This book explores fundamental and advanced concepts related to the AI-enabled Edge Technology paradigm, also known as Edge Intelligence, within the framework of the Internet of Things (IoT). Expanding the application of Edge computing is increasingly necessary. This can involve exploring automated, intelligent computational learning theorems, and ANN-oriented, trustworthy machine learning perspectives to enhance computational intelligence. The book functions as a valuable resource for professionals in the sector and also acts as a comprehensive learning tool for newcomers in the field of AI-enabled Edge Technologies and their applications, covering both fundamental and advanced concepts. This book uses data and network engineering and intelligent decision support system-by-design principles to design a reliable IoT edge-cloud ecosystem and to implement cyber-physical pervasive infrastructure solutions. The book will help readers understand the design architecture and AI algorithms and learn analytics through IoT edge, device-edge and the state-of-the-art in cloud-IoT countermeasures. The book is a valuable reference for anyone doing undergraduate or postgraduate studies, conducting research, or working in the computer science, information technology, electronics engineering, and complicated mathematical modeling domains.

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.

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.

This book presents the technologies that empower edge intelligence, along with their use in novel IoT solutions. Specifically, it presents how 5G/6G, Edge AI, and Blockchain solutions enable novel IoT-based decentralized intelligence use cases at the edge of the cloud/edge/IoT continuum. Emphasis is placed on presenting how these technologies support a wide array of functional and non-functional requirements spanning latency, performance, cybersecurity, data protection, real-time performance, energy efficiency, and more. The various chapters of the book are contributed by several EU-funded projects, which have recently developed novel IoT platforms that enable the development and deployment of edge intelligence applications based on the cloud/edge paradigm. Each one of the projects employs its own approach and uses a different mix of networking, middleware, and IoT technologies. Therefore, each of the chapters of the book contributes a unique perspective on the capabilities of enabling technologies and their integration in practical real-life applications in different sectors. The book is structured in five distinct parts. Each one of the first four parts focuses on a specific set of enabling technologies for edge intelligence and smart IoT applications in the cloud/edge/IoT continuum. Furthermore, the fifth part provides information about complementary aspects of next-generation IoT technology, including information about business models and IoT skills. Specifically: The first part focuses on 5G/6G networking technologies and their roles in implementing edge intelligence applications. The second part presents IoT applications that employ machine learning and other forms of Artificial Intelligence at the edge of the network. The third part illustrates decentralized IoT applications based on distributed ledger technologies. The fourth part is devoted to the presentation of novel IoT applications and use cases spanning the cloud/edge/IoT continuum. The fifth part discusses complementary aspects of IoT technologies, including business models and digital skills.

This book discusses the future possibilities of AI with cloud computing and edge computing. The main goal of this book is to conduct analyses, implementation and discussion of many tools (of artificial intelligence, machine learning and deep learning and cloud computing, fog computing, and edge computing including concepts of cyber security) for understanding integration of these technologies. With this book, readers can quickly get an overview of these emerging topics and get many ideas of the future of AI with cloud, edge, and in many other areas. Topics include machine and deep learning techniques for Internet of Things based cloud systems; security, privacy and trust issues in AI based cloud and IoT based cloud systems; AI for smart data storage in cloud-based IoT; blockchain based solutions for AI based cloud and IoT based cloud systems.This book is relevent to researchers, academics, students, and professionals.

Edge computing is focused on devices and technologies that are attached to the internet of things (IoT). Identifying IoT use across a range of industries and measuring strategic values helps identify what technologies to pursue and can avoid wasted resources on deployments with limited values. Edge Computing and Computational Intelligence Paradigms for the IoT is a critical research book that provides a complete insight on the recent advancements and integration of intelligence in IoT. This book highlights various topics such as disaster prediction, governance, and healthcare. It is an excellent resource for researchers, working professionals, academicians, policymakers, and defense companies.