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 xv1 Artificial Intelligence—The Driving Force of Industry 4.0 1Hesham Magd, Henry Jonathan, Shad Ahmad Khan and Mohamed El Geddawy1.1 Introduction 21.2 Methodology 21.3 Scope of AI in Global Economy and Industry 4.0 31.3.1 Artificial Intelligence—Evolution and Implications 41.3.2 Artificial Intelligence and Industry 4.0—Investments and Returns on Economy 51.3.3 The Driving Forces for Industry 4.0 71.4 Artificial Intelligence—Manufacturing Sector 81.4.1 AI Diversity—Applications to Manufacturing Sector 91.4.2 Future Roadmap of AI—Prospects to Manufacturing Sector in Industry 4.0 121.5 Conclusion 13References 142 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview 17Sachi Pandey, Vijay Laxmi and Rajendra Prasad Mahapatra2.1 Introduction 172.2 Industrial Transformation/Value Chain Transformation 182.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT 192.2.2 Second Scenario: Selling Outcome (User Demand)– Based Services Using IIoT 202.3 IIoT Reference Architecture 202.4 IIoT Technical Concepts 222.5 IIoT and Cloud Computing 262.6 IIoT and Security 27References 293 Artificial Intelligence of Things (AIoT) and Industry 4.0– Based Supply Chain (FMCG Industry) 31Seyyed Esmaeil Najafi, Hamed Nozari and S. A. Edalatpanah3.1 Introduction 323.2 Concepts 333.2.1 Internet of Things 333.2.2 The Industrial Internet of Things (IIoT) 343.2.3 Artificial Intelligence of Things (AIoT) 353.3 AIoT-Based Supply Chain 363.4 Conclusion 40References 404 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 43Alireza Goli, Amir-Mohammad Golmohammadi and S. A. Edalatpanah4.1 Introduction 444.2 Literature Review 454.2.1 Summary of the First Three Industrial Revolutions 454.2.2 Emergence of Industry 4.0 454.2.3 Some of the Challenges of Industry 4.0 474.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting 484.4 Proposed Approach 504.4.1 Mathematical Model 504.4.2 Advantages of the Proposed Model 514.5 Discussion and Conclusion 52References 535 Integrating IoT and Deep Learning—The Driving Force of Industry 4.0 57Muhammad Farrukh Shahid, Tariq Jamil Saifullah Khanzada and Muhammad Hassan Tanveer5.1 Motivation and Background 585.2 Bringing Intelligence Into IoT Devices 605.3 The Foundation of CR-IoT Network 625.3.1 Various AI Technique in CR-IoT Network 635.3.2 Artificial Neural Network (ANN) 635.3.3 Metaheuristic Technique 645.3.4 Rule-Based System 645.3.5 Ontology-Based System 655.3.6 Probabilistic Models 655.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network 655.5 Realization of CR-IoT Network in Daily Life Examples 695.6 AI-Enabled Agriculture and Smart Irrigation System—Case Study 705.7 Conclusion 75References 756 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 Mishra6.1 Introduction 806.2 Overview of Blockchain 836.3 Components of Blockchain 856.3.1 Data Block 856.3.2 Smart Contracts 876.3.3 Consensus Algorithms 876.4 Safety Issues in Blockchain Technology 886.5 Usage of Big Data Framework in Dynamic Supply Chain System 916.6 Machine Learning and Big Data 946.6.1 Overview of Shallow Models 956.6.1.1 Support Vector Machine (SVM) 956.6.1.2 Artificial Neural Network (ANN) 956.6.1.3 K-Nearest Neighbor (KNN) 956.6.1.4 Clustering 966.6.1.5 Decision Tree 966.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems 966.7.1 Replenishment Planning 966.7.2 Optimizing Orders 976.7.3 Arranging and Organizing 976.7.4 Enhanced Demand Structuring 976.7.5 Real-Time Management of the Supply Chain 976.7.6 Enhanced Reaction 986.7.7 Planning and Growth of Inventories 986.8 IoT-Enabled Blockchains 986.8.1 Securing IoT Applications by Utilizing Blockchain 996.8.2 Blockchain Based on Permission 1016.8.3 Blockchain Improvements in IoT 1016.8.3.1 Blockchain Can Store Information Coming from IoT Devices 1016.8.3.2 Secure Data Storage with Blockchain Distribution 1016.8.3.3 Data Encryption via Hash Key and Tested by the Miners 1026.8.3.4 Spoofing Attacks and Data Loss Prevention 1026.8.3.5 Unauthorized Access Prevention Using Blockchain 1036.8.3.6 Exclusion of Centralized Cloud Servers 1036.9 Conclusions 103References 1047 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet 111Yash Gupta, Shilpi Sharma, Naveen Rajan P. and Nadia Mohamed