Название: Industry 4.0 Interoperability, Analytics, Security, and Case Studies
Автор: G. Rajesh, X. Mercilin Raajini
Издательство: CRC Press
Серия: Big Data for Industry 4.0: Challenges and Applications
Год: 2021
Страниц: 332
Язык: английский
Формат: pdf (true), epub
Размер: 12.9 MB

All over the world, vast research is in progress on the domain of Industry 4.0 and related techniques. Industry 4.0 is expected to have a very high impact on labor markets, global value chains, education, health, environment, and many social economic aspects. Industry 4.0 Interoperability, Analytics, Security, and Case Studies provides a deeper understanding of the drivers and enablers of Industry 4.0. It includes real case studies of various applications related to different fields, such as cyber physical systems (CPS), Internet of Things (IoT), cloud computing, Machine Learning, virtualization, decentralization, blockchain, fog computing, and many other related areas. Also discussed are interoperability, design, and implementation challenges.

Big Data Analytics (BDA) can be referred to as “the process of analyzing large scale datasets in order to find unknown correlations, hidden patterns, and other valuable information which is not able to be analysed using conventional data analytics”, as the conventional data analysis techniques are no longer effective because of the special characteristics of big data: massive, heterogeneous, high dimensional, complex, erroneous, unstructured, noisy, and incomplete. BDA has attracted attention from not only academic but also industrial scientists as the requirement of discovering hidden trends in large scale datasets increases. Reference compared the impact of BDA for Industry 4.0 with the invention of the microscope and telescope for biology and astronomy, respectively. Recently, the considerable development in the ubiquitous IoT (i.e., Internet of Things), sensor networks, and CPS (i.e., cyber-physical systems) have expanded the data-collection process to an enormous scale in numerous domains, including: social media, smart cities, education, health care, finance, agriculture, etc. Various advanced techniques to analyze data (i.e., ML, computational intelligence, data mining, natural language processing) and potential strategies (i.e., parallelization, divide and conquer, granular computing, incremental learning, instance selection, feature selection, and sampling) can help to handle Big Data issues. Empowering more efficient processing, and making better decisions can also be obtained by using these techniques and strategies.

Researchers, academicians, and those working in industry around the globe will find this book of interest.

FEATURES:

Provides an understanding of the drivers and enablers of Industry 4.0
Includes real case studies of various applications for different fields
Discusses technologies such as cyber physical systems (CPS), Internet of Things (IoT), cloud computing, machine learning, virtualization, decentralization, blockchain, fog computing, and many other related areas
Covers design, implementation challenges, and interoperability
Offers detailed knowledge on Industry 4.0 and its underlying technologies, research challenges, solutions, and case studies

Contents:
Editors
Contributors
Chapter 1 Big Data Analytics and Machine Learning for Industry 4.0: An Overview
Chapter 2 Impact of Blockchain-Based Cyber Security Implementing Industry 4.0
Chapter 3 Hybrid Huffman Tree Model for Securing Medical Data Using Steganography
Chapter 4 Energy Conservation in IOT-Based Intelligent Transport System
Chapter 5 Digital Information System for Attack Reduction in Industry 4.0
Chapter 6 An Intelligent Blockchain-Based Framework for Securing Smart Grid
Chapter 7 Intelligent Fog Computing for Industrial Wireless Sensor Networks
Chapter 8 A Secure Data Sharing Scheme Based on Blockchain for Industrial Internet of Things Using Consensus Algorithm
Chapter 9 Smart Trust Management Scheme for Detecting Attacks in IoT and Ubiquitous Computing
Chapter 10 IoT-Based Smart Pipeline Leakage Detecting System for Petroleum Industries
Chapter 11 IOT-Based Smart Irrigation Systems
Chapter 12 Safety Wing for Industry (SWI 2020) – An Advanced Unmanned Aerial Vehicle Design for Safety and Security Facility Management in Industries
Chapter 13 An Efficient Decentralized Medical Prescription Tracking Using Blockchain
Chapter 14 Blockchain for Industry 4.0: An Assessment of Blockchain Adoptability
Chapter 15 An Effective E-Learning Mechanism to Meet the Learning Demand of Industry 4.0
Index