This book provides an overview of recent innovations and achievements in the broad areas of cyber-physical systems (CPS), including architecture, networking, systems, applications, security, and privacy. The book discusses various new CPS technologies from diverse aspects to enable higher level of innovation towards intelligent life. The book provides insight to the future integration, coordination and interaction between the physical world, the information world, and human beings. The book features contributions from renowned researchers and engineers, who discuss key issues from various perspectives, presenting opinions and recent CPS-related achievements. Investigates how to advance the development of cyber-physical systems Provides a joint consideration of other newly emerged technologies and concepts in relation to CPS like cloud computing, big data, fog computing, and crowd sourcing Includes topics related to CPS such as architecture, system, networking, application, algorithm, security and privacy

The book presents the proceedings of the 2nd International Conference on 5G for Ubiquitous Connectivity (5GU 2018), which took place on December 4-5, 2018 in Nanjing, People's Republic of China. The aim of this conference is to bring together researchers and developers as well as regulators and policy makers to present their latest views on 5G, including new networking, new wireless communications, resource control & management, future access techniques, new emerging applications, and latest findings in key research activities on 5G. The book is applicable to researchers, academics, students, and professionals. Features practical, tested applications in 5G for ubiquitous connectivity; Includes discussion of 5G for ubiquitous connectivity in relation to wireless communications, resource control & management, and future access techniques; Applicable to researchers, academics, students, and professionals.

Discover this multi-disciplinary and insightful work, which integrates machine learning, edge computing, and big data. Presents the basics of training machine learning models, key challenges and issues, as well as comprehensive techniques including edge learning algorithms, and system design issues. Describes architectures, frameworks, and key technologies for learning performance, security, and privacy, as well as incentive issues in training/inference at the network edge. Intended to stimulate fruitful discussions, inspire further research ideas, and inform readers from both academia and industry backgrounds. Essential reading for experienced researchers and developers, or for those who are just entering the field.

This book aims at the tiny machine learning (TinyML) software and hardware synergy for edge intelligence applications. This book presents on-device learning techniques covering model-level neural network design, algorithm-level training optimization and hardware-level instruction acceleration. Analyzing the limitations of conventional in-cloud computing would reveal that on-device learning is a promising research direction to meet the requirements of edge intelligence applications. As to the cutting-edge research of TinyML, implementing a high-efficiency learning framework and enabling system-level acceleration is one of the most fundamental issues. This book presents a comprehensive discussion of the latest research progress and provides system-level insights on designing TinyML frameworks, including neural network design, training algorithm optimization and domain-specific hardware acceleration. It identifies the main challenges when deploying TinyML tasks in the real world and guides the researchers to deploy a reliable learning system. This book will be of interest to students and scholars in the field of edge intelligence, especially to those with sufficient professional Edge AI skills. It will also be an excellent guide for researchers to implement high-performance TinyML systems.

This book covers three major parts of Big Data: concepts, theories and applications. Written by world-renowned leaders in Big Data, this book explores the problems, possible solutions and directions for Big Data in research and practice. It also focuses on high level concepts such as definitions of Big Data from different angles; surveys in research and applications; and existing tools, mechanisms, and systems in practice. Each chapter is independent from the other chapters, allowing users to read any chapter directly. After examining the practical side of Big Data, this book presents theoretical perspectives. The theoretical research ranges from Big Data representation, modeling and topology to distribution and dimension reducing. Chapters also investigate the many disciplines that involve Big Data, such as statistics, data mining, machine learning, networking, algorithms, security and differential geometry. The last section of this book introduces Big Data applications from different communities, such as business, engineering and science. Big Data Concepts, Theories and Applications is designed as a reference for researchers and advanced level students in computer science, electrical engineering and mathematics. Practitioners who focus on information systems, big data, data mining, business analysis and other related fields will also find this material valuable.