Kunhi7.1 Introduction 1127.2 Related Work 1137.3 Methodology 1147.3.1 Splitting of Data (Test/Train) 1167.3.2 Prophet Model 1167.3.3 Data Cleaning 1197.3.4 Model Implementation 1197.4 Results 1207.4.1 Comparing Forecast to Actuals 1217.4.2 Adding Holidays 1227.4.3 Comparing Forecast to Actuals with the Cleaned Data 1227.5 Conclusion and Future Scope 122References 1258 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems 127Manas Kumar Yogi and A.S.N. Chakravarthy8.1 Introduction 1288.2 Related Work 1318.3 Proposed Mechanism 1338.4 Experimental Results 1358.5 Future Directions 1378.6 Conclusion 138References 1389 Environmental and Industrial Applications Using Internet of Things (IoT) 141Manal Fawzy, Alaa El Din Mahmoud and Ahmed M. Abdelfatah9.1 Introduction 1429.2 IoT-Based Environmental Applications 1469.3 Smart Environmental Monitoring 1479.3.1 Air Quality Assessment 1479.3.2 Water Quality Assessment 1489.3.3 Soil Quality Assessment 1509.3.4 Environmental Health-Related to COVID- 19Monitoring 1509.4 Applications of Sensors Network in Agro-Industrial System 1519.5 Applications of IoT in Industry 1539.5.1 Application of IoT in the Autonomous Field 1539.5.2 Applications of IoT in Software Industries 1559.5.3 Sensors in Industry 1569.6 Challenges of IoT Applications in Environmental and Industrial Applications 1579.7 Conclusions and Recommendations 159Acknowledgments 159References 15910 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 Nath10.1 Introduction 17010.2 Allusion Design of IoT 17210.2.1 Stage 1—Edge Tool 17210.2.2 Stage 2—Connectivity 17210.2.3 Stage 3—Fog Computing 17310.2.4 Stage 4—Data Collection 17310.2.5 Stage 5—Data Abstraction 17310.2.6 Stage 6—Applications 17310.2.7 Stage 7—Cooperation and Processes 17410.3 Vulnerabilities of IoT 17410.3.1 The Properties and Relationships of Various IoT Networks 17410.3.2 Device Attacks 17510.3.3 Attacks on Network 17510.3.4 Some Other Issues 17510.3.4.1 Customer Delivery Value 17510.3.4.2 Compatibility Problems With Equipment 17610.3.4.3 Compatibility and Maintenance 17610.3.4.4 Connectivity Issues in the Field of Data 17610.3.4.5 Incorrect Data Collection and Difficulties 17710.3.4.6 Security Concern 17710.3.4.7 Problems in Computer Confidentiality 17710.4 Challenges in Technology 17810.4.1 Skepticism of Consumers 17810.5 Analysis of IoT Security 17910.5.1 Sensing Layer Security Threats 18010.5.1.1 Node Capturing 18010.5.1.2 Malicious Attack by Code Injection 18010.5.1.3 Attack by Fake Data Injection 18010.5.1.4 Sidelines Assaults 18110.5.1.5 Attacks During Booting Process 18110.5.2 Network Layer Safety Issues 18110.5.2.1 Attack on Phishing Page 18110.5.2.2 Attacks on Access 18210.5.2.3 Attacks on Data Transmission 18210.5.2.4 Attacks on Routing 18210.5.3 Middleware Layer Safety Issues 18210.5.3.1 Attack by SQL Injection 18310.5.3.2 Attack by Signature Wrapping 18310.5.3.3 Cloud Attack Injection with Malware 18310.5.3.4 Cloud Flooding Attack 18310.5.4 Gateways Safety Issues 18410.5.4.1 On-Boarding Safely 18410.5.4.2 Additional Interfaces 18410.5.4.3 Encrypting End-to-End 18410.5.5 Application Layer Safety Issues 18510.5.5.1 Theft of Data 18510.5.5.2 Attacks at Interruption in Service 18510.5.5.3 Malicious Code Injection Attack 18510.6 Improvements and Enhancements Needed for IoT Applications in the Future 18610.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) 18910.8 Conclusion 192References 19311 Potential, Scope, and Challenges of Industry 4.0 201Roshan Raman and Aayush Kumar11.1 Introduction 20211.2 Key Aspects for a Successful Production 20211.3 Opportunities with Industry 4.0 20411.4 Issues in Implementation of Industry 4.0 20611.5 Potential Tools Utilized in Industry 4.0 20711.6 Conclusion 210References 21012 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges 215Roshan Raman and Aditya Ranjan12.1 Introduction 21612.2 Changing Market Demands 21712.2.1 Individualization 21812.2.2 Volatility 21812.2.3 Efficiency in Terms of Energy Resources 21812.3 Recent Technological Advancements 21912.4 Industrial Revolution 4.0 22112.5 Challenges to Industry 4.0 22412.6 Conclusion 225References 22613 The Role of Multiagent System in Industry 4.0 227Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan