In the process of transforming modern manufacturing into intelligent manufacturing, more and more manufacturing processes are being deployed digitally on the Internet. As a novel digital industrial infrastructure and new application ecology, Industrial Internet could integrate the novel information and communication technology into various industrial manufacturing processes, through the safe and reliable intelligent interconnection of factors concerning people, machines and things. It aims at significantly improving the digitalization, networking and intelligence of manufacturing processes through the comprehensive interconnection of all manufacturing factors. To deploy and run traditional manufacturing processes on Industrial Internet efficiently, corresponding digital technical means are called for. These technical means play an important role in many potential applications of intelligent manufacturing, e.g. integrating product lifecycle data, strengthening industrial chain cooperation, providing decision-making service information oriented on the production organization process, and providing data support for product optimization and upgrading. This book focuses on the theory, techniques and means for integrating intelligent Industrial Internet applications deployed via mobile edge computing. Concretely, it mainly studies the intelligent theory, techniques and means from a problem-driven and demand-driven perspective, which supports collaborative design, agile manufacturing, remote operation and maintenance on Industrial Internet. These technologies primarily include Industrial Internet-centric intelligent resource scheduling methods, various types of industrial data privacy protection, intelligent version management technologies supporting digital twin applications, various data mining algorithms for intelligent execution of business processes, etc. Given its scope, the book offers a valuable theoretical and technical resource for project managers, technical developers, researchers, educators and students at colleges and universities.

Industrial IoT (IIoT) and Industry 4.0 are newly developing and fast emerging domains of interest among students, researchers, and professionals in academia and industry. Due to the popular demand of this topic, Introduction to Industrial Internet of Things and Industry 4.0 is written to serve a diverse readership from the domains of computer science and engineering, mechanical engineering, information technology, industrial engineering, electronics engineering, and other related branches of engineering. Based on the lead author’s massive open online courses (MOOCs), this book can be used as a textbook on the emerging paradigm of Industry 4.0 and IIoT, as well as a reference for professionals working in sectors of IIoT. The book covers the significant aspects of IIoT in detail, including sensors, actuators, data transmission, and data acquisition, which form the core of IIoT. Topics and concepts are presented in a comprehensive manner, so that readers can develop expertise and knowledge. The book helps beginners to gain a basic idea of Industry 4.0 and IIoT as the first section is an overview of IoT applications, infrastructure-based protocols, cloud computing, and fog computing. The second section is designed to impart a basic knowledge of Industry 4.0 and IIoT as well as of the different phases of development in industry. Delving into more advanced areas, other sections in the book cover: The business models and reference architecture of IIoT The technological aspects of Industry 4.0 and IIoT Predictive and prescriptive analytics applied in IIoT-based implementations Applications and case studies of IIoT Key enabling technologies of IIoT To aid students and professional master IIoT and Industry 4.0, the book includes conceptual questions, exercises, and learning objectives.

This book provides a comprehensive overview of wireless technologies for industrial network systems. The authors first describe the concept of industrial network systems and their application to industrial automation. They then go on to cover the role of sensing and control in industrial network systems, and the challenge of sensing and control in the industrial wireless environment. Then, the existing techniques for resource efficiency information transmission are introduced and studied. Afterward, the authors introduce sensing and control-oriented transmission for industrial network systems, which take advantage of spatial diversity gain to overcome the interference and fading, which in turn improves the transmission reliability without expending extra spectrum resources and enlarging the transmission delay. Subsequently, edge assisted efficient transmission schemes are introduced, which integrate the capacities of communication, computing, and control to relieve the contradiction of resource limitation and massive data. Finally, the authors discuss open research issues and future works about information transmission in industrial network systems.

The book studies emerging and sustaining technologies for applications of Industry 5.0 to develop technological solutions to address numerous real-life challenges to solve sustainable development-related issues. It identifies limitations, pitfalls, and open research questions in industry 5.0, discusses real-time problems, and challenges with equivalent solutions with a focus on sustainable growth to develop, humanization and environmentally friendly intelligent system applications. It analyses applications enabled by Industry 5.0 such as healthcare, supply chain, smart framing, remote sensing, production in manufacturing, and cloud manufacturing. It also includes the difficulties and problems posed by the organization between robots and humans on the assembly line to maintain sustainability. Addresses key challenges in implementing intelligent systems in IoT-based applications, including issues ranging from cost and energy efficiency to availability and quality of service Explores the technologies to allow human-machine association and its impact on consumption and sustainability Provides sustainable solutions to emerging industrial problems, especially in healthcare, manufacturing, remote sensing, environmental engineering Examines need for data pre-processing, classification & prediction, Cluster Analysis, Mining Multimedia, Text, and Web Data, Advanced machine learning techniques for scientific programming in Industry Presents success stories in the form of case studies of IIoT, IIoRT, Big Data, Intelligent Systems, Deep Learning in Industry 5.0 era The text is for postgraduate students, professionals, and academic researchers working in the fields of computer science and information technology, especially for professionals and researchers interested in the technological side of sustainable development.

