Edge computing and analytics are fascinating the whole world of computing. Industry and business are keenly embracing this sound concept to develop customer-centric solutions by enhancing their operations, offerings, and outputs. There is a bevy of advancements in this domain that came with the arrival of IoT devices. The seamless convergence of microservices and serverless computing creates vast opportunities. With the help of IoT devices and these other developments, there has become a deep interest in business automation and additional improvisations in edge computing. With the steady growth of edge devices and applications of IoT fog/edge computing and analytics, there are also distinct challenges and threats. Research has been keenly focused on identifying and understanding these issues and shortcomings to bring viable solution approaches and algorithms. Cases on Edge Computing and Analytics describes the latest innovations, improvements, and transformations happening with edge devices and computing. It addresses the key concerns of the edge computing paradigm, how they are processed, and the various technologies and tools empowering edge computing and analytics. While highlighting topics within edge computing such as the key drivers for implementation, computing capabilities, security considerations, and use-cases, this book is ideal for IT industry professionals and project managers, computer scientists, computer engineers, and practitioners, stakeholders, researchers, academicians, and students looking for research on the latest trends and transitions in edge computing.

This reference text presents the state-of-the-art in edge computing, its primitives, devices and simulators, applications, and healthcare-based case studies. The text provides integration of blockchain with edge computing systems and integration of edge with Internet of Things (IoT) and cloud computing. It will facilitate readers to setup edge-based environment and work with edge analytics. It covers important topics, including cluster computing, fog computing, networking architecture, edge computing simulators, edge analytics, privacy-preserving schemes, edge computing with blockchain, autonomous vehicles, and cross-domain authentication. Aimed at senior undergraduate, graduate students and professionals in the fields of electrical engineering, electronics engineering, computer science, and information technology, this text: Discusses edge data storage security with case studies and blockchain integration with the edge computing system Covers theoretical methods with the help of applications, use cases, case studies, and examples Provides healthcare real-time case studies elaborated by utilizing the virtues of homomorphic encryption Discusses real-time interfaces, devices, and simulators in detail

Summarizes the current state and upcoming trends within the area of fog computing Written by some of the leading experts in the field, Fog Computing: Theory and Practice focuses on the technological aspects of employing fog computing in various application domains, such as smart healthcare, industrial process control and improvement, smart cities, and virtual learning environments. In addition, the Machine-to-Machine (M2M) communication methods for fog computing environments are covered in depth. Presented in two parts—Fog Computing Systems and Architectures, and Fog Computing Techniques and Application—this book covers such important topics as energy efficiency and Quality of Service (QoS) issues, reliability and fault tolerance, load balancing, and scheduling in fog computing systems. It also devotes special attention to emerging trends and the industry needs associated with utilizing the mobile edge computing, Internet of Things (IoT), resource and pricing estimation, and virtualization in the fog environments. Includes chapters on deep learning, mobile edge computing, smart grid, and intelligent transportation systems beyond the theoretical and foundational concepts Explores real-time traffic surveillance from video streams and interoperability of fog computing architectures Presents the latest research on data quality in the IoT, privacy, security, and trust issues in fog computing Fog Computing: Theory and Practice provides a platform for researchers, practitioners, and graduate students from computer science, computer engineering, and various other disciplines to gain a deep understanding of fog computing.

This book provides the state-of-the-art development on security and privacy for fog/edge computing, together with their system architectural support and applications. This book is organized into five parts with a total of 15 chapters. Each area corresponds to an important snapshot. The first part of this book presents an overview of fog/edge computing, focusing on its relationship with cloud technology and the future with the use of 5G communication. Several applications of edge computing are discussed. The second part of this book considers several security issues in fog/edge computing, including the secure storage and search services, collaborative intrusion detection method on IoT-fog computing, and the feasibility of deploying Byzantine agreement protocols in untrusted environments. The third part of this book studies the privacy issues in fog/edge computing. It first investigates the unique privacy challenges in fog/edge computing, and then discusses a privacy-preserving framework for the edge-based video analysis, a popular machine learning application on fog/edge. This book also covers the security architectural design of fog/edge computing, including a comprehensive overview of vulnerabilities in fog/edge computing within multiple architectural levels, the security and intelligent management, the implementation of network-function-virtualization-enabled multicasting in part four. It explains how to use the blockchain to realize security services. The last part of this book surveys applications of fog/edge computing, including the fog/edge computing in Industrial IoT, edge-based augmented reality, data streaming in fog/edge computing, and the blockchain-based application for edge-IoT. This book is designed for academics, researchers and government officials, working in the field of fog/edge computing and cloud computing. Practitioners, and business organizations (e.g., executives, system designers, and marketing professionals), who conduct teaching, research, decision making, and designing fog/edge technology will also benefit from this book The content of this book will be particularly useful for advanced-level students studying computer science, computer technology, and information systems, but also applies to students in business, education, and economics, who would benefit from the information, models, and case studies therein.