and Rudra Pratap Ojha13.1 Introduction 22813.2 Characteristics and Goals of Industry 4.0 Conception 22813.3 Artificial Intelligence 23113.3.1 Knowledge-Based Systems 23213.4 Multiagent Systems 23413.4.1 Agent Architectures 23413.4.2 Jade 23813.4.3 System Requirements Definition 23913.4.4 HMI Development 24013.5 Developing Software of Controllers Multiagent Environment Behavior Patterns 24013.5.1 Agent Supervision 24013.5.2 Documents Dispatching Agents 24113.5.3 Agent Rescheduling 24213.5.4 Agent of Executive 24213.5.5 Primary Roles of High-Availability Agent 24313.6 Conclusion 244References 24414 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 Gbolagade14.1 Introduction 24814.2 Reviews of Related Works 25014.3 Materials and Methods 25814.3.1 Multimedia 25814.3.2 Artificial Intelligence and Explainable Artificial Intelligence 26114.3.3 Cryptography 26214.3.4 Encryption and Decryption 26514.3.5 Residue Number System 26614.4 Discussion and Conclusion 268References 26815 Market Trends with Cryptocurrency Trading in Industry 4.0 275Varun Khemka, Sagar Bafna, Ayush Gupta, Somya Goyal and Vivek Kumar Verma15.1 Introduction 27615.2 Industry Overview 27615.2.1 History (From Barter to Cryptocurrency) 27615.2.2 In the Beginning Was Bitcoin 27815.3 Cryptocurrency Market 27915.3.1 Blockchain 27915.3.1.1 Introduction to Blockchain Technology 27915.3.1.2 Mining 28015.3.1.3 From Blockchain to Cryptocurrency 28115.3.2 Introduction to Cryptocurrency Market 28115.3.2.1 What is a Cryptocurrency? 28115.3.2.2 Cryptocurrency Exchanges 28315.4 Cryptocurrency Trading 28315.4.1 Definition 28315.4.2 Advantages 28315.4.3 Disadvantages 28415.5 In-Depth Analysis of Fee Structures and Carbon Footprint in Blockchain 28515.5.1 Need for a Fee-Driven System 28515.5.2 Ethereum Structure 28615.5.3 How is the Gas Fee Calculated? 28715.5.3.1 Why are Ethereum Gas Prices so High? 28715.5.3.2 Carbon Neutrality 28715.6 Conclusion 291References 29216 Blockchain and Its Applications in Industry 4.0 295Ajay Sudhir Bale, Tarun Praveen Purohit, Muhammed Furqaan Hashim and Suyog Navale16.1 Introduction 29616.2 About Cryptocurrency 29616.3 History of Blockchain and Cryptocurrency 29816.4 Background of Industrial Revolution 30016.4.1 The First Industrial Revolution 30116.4.2 The Second Industrial Revolution 30116.4.3 The Third Industrial Revolution 30216.4.4 The Fourth Industrial Revolution 30216.5 Trends of Blockchain 30316.6 Applications of Blockchain in Industry 4.0 30416.6.1 Blockchain and the Government 30416.6.2 Blockchain in the Healthcare Sector 30416.6.3 Blockchain in Logistics and Supply Chain 30616.6.4 Blockchain in the Automotive Sector 30716.6.5 Blockchain in the Education Sector 30816.7 Conclusion 309References 310Index 315

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 xv1 Artificial Intelligence—The Driving Force of Industry 4.0 1Hesham Magd, Henry Jonathan, Shad Ahmad Khan and Mohamed El Geddawy1.1 Introduction 21.2 Methodology 21.3 Scope of AI in Global Economy and Industry 4.0 31.3.1 Artificial Intelligence—Evolution and Implications 41.3.2 Artificial Intelligence and Industry 4.0—Investments and Returns on Economy 51.3.3 The Driving Forces for Industry 4.0 71.4 Artificial Intelligence—Manufacturing Sector 81.4.1 AI Diversity—Applications to Manufacturing Sector 91.4.2 Future Roadmap of AI—Prospects to Manufacturing Sector in Industry 4.0 121.5 Conclusion 13References 142 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview 17Sachi Pandey, Vijay Laxmi and Rajendra Prasad Mahapatra2.1 Introduction 172.2 Industrial Transformation/Value Chain Transformation 182.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT 192.2.2 Second Scenario: Selling Outcome (User Demand)– Based Services Using IIoT 202.3 IIoT Reference Architecture 202.4 IIoT Technical Concepts 222.5 IIoT and Cloud Computing 262.6 IIoT and Security 27References 293 Artificial Intelligence of Things (AIoT) and Industry 4.0– Based Supply Chain (FMCG Industry) 31Seyyed Esmaeil Najafi, Hamed Nozari and S. A. Edalatpanah3.1 Introduction 323.2 Concepts 333.2.1 Internet of Things 333.2.2 The Industrial Internet of Things (IIoT) 343.2.3 Artificial Intelligence of Things (AIoT) 353.3 AIoT-Based Supply Chain 363.4 Conclusion 40References 404 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 43Alireza Goli, Amir-Mohammad Golmohammadi and S. A. Edalatpanah4.1 Introduction 444.2 Literature Review 454.2.1 Summary of the First Three Industrial Revolutions 454.2.2 Emergence of Industry 4.0 454.2.3 Some of the Challenges of Industry 4.0 474.