Table Of ContentA Roadmap for Enabling
Industry 4.0 by Artificial
Intelligence
Scrivener Publishing
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Publishers at Scrivener
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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
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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
