This book conducts a comprehensive and detailed survey of the recent research efforts in edge intelligence. The authors first review the background and present motivation for AI running at the network edge. The book then provides an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning models toward training/inference at the network edge. To illustrate the research problems for edge intelligence, the book also showcases four of the authors' own research projects on edge intelligence, ranging from rigorous theoretical analysis to studies based on realistic implementation. This second edition incorporates the latest research in this rapidly developing area. The authors also highlight the current applications and future research opportunities for edge intelligence.

In addition, this book: 

• Explains the need to push artificial intelligence to the network edge to reach the potential of IoT big data
• Presents key Internet of Things applications, including AR/VR, smart city, and video/audio surveillance
• Highlights open problems in order to stimulate fruitful discussions and open new directions in edge intelligence

About the Authors

Sen Lin, Ph.D., is an Assistant Professor in the Department of Computer Science at the University of Houston. 

Zhi Zhou, Ph.D., is an Associate Professor in the School of Computer Science and Engineering at Sun Yat-sen University. 

Zhaofeng Zhang, Ph.D., isa  Postdoctoral Researcher at School of Computing and Augmented Intelligence at Arizona State University. 

Xu Chen, Ph.D., is a Full Professor and Assistant Dean at the School of Computer Science and Engineering at Sun Yat-sen University. 

Junshan Zhang, Ph.D. is a Professor in the Electrical and Computer Engineering Department at the University of California, Davis.