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting 484.4 Proposed Approach 504.4.1 Mathematical Model 504.4.2 Advantages of the Proposed Model 514.5 Discussion and Conclusion 52References 535 Integrating IoT and Deep Learning—The Driving Force of Industry 4.0 57Muhammad Farrukh Shahid, Tariq Jamil Saifullah Khanzada and Muhammad Hassan Tanveer5.1 Motivation and Background 585.2 Bringing Intelligence Into IoT Devices 605.3 The Foundation of CR-IoT Network 625.3.1 Various AI Technique in CR-IoT Network 635.3.2 Artificial Neural Network (ANN) 635.3.3 Metaheuristic Technique 645.3.4 Rule-Based System 645.3.5 Ontology-Based System 655.3.6 Probabilistic Models 655.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network 655.5 Realization of CR-IoT Network in Daily Life Examples 695.6 AI-Enabled Agriculture and Smart Irrigation System—Case Study 705.7 Conclusion 75References 756 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 Mishra6.1 Introduction 806.2 Overview of Blockchain 836.3 Components of Blockchain 856.3.1 Data Block 856.3.2 Smart Contracts 876.3.3 Consensus Algorithms 876.4 Safety Issues in Blockchain Technology 886.5 Usage of Big Data Framework in Dynamic Supply Chain System 916.6 Machine Learning and Big Data 946.6.1 Overview of Shallow Models 956.6.1.1 Support Vector Machine (SVM) 956.6.1.2 Artificial Neural Network (ANN) 956.6.1.3 K-Nearest Neighbor (KNN) 956.6.1.4 Clustering 966.6.1.5 Decision Tree 966.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems 966.7.1 Replenishment Planning 966.7.2 Optimizing Orders 976.7.3 Arranging and Organizing 976.7.4 Enhanced Demand Structuring 976.7.5 Real-Time Management of the Supply Chain 976.7.6 Enhanced Reaction 986.7.7 Planning and Growth of Inventories 986.8 IoT-Enabled Blockchains 986.8.1 Securing IoT Applications by Utilizing Blockchain 996.8.2 Blockchain Based on Permission 1016.8.3 Blockchain Improvements in IoT 1016.8.3.1 Blockchain Can Store Information Coming from IoT Devices 1016.8.3.2 Secure Data Storage with Blockchain Distribution 1016.8.3.3 Data Encryption via Hash Key and Tested by the Miners 1026.8.3.4 Spoofing Attacks and Data Loss Prevention 1026.8.3.5 Unauthorized Access Prevention Using Blockchain 1036.8.3.6 Exclusion of Centralized Cloud Servers 1036.9 Conclusions 103References 1047 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet 111Yash Gupta, Shilpi Sharma, Naveen Rajan P. and Nadia Mohamed Kunhi7.1 Introduction 1127.2 Related Work 1137.3 Methodology 1147.3.1 Splitting of Data (Test/Train) 1167.3.2 Prophet Model 1167.3.3 Data Cleaning 1197.3.4 Model Implementation 1197.4 Results 1207.4.1 Comparing Forecast to Actuals 1217.4.2 Adding Holidays 1227.4.3 Comparing Forecast to Actuals with the Cleaned Data 1227.5 Conclusion and Future Scope 122References 1258 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems 127Manas Kumar Yogi and A.S.N. Chakravarthy8.1 Introduction 1288.2 Related Work 1318.3 Proposed Mechanism 1338.4 Experimental Results 1358.5 Future Directions 1378.6 Conclusion 138References 1389 Environmental and Industrial Applications Using Internet of Things (IoT) 141Manal Fawzy, Alaa El Din Mahmoud and Ahmed M. Abdelfatah9.1 Introduction 1429.2 IoT-Based Environmental Applications 1469.3 Smart Environmental Monitoring 1479.3.1 Air Quality Assessment 1479.3.2 Water Quality Assessment 1489.3.3 Soil Quality Assessment 1509.3.4 Environmental Health-Related to COVID- 19Monitoring 1509.4 Applications of Sensors Network in Agro-Industrial System 1519.5 Applications of IoT in Industry 1539.5.1 Application of IoT in the Autonomous Field 1539.5.2 Applications of IoT in Software Industries 1559.5.3 Sensors in Industry 1569.6 Challenges of IoT Applications in Environmental and Industrial Applications 1579.7 Conclusions and Recommendations 159Acknowledgments 159References 15910 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 Nath10.1 Introduction 17010.2 