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The book presents comprehensive and up-to-date technological solutions to the main aspects regarding the applications of artificial intelligence to Industry 4.0.
The industry 4.0 vision has been discussed for quite a while and the enabling technologies are now mature enough to turn this vision into a grand reality sooner rather than later. The fourth industrial revolution, or Industry 4.0, involves the infusion of technology-enabled deeper and decisive automation into manufacturing processes and activities. Several information and communication technologies (ICT) are being integrated and used towards attaining manufacturing process acceleration and augmentation. This book explores and educates the recent advancements in blockchain technology, artificial intelligence, supply chains in manufacturing, cryptocurrencies, and their crucial impact on realizing the Industry 4.0 goals. The book thus provides a conceptual framework and roadmap for decision-makers for implementing this transformation.
Audience
Computer and artificial intelligence scientists, information and communication technology specialists, and engineers in electronics and industrial manufacturing will find this book very useful.
Jyotir Moy Chatterjee is an assistant professor in the Information Technology department at Lord Buddha Education Foundation (LBEF), Kathmandu, Nepal. He has published more than 60 research papers in international publications, three conference papers, three authored books, 10 edited books, 16 book chapters, two Master's theses converted into books, and one patent.
Harish Garg, PhD, is an associate professor at Thapar Institute of Engineering & Technology, Deemed University, Patiala, Punjab, India. His research interests include soft computing, decision-making, aggregation operators, evolutionary algorithm, expert systems, and decision support systems. He has published more than 300 papers published in refereed international journals. Dr. Garg is the Editor-in-Chief of Annals of Optimization Theory and Practice.
R N Thakur, PhD, is a senior lecturer in the Information Technology Department, Lord Buddha Education Foundation (LBEF), Kathmandu, Nepal. He has published about 20 research articles in various journals.
Preface xv
1 Artificial Intelligence-The Driving Force of Industry 4.0 1Hesham 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 17Sachi 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
2.6 IIoT and Security 27
References 29
3 Artificial Intelligence of Things (AIoT) and Industry 4.0- Based Supply Chain (FMCG Industry) 31Seyyed 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 43Alireza 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 57Muhammad 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
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 79Mahendra 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
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 111Yash 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 127Manas Kumar Yogi and A.S.N. Chakravarthy
8.1 Introduction 128
8.2 Related Work 131
8.3 Proposed Mechanism 133
8.4 Experimental Results 135
8.5 Future Directions 137
8.6 Conclusion 138
References 138
9 Environmental and Industrial Applications Using Internet of Things (IoT) 141Manal Fawzy, Alaa El Din Mahmoud and Ahmed M. Abdelfatah
9.1 Introduction 142
9.2 IoT-Based Environmental Applications 146
9.3 Smart Environmental Monitoring 147
9.3.1 Air Quality Assessment 147
9.3.2 Water Quality Assessment 148
9.3.3 Soil Quality Assessment 150
9.3.4 Environmental Health-Related to COVID- 19
Monitoring 150
9.4 Applications of Sensors Network in Agro-Industrial System 151
9.5 Applications of IoT in Industry 153
9.5.1 Application of IoT in the Autonomous Field 153
9.5.2 Applications of IoT in Software Industries 155
9.5.3 Sensors in Industry 156
9.6 Challenges of IoT Applications in Environmental and Industrial Applications 157
9.7 Conclusions and Recommendations 159
Acknowledgments 159
References 159
10 An Introduction to Security in Internet of Things (IoT) and Big Data 169Sushree Bibhuprada B. Priyadarshini, Suraj Kumar Dash, Amrit Sahani, Brojo Kishore Mishra and Mahendra Prasad Nath
10.1 Introduction 170
10.2 Allusion Design of IoT 172
10.2.1 Stage 1-Edge Tool 172
10.2.2 Stage 2-Connectivity 172
10.2.3 Stage 3-Fog Computing 173
10.2.4 Stage 4-Data Collection 173
10.2.5 Stage 5-Data Abstraction 173
10.2.6 Stage 6-Applications 173
10.2.7 Stage 7-Cooperation and Processes 174