This book constitutes the proceedings of the International Conference on Big Data Intelligence and Computing, DataCom 2022, which took place in Denarau Island, Fiji, in December 2022. The 30 full papers included in this volume were carefully reviewed and selected from 88 submissions. The papers detail big data analytics solutions, distributed computation paradigms, on-demand services, autonomic systems, and pervasive applications.

The goal of this book is to explore various security paradigms such as Machine Learning, Big data, Cyber Physical Systems, and Blockchain to address both intelligence and reconfigurability in various IoT devices. The book further aims to address and analyze the state of the art of blockchain-based intelligent networks in IoT systems and related technologies including healthcare sector. AI can ease, optimize, and automate the blockchain-based decision-making process for better governance and higher performance in IoT systems. Considering the incredible progress made by AI models, a blockchain system powered by intelligent AI algorithms can detect the existence of any kind of attack and automatically invoke the required defense mechanisms. In case of unavoidable damage, AI models can help to isolate the compromised component from the blockchain platform and safeguard the overall system from crashing. Furthermore, AI models can also contribute toward the robustness and scalability of blockchain-based intelligent IoT networks. The book is designed to be the first-choice reference at university libraries, academic institutions, research and development centers, information technology centers, and any institutions interested in integration of AI and IoT. The intended audience of this book include UG/PG students, Ph.D. scholars of this fields, industry technologists, young entrepreneurs, professionals, network designers, data scientists, technology specialists, practitioners, and people who are interested in exploring the role of AI and blockchain technology in IoT systems.

Internet of Things: Challenges, Advances, and Applications provides a comprehensive introduction to IoT, related technologies, and common issues in the adoption of IoT on a large scale. It surveys recent technological advances and novel solutions for challenges in the IoT environment. Moreover, it provides detailed discussion of the utilization of IoT and its underlying technologies in critical application areas, such as smart grids, healthcare, insurance, and the automotive industry. The chapters of this book are authored by several international researchers and industry experts. This book is composed of 18 self-contained chapters that can be read, based on interest. Features: Introduces IoT, including its history, common definitions, underlying technologies, and challenges Discusses technological advances in IoT and implementation considerations Proposes novel solutions for common implementation issues Explores critical application domains, including large-scale electric power distribution networks, smart water and gas grids, healthcare and e-Health applications, and the insurance and automotive industries The book is an excellent reference for researchers and post-graduate students working in the area of IoT, or related areas. It also targets IT professionals interested in gaining deeper knowledge of IoT, its challenges, and application areas.

This volume includes extended and revised versions of a set of selected papers from the First International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2020, held as virtual event in November 4-6, 2020 and Second International Conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2021, held as virtual event in October 25-27, 2021. The 9 full papers included in this book were carefully reviewed and selected from 44 submissions. They were organized in topical sections as follows: ​on kernel search based gaussian process anomaly detection; general architecture framework and general modelling framework.

This book introduces Industrial AI in multiple dimensions. Industrial AI is a systematic discipline which focuses on developing, validating and deploying various machine learning algorithms for industrial applications with sustainable performance. Combined with the state-of-the-art sensing, communication and big data analytics platforms, a systematic Industrial AI methodology will allow integration of physical systems with computational models. The concept of Industrial AI is in infancy stage and may encompass the collective use of technologies such as Internet of Things, Cyber-Physical Systems and Big Data Analytics under the Industry 4.0 initiative where embedded computing devices, smart objects and the physical environment interact with each other to reach intended goals. A broad range of Industries including automotive, aerospace, healthcare, semiconductors, energy, transportation, mining, construction, and industrial automation could harness the power of Industrial AI to gain insights into the invisible relationship of the operation conditions and further use that insight to optimize their uptime, productivity and efficiency of their operations. In terms of predictive maintenance, Industrial AI can detect incipient changes in the system and predict the remains useful life and further to optimize maintenance tasks to avoid disruption to operations.