Allusion Design of IoT 17210.2.1 Stage 1—Edge Tool 17210.2.2 Stage 2—Connectivity 17210.2.3 Stage 3—Fog Computing 17310.2.4 Stage 4—Data Collection 17310.2.5 Stage 5—Data Abstraction 17310.2.6 Stage 6—Applications 17310.2.7 Stage 7—Cooperation and Processes 17410.3 Vulnerabilities of IoT 17410.3.1 The Properties and Relationships of Various IoT Networks 17410.3.2 Device Attacks 17510.3.3 Attacks on Network 17510.3.4 Some Other Issues 17510.3.4.1 Customer Delivery Value 17510.3.4.2 Compatibility Problems With Equipment 17610.3.4.3 Compatibility and Maintenance 17610.3.4.4 Connectivity Issues in the Field of Data 17610.3.4.5 Incorrect Data Collection and Difficulties 17710.3.4.6 Security Concern 17710.3.4.7 Problems in Computer Confidentiality 17710.4 Challenges in Technology 17810.4.1 Skepticism of Consumers 17810.5 Analysis of IoT Security 17910.5.1 Sensing Layer Security Threats 18010.5.1.1 Node Capturing 18010.5.1.2 Malicious Attack by Code Injection 18010.5.1.3 Attack by Fake Data Injection 18010.5.1.4 Sidelines Assaults 18110.5.1.5 Attacks During Booting Process 18110.5.2 Network Layer Safety Issues 18110.5.2.1 Attack on Phishing Page 18110.5.2.2 Attacks on Access 18210.5.2.3 Attacks on Data Transmission 18210.5.2.4 Attacks on Routing 18210.5.3 Middleware Layer Safety Issues 18210.5.3.1 Attack by SQL Injection 18310.5.3.2 Attack by Signature Wrapping 18310.5.3.3 Cloud Attack Injection with Malware 18310.5.3.4 Cloud Flooding Attack 18310.5.4 Gateways Safety Issues 18410.5.4.1 On-Boarding Safely 18410.5.4.2 Additional Interfaces 18410.5.4.3 Encrypting End-to-End 18410.5.5 Application Layer Safety Issues 18510.5.5.1 Theft of Data 18510.5.5.2 Attacks at Interruption in Service 18510.5.5.3 Malicious Code Injection Attack 18510.6 Improvements and Enhancements Needed for IoT Applications in the Future 18610.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) 18910.8 Conclusion 192References 19311 Potential, Scope, and Challenges of Industry 4.0 201Roshan Raman and Aayush Kumar11.1 Introduction 20211.2 Key Aspects for a Successful Production 20211.3 Opportunities with Industry 4.0 20411.4 Issues in Implementation of Industry 4.0 20611.5 Potential Tools Utilized in Industry 4.0 20711.6 Conclusion 210References 21012 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges 215Roshan Raman and Aditya Ranjan12.1 Introduction 21612.2 Changing Market Demands 21712.2.1 Individualization 21812.2.2 Volatility 21812.2.3 Efficiency in Terms of Energy Resources 21812.3 Recent Technological Advancements 21912.4 Industrial Revolution 4.0 22112.5 Challenges to Industry 4.0 22412.6 Conclusion 225References 22613 The Role of Multiagent System in Industry 4.0 227Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan and Rudra Pratap Ojha13.1 Introduction 22813.2 Characteristics and Goals of Industry 4.0 Conception 22813.3 Artificial Intelligence 23113.3.1 Knowledge-Based Systems 23213.4 Multiagent Systems 23413.4.1 Agent Architectures 23413.4.2 Jade 23813.4.3 System Requirements Definition 23913.4.4 HMI Development 24013.5 Developing Software of Controllers Multiagent Environment Behavior Patterns 24013.5.1 Agent Supervision 24013.5.2 Documents Dispatching Agents 24113.5.3 Agent Rescheduling 24213.5.4 Agent of Executive 24213.5.5 Primary Roles of High-Availability Agent 24313.6 Conclusion 244References 24414 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 Gbolagade14.1 Introduction 24814.2 Reviews of Related Works 25014.3 Materials and Methods 25814.3.1 Multimedia 25814.3.2 Artificial Intelligence and Explainable Artificial Intelligence 26114.3.3 Cryptography 26214.3.4 Encryption and Decryption 26514.3.5 Residue Number System 26614.4 Discussion and Conclusion 268References 26815 Market Trends with Cryptocurrency Trading in Industry 4.0 275Varun Khemka, Sagar Bafna, Ayush Gupta, Somya Goyal and Vivek Kumar Verma15.1 Introduction 27615.2 Industry Overview 27615.2.1 History (From Barter to Cryptocurrency) 27615.2.2 In the Beginning Was Bitcoin 27815.3 Cryptocurrency Market 27915.3.1 Blockchain 27915.3.1.1 Introduction to Blockchain Technology 27915.3.1.2 Mining 28015.3.1.3 From Blockchain to Cryptocurrency 28115.3.2 Introduction to Cryptocurrency Market 28115.3.2.1 What is a Cryptocurrency? 