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Full Description
Edge Computing and Intelligence is a new technical discipline that researches and
develops theories, methods, technologies, and application systems for providing the fast
and efficient processing and intelligent decision using the big data generated from IoT
system. Edge Computing and Intelligence enabled Internet of Thing (IoT) system helps
in alleviating the resource limitation of the IoT devices and facilities large scale
deployment of IoT system for different applications such smart city, smart grid, smart
home, smart healthcare system, smart video surveillance, e-healthcare etc. Edge
Computing based IoT architecture facilitates computational and storage resources at
the edge of the IoT system and helps in reducing the latency for end users. To facilitate
an efficient processing and analysis of large amount of Big IoT data, AI techniques can
be used at edge of the IoT system to enhance the response time.
Due to deployment of the IoT devices in open environment and its interaction with
other IoT devices through unstable and unreliable wireless channel, and placement of
Edge nodes near to the IoT system, IoT devices will offer many chances for malicious
attackers to compromise them through collusion attacks, and DoS attacks, and many
other types of attacks. Thus, Analysis of the network traffic and IoT data at the Edge
nodes with help of various latest AI techniques will address various issues such as
anomaly detection in IoT data, data poisoning attacks, and other security and privacy
issues of the IoT system. In this book, we will provide an overview of the fundamental
concepts of Edge Computing, Edge intelligence using advance machine and deep
learning models, architecture of Edge computing based IoT system, and edge
computing based cyber threat intelligence solutions for IoT systems.
The main aim of the book is to systematically collect and present quality research
works in recent trends in Edge Computing and Edge Intelligence for IoT system. This
book would emphasize and facilitate a greater understanding of various concepts such
as Edge Computing and Edge Intelligence, Edge Intelligence enabled security and
privacy approaches, application of the advance AI techniques such as machine/deep
learning, federated learning, for IoT data analytics and solving various issues of the IoT
system. There are numerous challenges in the development of Edge computing,
Artificial Intelligence (AI) and IoT-based Engineering Applications from the Security
and Privacy perspectives. This book will help in identifying areas where we are facing
many challenges and applying Edge Computing, advanced machine and deep learning
techniques for designing and development of Edge Computing based IoT system.
Contents
Chapter 1. Edge Intelligence for IoT: Architecture, Issues, and Applications
Chapter 2. Edge Intelligence based Internet of Medical Things: Architecture, Issues, and
Case Studies
Chapter 3. Edge Intelligence based Intrusion Detection and Avoidance Framework for
IoT Network
Chapter 4. Edge Intelligence based Anomaly Detection for IoT Network
Chapter 5. Edge Intelligence based Intelligent Transportation System
Chapter 6. Edge Intelligence and IoT-based Smart Agriculture System
Chapter 7. Edge Intelligence and Privacy Preservation Aware Intelligent Cyber Threats
Detection Model
Chapter 8. Edge Intelligence and Deep Learning Approaches based data analytics for
Fog-enabled IoT Network
Chapter 9. Edge Intelligence and Federated Learning based Healthcare Data Analytics
Chapter 10. Edge Intelligence and Deep Reinforcement Learning based Big Data Analytics
for IoT System
Chapter 11. Edge Intelligence and Privacy-aware Threat Intelligence for Fog-enabled IoT
Network using big data approaches.
Chapter 12. Edge Intelligence and Blockchain-based security architecture for Fog-enabled
IoT Systems
Chapter 13. Blockchain and Edge Intelligence based forensic evidence preservation in
Fog-enabled IoT Systems
Chapter 14. Edge Intelligence based Botnet detection in Fog-enabled IoT environments.
Chapter 15. Edge Intelligence based Data Analytics for Vehicular Communication
Networks
