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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.
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The textbook covers the main aspects of Edge Computing, from a thorough look at the technology to the standards and industry associations working in the field. The book is conceived as a textbook for graduate students but also functions as a working guide for developers, engineers, and researchers. The book aims not only at providing a comprehensive technology and standard reference overview for students, but also useful research insights and practical exercises for edge software developers and investigators in the area (and for students looking to apply their skills). A particular emphasis is given Multi-access Edge Computing (MEC) as defined in European Telecommunications Standards Institute (ETSI), in relationship with other standard organizations like 3GPP, thus in alignment with the recent industry efforts to produce harmonized standards for edge computing leveraging both ETSI ISG MEC and 3GPP specifications. Practical examples of Edge Computing implementation from industry groups, associations, companies and edge developers, complete the book and make it useful for students entering the field. The book includes exercises, examples, and quizzes throughout.
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Real-Time Operations Facilitations or ROOF is a class of computational framework for Edge Computing with Internet of Things (IoT). The ROOF framework provides support for building scalable, secure, and robust Internet of Things applications with limited tools in resource constrained situations. The ROOF helps in developing federated architecture for large scale IoT operations. At the same time, it can provide micro-models for rapid prototyping and quick decision making. It also provides autonomic architectural support for spatio-temporal events handling. The ROOF framework has been designed to provide innovation, reuse, better user experience, security by design, for harmonizing horizontal as well as vertical integration for the Internet of Things ecosystem.
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This reference text introduces concepts of edge computing and its integration with blockchain technology, cloud computing, and internet of things (IoT). It will serve as a useful text for senior undergraduate, graduate students and professionals in the fields of electrical engineering, electronics engineering, and computer science.
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Understand how to use K3s and k3OS for different use cases and discover best practices for building an edge computing system Key Features A guide to implementing an edge computing environment Reduce latency and costs for real-time applications running at the edge Find stable and relevant cloud native open source software to complement your edge environments Book Description Edge computing is a way of processing information near the source of data instead of processing it on data centers in the cloud. In this way, edge computing can reduce latency when data is processed, improving the user experience on real-time data visualization for your applications. Using K3s, a light-weight Kubernetes and k3OS, a K3s-based Linux distribution along with other open source cloud native technologies, you can build reliable edge computing systems without spending a lot of money. In this book, you will learn how to design edge computing systems with containers and edge devices using sensors, GPS modules, WiFi, LoRa communication and so on. You will also get to grips with different use cases and examples covered in this book, how to solve common use cases for edge computing such as updating your applications using GitOps, reading data from sensors and storing it on SQL and NoSQL databases. Later chapters will show you how to connect hardware to your edge clusters, predict using machine learning, and analyze images with computer vision. All the examples and use cases in this book are designed to run on devices using 64-bit ARM processors, using Raspberry Pi devices as an example. By the end of this book, you will be able to build your own edge computing systems using the content of the chapters as Lego pieces to fit your needs. What you will learn Configure k3OS and K3s for development and production scenarios Package applications into K3s for shipped-node scenarios Deploy in occasionally connected scenarios, from one node to one million nodes Manage GitOps for applications across different locations Use open source cloud native software to complement your edge computing systems Implement observability event-driven and serverless edge applications Collect and process data from sensors at the edge and visualize it into the cloud Who this book is for This book is for engineers (developers and/or operators) seeking to bring the cloud native benefits of GitOps and Kubernetes to the edge. Anyone with basic knowledge of Linux and containers looking to learn Kubernetes using examples applied to edge computing and hardware systems will benefit from this book.
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The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations. Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases. FEATURES The opportunities and benefits of intelligent edge computing Edge architecture and infrastructure AI-enhanced analytics in an edge environment Encryption for securing information An Edge AI system programmed with Tiny Machine learning algorithms for decision making An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge Ambient intelligence in healthcare services and in development of consumer electronic systems Smart manufacturing of unmanned aerial vehicles (UAVs) AI, edge computing, and blockchain in systems for environmental protection Case studies presenting the potential of leveraging AI in 5G wireless communication
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Edge computing. --- Electronic data processing --- Distributed processing
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Edge computing. --- Electronic data processing --- Distributed processing
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Edge computing. --- Electronic data processing --- Distributed processing
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