28115.3.2.2 Cryptocurrency Exchanges 28315.4 Cryptocurrency Trading 28315.4.1 Definition 28315.4.2 Advantages 28315.4.3 Disadvantages 28415.5 In-Depth Analysis of Fee Structures and Carbon Footprint in Blockchain 28515.5.1 Need for a Fee-Driven System 28515.5.2 Ethereum Structure 28615.5.3 How is the Gas Fee Calculated? 28715.5.3.1 Why are Ethereum Gas Prices so High? 28715.5.3.2 Carbon Neutrality 28715.6 Conclusion 291References 29216 Blockchain and Its Applications in Industry 4.0 295Ajay Sudhir Bale, Tarun Praveen Purohit, Muhammed Furqaan Hashim and Suyog Navale16.1 Introduction 29616.2 About Cryptocurrency 29616.3 History of Blockchain and Cryptocurrency 29816.4 Background of Industrial Revolution 30016.4.1 The First Industrial Revolution 30116.4.2 The Second Industrial Revolution 30116.4.3 The Third Industrial Revolution 30216.4.4 The Fourth Industrial Revolution 30216.5 Trends of Blockchain 30316.6 Applications of Blockchain in Industry 4.0 30416.6.1 Blockchain and the Government 30416.6.2 Blockchain in the Healthcare Sector 30416.6.3 Blockchain in Logistics and Supply Chain 30616.6.4 Blockchain in the Automotive Sector 30716.6.5 Blockchain in the Education Sector 30816.7 Conclusion 309References 310Index 315

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 xv1 Artificial Intelligence—The Driving Force of Industry 4.0 1Hesham Magd, Henry Jonathan, Shad Ahmad Khan and Mohamed El Geddawy1.1 Introduction 21.2 Methodology 21.3 Scope of AI in Global Economy and Industry 4.0 31.3.1 Artificial Intelligence—Evolution and Implications 41.3.2 Artificial Intelligence and Industry 4.0—Investments and Returns on Economy 51.3.3 The Driving Forces for Industry 4.0 71.4 Artificial Intelligence—Manufacturing Sector 81.4.1 AI Diversity—Applications to Manufacturing Sector 91.4.2 Future Roadmap of AI—Prospects to Manufacturing Sector in Industry 4.0 121.5 Conclusion 13References 142 Industry 4.0, Intelligent Manufacturing, Internet of Things, Cloud Computing: An Overview 17Sachi Pandey, Vijay Laxmi and Rajendra Prasad Mahapatra2.1 Introduction 172.2 Industrial Transformation/Value Chain Transformation 182.2.1 First Scenario: Reducing Waste and Increasing Productivity Using IIoT 192.2.2 Second Scenario: Selling Outcome (User Demand)– Based Services Using IIoT 202.3 IIoT Reference Architecture 202.4 IIoT Technical Concepts 222.5 IIoT and Cloud Computing 262.6 IIoT and Security 27References 293 Artificial Intelligence of Things (AIoT) and Industry 4.0– Based Supply Chain (FMCG Industry) 31Seyyed Esmaeil Najafi, Hamed Nozari and S. A. Edalatpanah3.1 Introduction 323.2 Concepts 333.2.1 Internet of Things 333.2.2 The Industrial Internet of Things (IIoT) 343.2.3 Artificial Intelligence of Things (AIoT) 353.3 AIoT-Based Supply Chain 363.4 Conclusion 40References 404 Application of Artificial Intelligence in Forecasting the Demand for Supply Chains Considering Industry 4.0 43Alireza Goli, Amir-Mohammad Golmohammadi and S. A. Edalatpanah4.1 Introduction 444.2 Literature Review 454.2.1 Summary of the First Three Industrial Revolutions 454.2.2 Emergence of Industry 4.0 454.2.3 Some of the Challenges of Industry 4.0 474.3 Application of Artificial Intelligence in Supply Chain Demand Forecasting 484.4 Proposed Approach 504.4.1 Mathematical Model 504.4.2 Advantages of the Proposed Model 514.5 Discussion and Conclusion 52References 535 Integrating IoT and Deep Learning—The Driving Force of Industry 4.0 57Muhammad Farrukh Shahid, Tariq Jamil Saifullah Khanzada and Muhammad Hassan Tanveer5.1 Motivation and Background 585.2 Bringing Intelligence Into IoT Devices 605.3 The Foundation of CR-IoT Network 625.3.1 Various AI Technique in CR-IoT Network 635.3.2 Artificial Neural Network (ANN) 635.3.3 Metaheuristic Technique 645.3.4 Rule-Based System 645.3.5 Ontology-Based System 655.3.6 Probabilistic Models 655.4 The Principles of Deep Learning and Its Implementation in CR-IoT Network 655.5 Realization of CR-IoT Network in Daily Life Examples 695.6 AI-Enabled Agriculture and Smart Irrigation System—Case Study 705.7 Conclusion 75References 756 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 Mishra6.1 Introduction 806.2 Overview of Blockchain 836.3 Components of Blockchain 856.3.1 Data