Table Of Content

A Roadmap for Enabling Industry 4.0 by Artificial Intelligence Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106 Publishers at Scrivener Martin Scrivener ([email protected]) Phillip Carmical ([email protected]) A Roadmap for Enabling Industry 4.0 by Artificial Intelligence Edited by Jyotir Moy Chatterjee Harish Garg and R. N. Thakur This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2023 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or other- wise, except as permitted by law. 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Library of Congress Cataloging-in-Publication Data ISBN 978-1-119-90485-4 Cover image: Pixabay.Com Cover design by Russell Richardson Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines Printed in the USA 10 9 8 7 6 5 4 3 2 1 Contents Preface xv 1 Artificial Intelligence—The Driving Force of Industry 4.0 1 Hesham Magd, Henry Jonathan, Shad Ahmad Khan and Mohamed El Geddawy 1.1 Introduction 2 1.2 Methodology 2 1.3 Scope of AI in Global Economy and Industry 4.0 3 1.3.1 Artificial Intelligence—Evolution and Implications 4 1.3.2 Artificial Intelligence and Industry 4.0—Investments and Returns on Economy 5 1.3.3 The Driving Forces for Industry 4.0 7 1.4 Artificial Intelligence—Manufacturing Sector 8 1.4.1 AI Diversity—Applications to Manufacturing Sector 9 1.4.2 Future Roadmap of AI—Prospects to Manufacturing Sector in Industry 4.0 12 1.5 Conclusion 13 References 14 2 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview 17 Sachi Pandey, Vijay Laxmi and Rajendra Prasad Mahapatra 2.1 Introduction 17 2.2 Industrial Transformation/Value Chain Transformation 18 2.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT 19 2.2.2 Second Scenario: Selling Outcome (User Demand)– Based Services Using IIoT 20 2.3 IIoT Reference Architecture 20 2.4 IIoT Technical Concepts 22 2.5 IIoT and Cloud Computing 26 v vi Contents 2.6 IIoT and Security 27 References 29 3 Artificial Intelligence of Things (AIoT) and Industry 4.0– Based Supply Chain (FMCG Industry) 31 Seyyed Esmaeil Najafi, Hamed Nozari and S. A. Edalatpanah 3.1 Introduction 32 3.2 Concepts 33 3.2.1 Internet of Things 33 3.2.2 The Industrial Internet of Things (IIoT) 34 3.2.3 Artificial Intelligence of Things (AIoT) 35 3.3 AIoT-Based Supply Chain 36 3.4 Conclusion 40 References 40 4 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 43 Alireza Goli, Amir-Mohammad Golmohammadi and S. A. Edalatpanah 4.1 Introduction 44 4.2 Literature Review 45 4.2.1 Summary of the First Three Industrial Revolutions 45 4.2.2 Emergence of Industry 4.0 45 4.2.3 Some of the Challenges of Industry 4.0 47 4.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting 48 4.4 Proposed Approach 50 4.4.1 Mathematical Model 50 4.4.2 Advantages of the Proposed Model 51 4.5 Discussion and Conclusion 52 References 53 5 Integrating IoT and Deep Learning—The Driving Force of Industry 4.0 57 Muhammad Farrukh Shahid, Tariq Jamil Saifullah Khanzada and Muhammad Hassan Tanveer 5.1 Motivation and Background 58 5.2 Bringing Intelligence Into IoT Devices 60 5.3 The Foundation of CR-IoT Network 62 5.3.1 Various AI Technique in CR-IoT Network 63 5.3.2 Artificial Neural Network (ANN) 63 5.3.3 Metaheuristic Technique 64 Contents vii 5.3.4 Rule-Based System 64 5.3.5 Ontology-Based System 65 5.3.6 Probabilistic Models 65 5.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network 65 5.5 Realization of CR-IoT Network in Daily Life Examples 69 5.6 AI-Enabled Agriculture and Smart Irrigation System—Case Study 70 5.7 Conclusion 75 References 75 6 A Systematic Review on Blockchain Security Technology and Big Data Employed in Cloud Environment 79 Mahendra Prasad Nath, Sushree Bibhuprada B. Priyadarshini, Debahuti Mishra and Brojo Kishore Mishra 6.1 Introduction 80 6.2 Overview of Blockchain 83 6.3 Components of Blockchain 85 6.3.1 Data Block 85 6.3.2 Smart Contracts 87 6.3.3 Consensus Algorithms 87 6.4 Safety Issues in Blockchain Technology 88 6.5 Usage of Big Data Framework in Dynamic Supply Chain System 91 6.6 Machine Learning and Big Data 94 6.6.1 Overview of Shallow Models 95 6.6.1.1 Support Vector Machine (SVM) 95 6.6.1.2 Artificial Neural Network (ANN) 95 6.6.1.3 K-Nearest Neighbor (KNN) 95 6.6.1.4 Clustering 96 6.6.1.5 Decision Tree 96 6.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems 96 6.7.1 Replenishment Planning 96 6.7.2 Optimizing Orders 97 6.7.3 Arranging and Organizing 97 6.7.4 Enhanced Demand Structuring 97 6.7.5 Real-Time Management of the Supply Chain 97 6.7.6 Enhanced Reaction 98 6.7.7 Planning and Growth of Inventories 98 viii Contents 6.8 IoT-Enabled Blockchains 98 6.8.1 Securing IoT Applications by Utilizing Blockchain 99 6.8.2 Blockchain Based on Permission 101 6.8.3 Blockchain Improvements in IoT 101 6.8.3.1 Blockchain Can Store Information Coming from IoT Devices 101 6.8.3.2 Secure Data Storage with Blockchain Distribution 101 6.8.3.3 Data Encryption via Hash Key and Tested by the Miners 102 6.8.3.4 Spoofing Attacks and Data Loss Prevention 102 6.8.3.5 Unauthorized Access Prevention Using Blockchain 103 6.8.3.6 Exclusion of Centralized Cloud Servers 103 6.9 Conclusions 103 References 104 7 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet 111 Yash Gupta, Shilpi Sharma, Naveen Rajan P. and Nadia Mohamed Kunhi 7.1 Introduction 112 7.2 Related Work 113 7.3 Methodology 114 7.3.1 Splitting of Data (Test/Train) 116 7.3.2 Prophet Model 116 7.3.3 Data Cleaning 119 7.3.4 Model Implementation 119 7.4 Results 120 7.4.1 Comparing Forecast to Actuals 121 7.4.2 Adding Holidays 122 7.4.3 Comparing Forecast to Actuals with the Cleaned Data 122 7.5 Conclusion and Future Scope 122 References 125 8 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems 127 Manas Kumar Yogi and A.S.N. Chakravarthy 8.1 Introduction 128 8.2 Related Work 131