***BUSINESS BOOK AWARDS - FINALIST 2021*** Discover how 25 powerful technology trends are transforming 21st century businesses How will the latest technologies transform your business? Future Tech Trends in Practice will give you the knowledge of today’s most important technology trends, and how to take full advantage of them to grow your business. The book presents25 real-world technology trends along with their potential contributions to organisational success. You’ll learn how to integrate existing advancements and plan for those that are on the way. In this book, best-selling author, strategic business advisor, and respected futurist Bernard Marr explains the role of technology in providing innovative businesses solutions for companies of varying sizes and across different industries. He covers wide-ranging trends and provides an overview of how companies are using these new and emerging technologies in practice. You, too, can prepare your company for the potential and power of trending technology by examining these and other areas of innovation described in Future Tech Trends in Practice: Artificial intelligence, including machine and deep learning The Internet of Things and the rise of smart devices Self-driving cars and autonomous drones 3D printing and additive manufacturing Blockchain technology Genomics and gene editing Augmented, virtual and mixed reality When you understand the technology trends that are driving success, now and into the future, you’ll be better positioned to address and solve problems within your organisation.

The book aims to focus only on the 'edge computing' paradigm - as opposed to other similar disciplines such as fog computing - due to its unique characteristics where heterogeneous devices can be equipped with decision-making processes and automation procedures to carry out applications (mostly) across widely geographically distributed areas. The book is sectioned into three parts (models, technologies and applications) to reflect complementary viewpoints and shed light onto various aspect of edge computing platforms. The first part (models) serves as an introductory section to differentiate edge computing from its fairly close/similar paradigms such as fog computing, multi-access edge computing and mobile cloud computing. It then highlights various theoretical models through which edge computing systems could be mathematically expressed and their relevant technological problems could be systematically solved or optimized. The second part (technologies) focuses on different technologies (virtualization, networking, orchestration, etc.) where edge computing systems could be realized. And finally, the third part (applications) focuses on various application domains (e.g., smart cities) where edge computing systems could, or already did, help in providing novel services beyond the reach of pure/typical cloud computing solutions.

Harness the power of Python to analyze your IoT data KEY FEATURES ● Learn how to build an IoT Data Analytics infrastructure. ● Explore advanced techniques for IoT Data Analysis with Python. ● Gain hands-on experience applying IoT Data Analytics to real-world situations. DESCRIPTION Python is a popular programming language for data analytics, and it is also well-suited for IoT Data Analytics. By leveraging Python's versatility and its rich ecosystem of libraries and tools, Data Analytics for IoT can unlock valuable insights, enable predictive capabilities, and optimize decision-making in various IoT applications and domains. The book begins with a foundation in IoT fundamentals, its role in digital transformation, and why Python is the preferred language for IoT Data Analytics. It then covers essential data analytics concepts, how to establish an IoT Data Analytics environment, and how to design and manage real-time IoT data flows. Next, the book discusses how to implement Descriptive Analytics with Pandas, Time Series Forecasting with Python libraries, and Monitoring, Preventive Maintenance, Optimization, Text Mining, and Automation strategies. It also introduces Edge Computing and Analytics, discusses Continuous and Adaptive Learning concepts, and explores data flow and use cases for Edge Analytics. Finally, the book concludes with a chapter on IoT Data Analytics for self-driving cars, using the CRISP-DM framework for data collection, modeling, and deployment. By the end of the book, you will be equipped with the skills and knowledge needed to extract valuable insights from IoT data and build real-world applications. WHAT YOU WILL LEARN ● Explore the essentials of IoT Data Analytics and the Industry 4.0 revolution. ● Learn how to set up the IoT Data Analytics environment. ● Equip Python developers with data analysis foundations. ● Learn to build data lakes for real-time IoT data streaming. ● Learn to deploy machine learning models on edge devices. ● Understand Edge Computing with MicroPython for efficient IoT Data Analytics. WHO THIS BOOK IS FOR If you are an experienced Python developer who wants to master IoT Data Analytics, or a newcomer who wants to learn Python and its applications in IoT, this book will give you a thorough understanding of IoT Data Analytics and practical skills for real-world use cases. TABLE OF CONTENTS 1. Necessity of Analytics Across IoT 2. Up and Running with Data Analytics Fundamentals 3. Setting Up IoT Analytics Environment 4. Managing Data Pipeline and Cleaning 5. Designing Data Lake and Executing Data Transformation 6. Implementing Descriptive Analytics Using Pandas 7. Time Series Forecasting and Predictions 8. Monitoring and Preventive Maintenance 9. Model Deployment on Edge Devices 10. Understanding Edge Computing with MicroPython 11. IoT Analytics for Self-driving Vehicles