10.3 Vulnerabilities of IoT 174
10.3.1 The Properties and Relationships of Various IoT Networks 174
10.3.2 Device Attacks 175
10.3.3 Attacks on Network 175
10.3.4 Some Other Issues 175
10.3.4.1 Customer Delivery Value 175
10.3.4.2 Compatibility Problems With Equipment 176
10.3.4.3 Compatibility and Maintenance 176
10.3.4.4 Connectivity Issues in the Field of Data 176
10.3.4.5 Incorrect Data Collection and Difficulties 177
10.3.4.6 Security Concern 177
10.3.4.7 Problems in Computer Confidentiality 177
10.4 Challenges in Technology 178
10.4.1 Skepticism of Consumers 178
10.5 Analysis of IoT Security 179
10.5.1 Sensing Layer Security Threats 180
10.5.1.1 Node Capturing 180
10.5.1.2 Malicious Attack by Code Injection 180
10.5.1.3 Attack by Fake Data Injection 180
10.5.1.4 Sidelines Assaults 181
10.5.1.5 Attacks During Booting Process 181
10.5.2 Network Layer Safety Issues 181
10.5.2.1 Attack on Phishing Page 181
10.5.2.2 Attacks on Access 182
10.5.2.3 Attacks on Data Transmission 182
10.5.2.4 Attacks on Routing 182
10.5.3 Middleware Layer Safety Issues 182
10.5.3.1 Attack by SQL Injection 183
10.5.3.2 Attack by Signature Wrapping 183
10.5.3.3 Cloud Attack Injection with Malware 183
10.5.3.4 Cloud Flooding Attack 183
10.5.4 Gateways Safety Issues 184
10.5.4.1 On-Boarding Safely 184
10.5.4.2 Additional Interfaces 184
10.5.4.3 Encrypting End-to-End 184
10.5.5 Application Layer Safety Issues 185
10.5.5.1 Theft of Data 185
10.5.5.2 Attacks at Interruption in Service 185
10.5.5.3 Malicious Code Injection Attack 185
10.6 Improvements and Enhancements Needed for IoT Applications in the Future 186
10.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) 189
10.8 Conclusion 192
References 193
11 Potential, Scope, and Challenges of Industry 4.0 201Roshan Raman and Aayush Kumar
11.1 Introduction 202
11.2 Key Aspects for a Successful Production 202
11.3 Opportunities with Industry 4.0 204
11.4 Issues in Implementation of Industry 4.0 206
11.5 Potential Tools Utilized in Industry 4.0 207
11.6 Conclusion 210
References 210
12 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges 215Roshan Raman and Aditya Ranjan
12.1 Introduction 216
12.2 Changing Market Demands 217
12.2.1 Individualization 218
12.2.2 Volatility 218
12.2.3 Efficiency in Terms of Energy Resources 218
12.3 Recent Technological Advancements 219
12.4 Industrial Revolution 4.0 221
12.5 Challenges to Industry 4.0 224
12.6 Conclusion 225
References 226
13 The Role of Multiagent System in Industry 4.0 227Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan and Rudra Pratap Ojha
13.1 Introduction 228
13.2 Characteristics and Goals of Industry 4.0 Conception 228
13.3 Artificial Intelligence 231
13.3.1 Knowledge-Based Systems 232
13.4 Multiagent Systems 234
13.4.1 Agent Architectures 234
13.4.2 Jade 238
13.4.3 System Requirements Definition 239
13.4.4 HMI Development 240
13.5 Developing Software of Controllers Multiagent Environment Behavior Patterns 240
13.5.1 Agent Supervision 240
13.5.2 Documents Dispatching Agents 241
13.5.3 Agent Rescheduling 242
13.5.4 Agent of Executive 242
13.5.5 Primary Roles of High-Availability Agent 243
13.6 Conclusion 244
References 244
14 An Overview of Enhancing Encryption Standards for Multimedia in Explainable Artificial Intelligence Using Residue Number Systems for Security 247Akeem Femi Kadri, Micheal Olaolu Arowolo, Ayisat Wuraola Yusuf-Asaju, Kafayat Odunayo Tajudeen and Kazeem Alagbe Gbolagade
14.1 Introduction 248
14.2 Reviews of Related Works 250
14.3 Materials and Methods 258
14.3.1 Multimedia 258
14.3.2 Artificial Intelligence and Explainable Artificial Intelligence 261
14.3.3 Cryptography 262
14.3.4 Encryption and Decryption 265
14.3.5 Residue Number System 266
14.4 Discussion and Conclusion 268
References 268
15 Market Trends with Cryptocurrency Trading in Industry 4.0 275Varun Khemka, Sagar Bafna, Ayush Gupta, Somya Goyal and Vivek Kumar Verma
15.1 Introduction 276
15.2 Industry Overview 276
15.2.1 History (From Barter to Cryptocurrency) 276
15.2.2 In the Beginning Was Bitcoin 278
15.3 Cryptocurrency Market 279
15.3.1 Blockchain 279
15.3.1.1 Introduction to Blockchain Technology 279
15.3.1.2 Mining 280
15.3.1.3 From Blockchain to Cryptocurrency 281
15.3.2 Introduction to Cryptocurrency Market 281
15.3.2.1 What is a Cryptocurrency? 281
15.3.2.2 Cryptocurrency Exchanges 283
15.4 Cryptocurrency Trading 283
15.4.1 Definition 283
15.4.2 Advantages 283
15.4.3 Disadvantages 284
15.5 In-Depth Analysis of Fee Structures and Carbon Footprint in Blockchain 285
