Edge Intelligence
Description
In the modern world, the application of edge computing in societal applications or IoT-enabled applications is becoming one of the crucial steps to IT infrastructure. The necessity to build ultra-low-latency decision-making solutions in various domains such as industry, agriculture, education, healthcare, and so forth is enhanced as an integral role for application developers.
Edge Intelligence: Deep Learning-Enabled Edge Computing is a book that targets researchers and practitioners who are interested in applying intelligence without compromising data privacy. The book reveals the existing edge-AI techniques and forecasts future edge-AI integration methods. The book delves into edge computing architectures after describing relevant basic technologies such as IoT, Cloud Computing, and other security-related architectures.
The core values of the book are multi-folded: it highlights the various operating systems that could be incorporated in tiny devices; it categorizes the application of deep learning and transfer learning techniques in edge computing nodes so that application developers or users could focus on applying apt edge intelligence techniques for their solutions; it expresses the utilization of edge-enabled computing devices to increase the performance of applications; it showcases the security features of edge-enabled IoT-based applications; and so forth.
The book starts with an explanation of all relevant basic technologies. It offers a smooth transition from the basics to insightful practical sessions for practitioners. The ideas of providing innovative ideas and applications in the later part of the book can enthuse researchers and developers to engage themselves in innovating newer products with the application of edge intelligence.
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Person
Shajulin Benedict graduated in 2001 from Manonmaniam Sunderanar University, India, with Distinction. In 2004, he received M.E Degree in Digital Communication and Computer Networking from A.K.C.E, Anna University, Chennai. He is the University second rank holder for his masters. He did his Ph.D degree in the area of Grid scheduling under Anna University, Chennai (Supervisor - Dr. V. Vasudevan, Director, Software Technologies Group of TIFAC Core in Network Engineering). He was affiliated towards the same group and published more papers in Int. Journals. He served as Professor at SXCCE Research Centre of Anna University-Chennai. Later, he visited TUM Germany for teaching Cloud Computing as Guest Professor of TUM Germany. Now, he works at the Indian Institute of Information Technology Kottayam, Kerala, India, an institute of national importance of India. He serves as Director/PI/Representative Officer of AIC-IIIT Kottayam (Sec.8 Company) for nourishing young entrepreneurs of India. He is also working as Guest Professor of TUM Germany, His research interests include HPC/Cloud/Grid scheduling, Performance Analysis of parallel applications (including exa-scale), Cloud Computing, IIoT, Blockchain, parallel compilers, and so forth. His webpage can be found at: sbenedictglobal.com
Content
Preface
Acknowledgements
Author biography
Part I Introduction
1 Edge intelligence
2 Edge computing architectures
3 Edge OS and programming models
Part II Learning techniques
4 Edge intelligence: learning techniques
5 Inference/prediction techniques
6 Edge resources and accelerators
7 Performance analysis of edge-enabled applications
8 Security in edge-AI systems
Part III Tools and solutions
9 Frameworks: edge-AI platforms
10 Orchestration platforms: computing continuum
Part IV Applications
11 Edge-AI applications
12 Business opportunities using edge-AI
13 Challenges and future directions