Block 856.3.2 Smart Contracts 876.3.3 Consensus Algorithms 876.4 Safety Issues in Blockchain Technology 886.5 Usage of Big Data Framework in Dynamic Supply Chain System 916.6 Machine Learning and Big Data 946.6.1 Overview of Shallow Models 956.6.1.1 Support Vector Machine (SVM) 956.6.1.2 Artificial Neural Network (ANN) 956.6.1.3 K-Nearest Neighbor (KNN) 956.6.1.4 Clustering 966.6.1.5 Decision Tree 966.7 Advantages of Using Big Data for Supply Chain and Blockchain Systems 966.7.1 Replenishment Planning 966.7.2 Optimizing Orders 976.7.3 Arranging and Organizing 976.7.4 Enhanced Demand Structuring 976.7.5 Real-Time Management of the Supply Chain 976.7.6 Enhanced Reaction 986.7.7 Planning and Growth of Inventories 986.8 IoT-Enabled Blockchains 986.8.1 Securing IoT Applications by Utilizing Blockchain 996.8.2 Blockchain Based on Permission 1016.8.3 Blockchain Improvements in IoT 1016.8.3.1 Blockchain Can Store Information Coming from IoT Devices 1016.8.3.2 Secure Data Storage with Blockchain Distribution 1016.8.3.3 Data Encryption via Hash Key and Tested by the Miners 1026.8.3.4 Spoofing Attacks and Data Loss Prevention 1026.8.3.5 Unauthorized Access Prevention Using Blockchain 1036.8.3.6 Exclusion of Centralized Cloud Servers 1036.9 Conclusions 103References 1047 Deep Learning Approach to Industrial Energy Sector and Energy Forecasting with Prophet 111Yash Gupta, Shilpi Sharma, Naveen Rajan P. and Nadia Mohamed Kunhi7.1 Introduction 1127.2 Related Work 1137.3 Methodology 1147.3.1 Splitting of Data (Test/Train) 1167.3.2 Prophet Model 1167.3.3 Data Cleaning 1197.3.4 Model Implementation 1197.4 Results 1207.4.1 Comparing Forecast to Actuals 1217.4.2 Adding Holidays 1227.4.3 Comparing Forecast to Actuals with the Cleaned Data 1227.5 Conclusion and Future Scope 122References 1258 Application of Novel AI Mechanism for Minimizing Private Data Release in Cyber-Physical Systems 127Manas Kumar Yogi and A.S.N. Chakravarthy8.1 Introduction 1288.2 Related Work 1318.3 Proposed Mechanism 1338.4 Experimental Results 1358.5 Future Directions 1378.6 Conclusion 138References 1389 Environmental and Industrial Applications Using Internet of Things (IoT) 141Manal Fawzy, Alaa El Din Mahmoud and Ahmed M. Abdelfatah9.1 Introduction 1429.2 IoT-Based Environmental Applications 1469.3 Smart Environmental Monitoring 1479.3.1 Air Quality Assessment 1479.3.2 Water Quality Assessment 1489.3.3 Soil Quality Assessment 1509.3.4 Environmental Health-Related to COVID- 19Monitoring 1509.4 Applications of Sensors Network in Agro-Industrial System 1519.5 Applications of IoT in Industry 1539.5.1 Application of IoT in the Autonomous Field 1539.5.2 Applications of IoT in Software Industries 1559.5.3 Sensors in Industry 1569.6 Challenges of IoT Applications in Environmental and Industrial Applications 1579.7 Conclusions and Recommendations 159Acknowledgments 159References 15910 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 Nath10.1 Introduction 17010.2 Allusion Design of IoT 17210.2.1 Stage 1—Edge Tool 17210.2.2 Stage 2—Connectivity 17210.2.3 Stage 3—Fog Computing 17310.2.4 Stage 4—Data Collection 17310.2.5 Stage 5—Data Abstraction 17310.2.6 Stage 6—Applications 17310.2.7 Stage 7—Cooperation and Processes 17410.3 Vulnerabilities of IoT 17410.3.1 The Properties and Relationships of Various IoT Networks 17410.3.2 Device Attacks 17510.3.3 Attacks on Network 17510.3.4 Some Other Issues 17510.3.4.1 Customer Delivery Value 17510.3.4.2 Compatibility Problems With Equipment 17610.3.4.3 Compatibility and Maintenance 17610.3.4.4 Connectivity Issues in the Field of Data 17610.3.4.5 Incorrect Data Collection and Difficulties 17710.3.4.6 Security Concern 17710.3.4.7 Problems in Computer Confidentiality 17710.4 Challenges in Technology 17810.4.1 Skepticism of Consumers 17810.5 Analysis of IoT Security 17910.5.1 Sensing Layer Security Threats 18010.5.1.1 Node Capturing 18010.5.1.2 Malicious Attack by Code Injection 18010.5.1.3 Attack by Fake Data Injection 18010.5.1.4 Sidelines Assaults 18110.5.1.5 Attacks During Booting Process 18110.5.2 Network Layer Safety Issues 18110.5.2.1 Attack on Phishing Page 18110.5.2.2 Attacks on Access 18210.5.2.3 Attacks on Data Transmission 18210.5.2.4 Attacks on Routing 18210.5.3 Middleware