. By providing a broader vision and perspective, this book sheds light on the emerging inter-disciplinary field of edge computing.

The authors explore various machine learning algorithms tailored for edge computing, discussing their future needs and potential applications. They also provide a comprehensive overview of the core concepts, frameworks, patterns, and research roadmap that form the foundation for potential future research programs in edge intelligence.

The target audience of this book spans academics, research scholars, industrial experts, scientists, and postgraduate students actively engaged in the fields of Internet of Things (IoT) or edge computing. These individuals seek to enhance their work by incorporating machine learning techniques, making this book an invaluable resource for their endeavors.

The book explores a wide range of topics, including:

Edge computing: A comprehensive introduction to the concept of distributed computing, where computing resources are distributed closer to the data sources, enabling faster and more efficient processing.

Hardware for edge computing: An exploration of the hardware components and architectures required to support edge computing, including sensors, processors, storage devices, and networking technologies.

AI for edge: A detailed examination of how AI can be integrated into edge computing systems to enhance their capabilities, including machine learning algorithms, deep learning applications, and edge virtualization techniques.

Edge intelligence and deep learning applications: A discussion of how edge intelligence and deep learning can be applied to various industries, such as healthcare, transportation, and manufacturing, to improve efficiency, accuracy, and decision-making.

Training and optimization: An exploration of the techniques and methodologies used to train and optimize AI models for edge computing, including transfer learning, reinforcement learning, and adversarial training.

Machine learning algorithms used for edge computing: A comprehensive review of the machine learning algorithms commonly employed in edge computing, including supervised learning, unsupervised learning, and reinforcement learning.

AI on IoT: A deep dive into the intersection of AI and IoT, exploring how AI can be leveraged to enhance the capabilities of IoT devices, enable intelligent decision-making, and facilitate seamless communication between devices.

Discusses future edge computing needs: A forward-looking analysis of the future trends and requirements in edge computing, including the increasing demand for low-latency, high-bandwidth connections, the rise of 5G networks, and the growing need for edge-based AI applications.

The authors of this book are accomplished professionals with extensive experience in their respective fields. Amitoj Singh holds the position of Associate Professor at the School of Sciences of Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Punjab, India. Vinay Kukreja serves as a Professor at the Chitkara Institute of Engineering and Technology, Chitkara University, Punjab, India. Taghi Javdani Gandomani holds the position of Assistant Professor at Shahrekord University, Shahrekord, Iran.

Their collective expertise and research contributions make this book an invaluable resource for anyone seeking to gain insights into the rapidly evolving field of edge intelligence and its applications.

Weight: 530g
Dimension: 234 x 156 (mm)
ISBN-13: 9780367694326