Xu Chen

Dusit Niyato

Victor C. M. Leung

Xiaofei Wang

Yiwen Han

Xueqiang Yan

As an important enabler for changing people’s lives, advances in artificial intelligence (AI)-based applications and services are on the rise, despite being hindered by efficiency and latency issues. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. To do so, it introduces and discusses: 1) edge intelligence and intelligent edge; and 2) their implementation methods and enabling technologies, namely AI training and inference in the customized edge computing framework. Gathering essential information previously scattered across the communication, networking, and AI areas, the book can help readers to understand the connections between key enabling technologies, e.g. a) AI applications in edge; b) AI inference in edge; c) AI training for edge; d) edge computing for AI; and e) using AI to optimize edge. After identifying these five aspects, which are essential for the fusion of edge computing and AI, it discusses current challenges and outlines future trends in achieving more pervasive and fine-grained intelligence with the aid of edge computing.

Convergence of Edge Computing and Artificial Intelligence

Edge AI

Husheng Li

Lingjie Duan

Anibal Sanjab

Donatello Materassi

Rachid Elazouzi

This book constitutes the refereed proceedings of the 7th EAI International Conference on Game Theory for Networks, GameNets 2017, held in Knoxville, Tennessee, USA, in May 2017. The 10 conference papers and 5 invited papers presented cover topics such as smart electric grid, Internet of Things (IoT), social networks, networks security, mobile service markets, and epidemic control.

7th International EAI Conference, GameNets 2017 Knoxville, TN, USA, May 9, 2017, Proceedings

Game Theory for Networks

Junshan Zhang

Sen Lin

Zhi Zhou

Zhaofeng Zhang

With the explosive growth of mobile computing and Internet of Things (IoT) applications, as exemplified by AR/VR, smart city, and video/audio surveillance, billions of mobile and IoT devices are being connected to the Internet, generating zillions of bytes of data at the network edge. Driven by this trend, there is an urgent need to push the frontiers of artificial intelligence (AI) to the network edge to fully unleash the potential of IoT big data. Indeed, the marriage of edge computing and AI has resulted in innovative solutions, namely edge intelligence or edge AI. Nevertheless, research and practice on this emerging inter-disciplinary field is still in its infancy stage. To facilitate the dissemination of the recent advances in edge intelligence in both academia and industry, this book conducts a comprehensive and detailed survey of the recent research efforts and also showcases the authors' own research progress on edge intelligence. Specifically, the book first reviews the background and present motivation for AI running at the network edge. Next, it 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. Finally, it discusses the applications, marketplace, and future research opportunities of edge intelligence. This emerging interdisciplinary field offers many open problems and yet also tremendous opportunities, and this book only touches the tip of iceberg. Hopefully, this book will elicit escalating attention, stimulate fruitful discussions, and open new directions on edge intelligence.

Optimization, Deep Learning, and Applications

Edge Intelligence in the Making

Yang Yang

Rui Tan

Yong Xiao

INTELLIGENT IOT FOR THE DIGITAL WORLD DISCOVER HOW THE INTELLIGENT INTERNET OF THINGS WILL CHANGE THE INFORMATION AND COMMUNICATION TECHNOLOGY INDUSTRY IN THE NEXT DECADE In the digital world, most data and Internet of Things (IoT) services need to be efficiently processed and executed by intelligent algorithms using local or regional computing resources, thus greatly saving and reducing communication bandwidth, end-to-end service delay, long-distance data transmissions, and potential privacy breaches. This book proposes a pyramid model, where data, computing and algorithm jointly constitute the triangular base to support a variety of user-centric intelligent IoT services at the spire by using different kinds of smart terminals or devices. This book provides a state-of-the-art review of intelligent IoT technologies and applications, discusses the key challenges and opportunities facing the digital world, and answers the following five critical questions: What is the most feasible network architecture to effectively provide sufficient resources anywhere anytime for intelligent IoT application scenarios? How do we efficiently discover, allocate and manage computing, communication and caching resources in heterogeneous networks across multiple domains and operators? How do we agilely achieve adaptive service orchestration and reliable service provisioning to meet dynamic user requirements in real time? How do we effectively protect data privacy in IoT applications, where IoT devices and edge/fog computing nodes only have limited resources and capabilities? How do we continuously guarantee and maintain the synchronization and reliability of wide-area IoT systems and applications? Written for professionals working in 5G/IoT technology development, service management and big data analytics, this book offers an overview of intelligent IoT service architecture, key technologies, important applications and future technological trends.

Incorporating 5G Communications and Fog/Edge Computing Technologies

Intelligent IoT for the Digital World

Yongfeng Zhang

In recent years, a large number of explainable recommendation approaches have been proposed and applied in real-world systems. This survey provides a comprehensive review of the explainable recommendation research.

A Survey and New Perspectives

Explainable Recommendation

Xu Chen

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