15.5.1 Need for a Fee-Driven System 285
15.5.2 Ethereum Structure 286
15.5.3 How is the Gas Fee Calculated? 287
15.5.3.1 Why are Ethereum Gas Prices so High? 287
15.5.3.2 Carbon Neutrality 287
15.6 Conclusion 291
References 292
16 Blockchain and Its Applications in Industry 4.0 295Ajay Sudhir Bale, Tarun Praveen Purohit, Muhammed Furqaan Hashim and Suyog Navale
16.1 Introduction 296
16.2 About Cryptocurrency 296
16.3 History of Blockchain and Cryptocurrency 298
16.4 Background of Industrial Revolution 300
16.4.1 The First Industrial Revolution 301
16.4.2 The Second Industrial Revolution 301
16.4.3 The Third Industrial Revolution 302
16.4.4 The Fourth Industrial Revolution 302
16.5 Trends of Blockchain 303
16.6 Applications of Blockchain in Industry 4.0 304
16.6.1 Blockchain and the Government 304
16.6.2 Blockchain in the Healthcare Sector 304
16.6.3 Blockchain in Logistics and Supply Chain 306
16.6.4 Blockchain in the Automotive Sector 307
16.6.5 Blockchain in the Education Sector 308
16.7 Conclusion 309
References 310
Index 315
Hesham Magd1*, Henry Jonathan2, Shad Ahmad Khan3 and Mohamed El Geddawy4
1Quality Assurance & Accreditation, Faculty of Business and Economics, Muscat, Sultanate of Oman
2Department of Transportation, Logistics, and Safety Management, Faculty of Business and Economics, Muscat, Sultanate of Oman
3College of Business, University of Buraimi, Al-Buraimi, Sultanate of Oman
4Prince Mohammed University, Al Khobar, Kingdom of Saudi Arabia
The 21st century reminds the dawn of the 4th industrial revolution (IR), which has brought new frontiers of technology utilization to industries. Industry 4.0 chiefly relays from the 3rd revolution, taking further the application of computer and automation domain to the current century. Industry 4.0 already has shown remarkable success due to the multifaceted specialisms in digitalization and artificial intelligence (AI). However, in the face of rapidly changing technology, the growth of industrialization depends significantly on the progressive involvement of artificial intelligence applications in wide areas of industrial products and processes. AI technology potential contribution to the global economy is $15.7 trillion currently with 2% contribution from the middle east region. The manufacturing sector investment and funding on project gained in USA and China through AI-enabled technologies by automotive and IT sectors leading GDP by 115% accounting to $77.5 billion in 2021. The factors driving the industry 4.0 mainly are competitiveness and innovation, cost reduction, and performance improvement, which led to the use of AI technology applications to industrial sector. AI-enabled applications are explored in automotive, consumer products, industrial manufacturing, telecommunication sectors to enhance product quality, design in the process. Despite the benefits offered by the AI applications to industries, the barriers for effective adoption of industry 4.0 ideology to all sectors is a time-taking process.
Keywords: Technology, organization, manufacturing, industry, sector, application, tools
The current century marks the renaissance of fourth industrial revolution the term that was first introduced by Klaus Schwab in 2016 to denote the future trend of industrial world [1]. The industry 4.0 principles are built upon the developments of third industrial revolution that already has introduced computerization technology to the world. This paved the way to the most prominent applications, such as automation and artificial intelligence, and in addition to digital transformation of process and business operations along the different industrial revolutions [2]. The first industrial revolution marks the advent of mechanization from steam and waterpower during 1750 and 1850, followed by second revolution bringing in electrical power and mass production systems during 1870 to 1914 and third revolution in the 20th century gave entry of computer and electronics into industrial process after 1970.
Currently, the industry 4.0 began during 2014, where it works on conceptualizing the use of innovative technology and advanced digital production together contributing to a sustainable industrial development [3, 4]. According to research studies by UNIDO, industry 4.0 scope has not made full advancement in all sectors as of yet, in Argentina and Brazil claims that 3% to 4% of firms have adopted few applications, while countries like Ghana, Vietnam and Thailand has very much behind in exploiting the benefits of the fourth revolution.
