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Table of Contents
Cover
Half Title
Title Page
Copyright Page
Table of Contents
Acknowledgments
Editors
Contributors
Chapter 1 Analytics Approach for Intelligent Cyber-Physical System Integration in Industrial Internet of Things (Industry 4.0)
1.1 Introduction
1.1.1 Cyber-Physical System (CPS)
1.1.2 Industrial Internet of Things (IIoT)/Industry 4.0
1.1.2.1 Advantages of Industry 4.0 Technology
1.1.2.2 Challenges of Industry 4.0
1.1.3 Research Motivation
1.2 Intelligent Cyber-Physical System Design Consideration
1.2.1 Fault Tolerance and Reliability
1.2.2 Scalability æ (S)
1.2.3 Hardware System Design
1.2.4 Network Topology
1.2.5 Data Model Design
1.2.6 Operating Environment
1.2.7 Communication and Transmission Technology
1.2.8 Energy Consumption
1.2.9 Routing Protocols
1.2.10 Security Challenges
1.3 The Industrial Internet of Things Architecture Design and Industry 4.0
1.4 The Communication Model of ICPS in Industry 4.0
1.5 Simulation Results of Cyber-Physical Communication Model in IIoT Environment
1.6 Conclusion
References
Chapter 2 Digital Twins A State of the Art from Industry 4.0 Perspective
2.1 Introduction
2.2 Recent Studies on Digital Twin
2.3 Components of Digital Twin
2.4 Applications of Digital Twin
2.4.1 Smart Manufacturing
2.4.2 Supply Chain Management
2.4.3 Sustainability
2.4.4 Intelligent Risk Management
2.5 Challenges and Implementation Issues
2.6 Future Research Avenues
2.7 Conclusion
References
Chapter 3 Human-Centered Approach to Intelligent Analytics in Industry 4.0
3.1 Introduction
3.2 Related Work
3.3 Research Questions
3.4 Research Solutions
3.5 Discussion and Recommendations
3.6 Conclusion and Future Work
Chapter 6 Assessment of Fuzzy Logic Assessed Recommender System A Critical Critique
6.1 Introduction
6.2 Recommendation System Approaches
6.2.1 Collaborative Filtering Method
6.2.1.1 Cold-Start Problem
6.2.2 Content-Based Methods
6.3 Various Recommendation System Based on E-Commerce
6.3.1 Personalized Recommendation System of E-Commerce Based on Big Data Analysis
6.3.2 Hybrid Ontology-Dependent RS for E-Commerce
6.3.3 Trust-Dependent CF Algorithm for E-Commerce RS
6.3.4 Enhanced Slope One Algorithm
6.3.5 Similarity Measures
6.4 Conclusions
References
Half Title
Title Page
Copyright Page
Table of Contents
Acknowledgments
Editors
Contributors
Chapter 1 Analytics Approach for Intelligent Cyber-Physical System Integration in Industrial Internet of Things (Industry 4.0)
1.1 Introduction
1.1.1 Cyber-Physical System (CPS)
1.1.2 Industrial Internet of Things (IIoT)/Industry 4.0
1.1.2.1 Advantages of Industry 4.0 Technology
1.1.2.2 Challenges of Industry 4.0
1.1.3 Research Motivation
1.2 Intelligent Cyber-Physical System Design Consideration
1.2.1 Fault Tolerance and Reliability
1.2.2 Scalability æ (S)
1.2.3 Hardware System Design
1.2.4 Network Topology
1.2.5 Data Model Design
1.2.6 Operating Environment
1.2.7 Communication and Transmission Technology
1.2.8 Energy Consumption
1.2.9 Routing Protocols
1.2.10 Security Challenges
1.3 The Industrial Internet of Things Architecture Design and Industry 4.0
1.4 The Communication Model of ICPS in Industry 4.0
1.5 Simulation Results of Cyber-Physical Communication Model in IIoT Environment
1.6 Conclusion
References
Chapter 2 Digital Twins A State of the Art from Industry 4.0 Perspective
2.1 Introduction
2.2 Recent Studies on Digital Twin
2.3 Components of Digital Twin
2.4 Applications of Digital Twin
2.4.1 Smart Manufacturing
2.4.2 Supply Chain Management
2.4.3 Sustainability
2.4.4 Intelligent Risk Management
2.5 Challenges and Implementation Issues
2.6 Future Research Avenues
2.7 Conclusion
References
Chapter 3 Human-Centered Approach to Intelligent Analytics in Industry 4.0
3.1 Introduction
3.2 Related Work
3.3 Research Questions
3.4 Research Solutions
3.5 Discussion and Recommendations
3.6 Conclusion and Future Work
Chapter 6 Assessment of Fuzzy Logic Assessed Recommender System A Critical Critique
6.1 Introduction
6.2 Recommendation System Approaches
6.2.1 Collaborative Filtering Method
6.2.1.1 Cold-Start Problem
6.2.2 Content-Based Methods
6.3 Various Recommendation System Based on E-Commerce
6.3.1 Personalized Recommendation System of E-Commerce Based on Big Data Analysis
6.3.2 Hybrid Ontology-Dependent RS for E-Commerce
6.3.3 Trust-Dependent CF Algorithm for E-Commerce RS
6.3.4 Enhanced Slope One Algorithm
6.3.5 Similarity Measures
6.4 Conclusions
References