Layer Safety Issues 18210.5.3.1 Attack by SQL Injection 18310.5.3.2 Attack by Signature Wrapping 18310.5.3.3 Cloud Attack Injection with Malware 18310.5.3.4 Cloud Flooding Attack 18310.5.4 Gateways Safety Issues 18410.5.4.1 On-Boarding Safely 18410.5.4.2 Additional Interfaces 18410.5.4.3 Encrypting End-to-End 18410.5.5 Application Layer Safety Issues 18510.5.5.1 Theft of Data 18510.5.5.2 Attacks at Interruption in Service 18510.5.5.3 Malicious Code Injection Attack 18510.6 Improvements and Enhancements Needed for IoT Applications in the Future 18610.7 Upcoming Future Research Challenges with Intrusion Detection Systems (IDS) 18910.8 Conclusion 192References 19311 Potential, Scope, and Challenges of Industry 4.0 201Roshan Raman and Aayush Kumar11.1 Introduction 20211.2 Key Aspects for a Successful Production 20211.3 Opportunities with Industry 4.0 20411.4 Issues in Implementation of Industry 4.0 20611.5 Potential Tools Utilized in Industry 4.0 20711.6 Conclusion 210References 21012 Industry 4.0 and Manufacturing Techniques: Opportunities and Challenges 215Roshan Raman and Aditya Ranjan12.1 Introduction 21612.2 Changing Market Demands 21712.2.1 Individualization 21812.2.2 Volatility 21812.2.3 Efficiency in Terms of Energy Resources 21812.3 Recent Technological Advancements 21912.4 Industrial Revolution 4.0 22112.5 Challenges to Industry 4.0 22412.6 Conclusion 225References 22613 The Role of Multiagent System in Industry 4.0 227Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan and Rudra Pratap Ojha13.1 Introduction 22813.2 Characteristics and Goals of Industry 4.0 Conception 22813.3 Artificial Intelligence 23113.3.1 Knowledge-Based Systems 23213.4 Multiagent Systems 23413.4.1 Agent Architectures 23413.4.2 Jade 23813.4.3 System Requirements Definition 23913.4.4 HMI Development 24013.5 Developing Software of Controllers Multiagent Environment Behavior Patterns 24013.5.1 Agent Supervision 24013.5.2 Documents Dispatching Agents 24113.5.3 Agent Rescheduling 24213.5.4 Agent of Executive 24213.5.5 Primary Roles of High-Availability Agent 24313.6 Conclusion 244References 24414 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 Gbolagade14.1 Introduction 24814.2 Reviews of Related Works 25014.3 Materials and Methods 25814.3.1 Multimedia 25814.3.2 Artificial Intelligence and Explainable Artificial Intelligence 26114.3.3 Cryptography 26214.3.4 Encryption and Decryption 26514.3.5 Residue Number System 26614.4 Discussion and Conclusion 268References 26815 Market Trends with Cryptocurrency Trading in Industry 4.0 275Varun Khemka, Sagar Bafna, Ayush Gupta, Somya Goyal and Vivek Kumar Verma15.1 Introduction 27615.2 Industry Overview 27615.2.1 History (From Barter to Cryptocurrency) 27615.2.2 In the Beginning Was Bitcoin 27815.3 Cryptocurrency Market 27915.3.1 Blockchain 27915.3.1.1 Introduction to Blockchain Technology 27915.3.1.2 Mining 28015.3.1.3 From Blockchain to Cryptocurrency 28115.3.2 Introduction to Cryptocurrency Market 28115.3.2.1 What is a Cryptocurrency? 28115.3.2.2 Cryptocurrency Exchanges 28315.4 Cryptocurrency Trading 28315.4.1 Definition 28315.4.2 Advantages 28315.4.3 Disadvantages 28415.5 In-Depth Analysis of Fee Structures and Carbon Footprint in Blockchain 28515.5.1 Need for a Fee-Driven System 28515.5.2 Ethereum Structure 28615.5.3 How is the Gas Fee Calculated? 28715.5.3.1 Why are Ethereum Gas Prices so High? 28715.5.3.2 Carbon Neutrality 28715.6 Conclusion 291References 29216 Blockchain and Its Applications in Industry 4.0 295Ajay Sudhir Bale, Tarun Praveen Purohit, Muhammed Furqaan Hashim and Suyog Navale16.1 Introduction 29616.2 About Cryptocurrency 29616.3 History of Blockchain and Cryptocurrency 29816.4 Background of Industrial Revolution 30016.4.1 The First Industrial Revolution 30116.4.2 The Second Industrial Revolution 30116.4.3 The Third Industrial Revolution 30216.4.4 The Fourth Industrial Revolution 30216.5 Trends of Blockchain 30316.6 Applications of Blockchain in Industry 4.0 30416.6.1 Blockchain and the Government 30416.6.2 Blockchain in the Healthcare Sector 30416.6.3 Blockchain in Logistics and Supply Chain 30616.6.4 Blockchain in the Automotive Sector 30716.6.5 Blockchain in the Education Sector 30816.7 Conclusion 309References 310Index 315