In this viewpoint, this chapter will deal in describing the role of artificial intelligence technology particularly in spearheading the industry 4.0 concept in the context of manufacturing sector globally. In combination, the chapter will likewise attempt to provide a comprehensive account on the factors that are considered contributing to the employment of industry 4.0 across different organizations.
The purpose of the chapter is to provide an overview and in-depth understanding of the factors that are driving the industry 4.0 in the perspective of artificial intelligence technology. To elaborate the details, published literature and reports from previous studies are referred to meet the objectives of the study, and secondary sources of information by visiting different organizational websites were conducted to examine the factors contributing to the industry 4.0. Thorough searches from previously published work by various researchers was conducted to examine the different driving factors for industry 4.0 and were analyzed. Information related to the industry 4.0 technologies, their roles toward manufacturing sector, statistical information on status global economy with artificial intelligence technology were collected from secondary data published in government information portals and global private organizations like price Waterhouse coopers, McKinsey & Company, Deloitte etc. For presenting the findings from the analysis, the authors have maximized the available information published in researchers and details provide in various organizational websites.
The fourth industrial revolution is fueled from its predecessor industrial phase essentially pushing the trend toward automation and fully integrating the digitalization process in industries, expanding the applicability through some of the significant applications like internet of things (IoT), cyber physical systems, smart factory, cloud computing, cognitive computing, artificial intelligence, etc. Theoretically, the fourth industrial revolution is driven by four key ideologies, enabling interconnection between different technological applications, transparency in information and procurement, technical assistance, and regionalized decisions enabling the industry 4.0 concept a distinctive approach from the third revolution [5].
Artificial intelligence (AI) can be explained as the use of computer programming to mimic human thought and actions to create human-like responses through machine learning, logic, perception, and reasoning. Legendary physicist Stephen Hawking quotes "Success in creating effective AI could be the biggest event in the history of our civilization. Or the worst. We just don't know. So, we cannot know if we will be infinitely helped by AI, or ignored by it and side-lined, or conceivably destroyed by it."
Artificial intelligence (AI) obviously is a significant technology among the other technological applications in the industry 4.0 as it principally works on the foundation of computer learning and augmentation pertinent to the entire industry ecosystem. Most importantly, AI technology has tremendous scope for its application in manufacturing industries in the production and design stages enabling reduction in human errors, production efficiency and reduction in losses. Moreover, the use of AI techniques, such as machine learning and deep learning, have substantial contribution toward advancement of global economy as the global race for embedding AI into businesses have already started reaching milestones in some countries, for example 82% of Spanish companies have explored AI technology in their industrial system.
Following the prospective growth in utilizing AI technology, global projection shows the technology has the potential to contribute $15.7 trillion to the economy by 2030 of which middle east expects to claim 2% of the benefits accounting to $320 billion. AI Technologies in the GCC four countries (Bahrain, Kuwait, Oman, and Qatar) is likely to contribute around 8.2% of GDP by 2030, while in comparison to no middle east economies, the share of AI to the GDP is large, 26.1% in China, and 14.5% in North America. Notwithstanding to say, the prospects of AI technology to industries especially in the developing nations is tremendous as the potential for embedding AI in manufacturing and allied sectors is increasing gradually. In addition, in the UAE, KSA, GCC, and Egypt, the annual growth of AI from 2018 to 2030 is expected to be 33.5%, 31.3% 28.8%, and 25.5%, respectively [6].
In developed nations, the importance of AI technology to business achievement shows more promising growth in the UK from 14 % to 45%, United States 11% to 42%, Germany 4% to 32%, Australia 12% to 29% in the next few years [7]. Regional analysis on the investment scenario on AI in Asian markets shows $0.69 billion in 2019 with the global market expected to reach $9.89 billion by 2027, in the UK, investment on AI stands at $1.3 billion; further, there are growth returns expected from the increasing investment on AI north and south America according to Market research report of Fortune Business Insights [8].
The period from 1950 to 1960 marks the birth and evolution of AI to the world, later in the six decades of its journey, the technology has taken leaps and bounds along with the industrial revolutions [9]. Presently, in the 21st century, AI is certainly the prominent technology for the industry 4.0 and for the future. Global economists also estimate the wide applications AI will be very much instrumental for global expansion in the future, and quite obviously AI will be one of the most preferred technologies to be used by companies by the year 2025 according to the reports by world economic forum [10]. Added to this, COVID-19 pandemic has necessitated remote work and work for home...
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