Cyber-Physical, IoT, and Autonomous Systems in Industry 4.0
- Length: 416 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-11-22
- ISBN-10: 036770515X
- ISBN-13: 9780367705152
- Sales Rank: #0 (See Top 100 Books)
This book addresses topics related to Internet of Things (IoT), Machine Learning, Cyber-Physical Systems, Cloud Computing and Autonomous Vehicles for Industry 4.0. It investigates challenges across multiple sectors, industries and considers Industry 4.0 for operations research and supply chain management.
Cyber-Physical, IoT, and Autonomous Systems in Industry 4.0 encourages readers to develop novel theories and enrich their knowledge to foster sustainability. It examines the recent research trends and the future of Cyber-Physical systems, IoT, and Autonomous Systems as it relates to Industry 4.0.
This book is intended for undergraduates, postgraduates, academics, researchers, and industry individuals to explore new ideas, techniques, and tools related to Industry 4.0.
Cover Half Title Title Page Copyright Page Table of Contents Preface Edirors Contributors Chapter 1: Cyber Systems and Security in Industry 4.0 1.1 Introduction to Cyberspace, Cyber Systems, and Cyber Security 1.1.1 Cyber Threats, Vulnerabilities, and Risks 1.2 The Internet of Things and Security Issues 1.2.1 IoT Architecture and Protocol Stacks 1.2.2 Applications of the IoT 1.2.3 Security of IoT Systems 1.2.4 Securing IoT Devices and Networks 1.3 Industrial Internet of Things (IIoT) 1.3.1 Industry 4.0: Overview 1.3.2 IIoT Applications 1.3.3 IIoT Architecture 1.3.4 Impact of IIoT on the Economy 1.4 Security Challenges in Industry 4.0 1.4.1 How Secure Is IIoT? 1.4.2 IIoT Security Challenges 1.4.2.1 Device Hijacking 1.4.2.2 Data Siphoning 1.4.2.3 Denial of Service Attacks 1.4.2.4 Data Breaches 1.4.2.5 Device Theft 1.4.2.6 Man-in-the-Middle or Device “Spoofing” 1.4.3 IIoT Security Solutions 1.4.3.1 Endpoint Security-by-Design 1.5 Conclusions and Future Work 1.5.1 Conclusions 1.5.2 Limitations 1.5.3 Future Work References Chapter 2: A Demand Response Program for Social Welfare Maximization in the Context of the Indian Smart Grid: A Review 2.1 Introduction 2.2 A Smart Grid for India: A Dire Need 2.3 Progress of the Indian Smart Grid 2.3.1 SG Initiatives in India 2.3.2 Indian Governmental Energy Organization 2.3.3 Smart Grid Projects and Their Experience in India 2.4 DRP-based Social Welfare Maximization Model 2.5 Literature Survey, Research Findings, and the Framework of SWMM 2.6 Challenges and Discussion 2.7 Conclusion and Future Scope References Chapter 3: Cloud Computing Security Framework Based on Shared Responsibility Models: Cloud Computing 3.1 Introduction 3.1.1 Cloud Computing Reference Models 3.1.2 Cloud Computing Architecture 3.2 Related Work 3.2.1 Cloud Computing SRM for IaaS, PaaS, and SaaS 3.2.2 Analysis of SRM Implementation by Leading Cloud Service Providers 3.3 Proposed Cloud Computing Security Framework Based on an SRM and its Application 3.4 Results and Conclusion 3.5 Future Work References Chapter 4: Performance Analysis of a Hypervisor and the Container-based Migration Technique for Cloud Virtualization 4.1 Introduction 4.2 Background and Motivation 4.2.1 Need for Virtualization in Cloud Data Centers 4.2.2 Evolution of Cloud Virtualization from Hypervisor to Containerization 4.3 Related Work 4.4 LXD/CR: A Container Migration Technique 4.5 Experimental Setup 4.6 Performance Evaluation of LXD/CR 4.6.1 Downtime and Migration Time 4.6.2 Number of Pages Transferred 4.7 Open Research Issues and Challenges 4.8 Conclusion and Future Scope Bibliography Chapter 5: Segmentation of Fine-Grained Iron Ore Using Deep Learning and the Internet of Things 5.1 Introduction 5.2 Related Work 5.3 Problems and Challenges in Image Segmentation 5.4 Deep Neural Networks 5.5 Experiments and Results 5.5.1 The Dataset 5.5.2 Hardware and Software Configuration 5.5.3 Model Evaluation: U-Net 5.5.4 Edge Device Solution Evaluation 5.6 Conclusions and Future Work Note References Chapter 6: Amalgamation of Blockchain Technology and Cloud Computing for a Secure and More Adaptable Cloud 6.1 Basics of Cloud Computing 6.1.1 Some of the Essential Characteristics of Cloud Computing 6.1.2 Cloud Service Model 6.1.3 Types of Cloud Deployment Model 6.1.4 Advantages and Disadvantages of Cloud Computing 6.2 Existing Cloud Computing Security and Related Issues 6.2.1 Confidentiality 6.2.2 Integrity 6.2.2.1 Data Integrity 6.2.2.2 Virtualization Integrity 6.2.3 Availability 6.2.3.1 Data/Service Availability 6.2.3.2 Virtualization Availability 6.3 Limitations in Existing Cloud-based Security 6.4 Basics of Blockchain Technology 6.5 Advantages of Blockchain 6.6 The Working of Blockchain 6.7 Amalgamation of Blockchain Technology and Cloud Computing 6.8 Conclusion, Future Scope, and Limitations 6.8.1 Conclusion & Future Scope 6.8.2 Limitations References Chapter 7: Cloud, Edge, and Fog Computing: Trends 7.1 Introduction 7.2 Background 7.3 Cloud Computing 7.3.1 Advantages of Cloud Computing 7.3.2 Limitations of Cloud Computing 7.4 Edge Computing 7.4.1 Advantages of Edge Computing 7.4.2 Limitations of Edge Computing 7.5 Fog Computing 7.5.1 Advantages of Fog Computing 7.5.2 Limitations of Fog Computing 7.6 Edge Computing vs Fog Computing 7.7 Smart Devices 7.8 Trends of Cloud, Edge, and Fog Computing 7.9 Conclusion Notes References Chapter 8: Progression in Cyber Security Concerns for Learning Management Systems: Analyzing the Role of Participants 8.1 Introduction 8.2 Literature Review 8.2.1 Cyber-Physical Systems for Education 8.2.2 Cyber Security in Learning Management Systems 8.3 Adoption of Cyber Security in LMSs by Instructors: A Case Study 8.4 Methodology 8.4.1 Measurement Model 8.4.2 Data Analysis Using Structural Equation Modeling (SEM) 8.5 Results and Discussion 8.5.1 Theoretical Implications 8.5.2 Practical Implications 8.6 Limitations and Future Scope References Chapter 9: A Security Model for Cloud-computing-based E-governance Applications 9.1 Introduction 9.2 Security of E-governance Applications 9.3 Related Work 9.4 Threats to the Security of E-governance Services 9.5 Security Model for E-governance 9.5.1 Firewall and Access Control 9.5.2 Intrusion Detection and Intrusion Prevention System (IDS/IPS) 9.5.3 Encryption 9.6 Experiment and Results Analysis 9.7 Conclusion 9.8 Limitation and Future Scope References Chapter 10: Automatic Time and Motion Study Using Deep Learning 10.1 Introduction 10.1.1 Research Contribution 10.2 Literature Review 10.3 Methodology 10.3.1 Micro-action Recognition 10.3.2 Macro-action Recognition 10.4 Case study: Order Preparation in a Distribution Center 10.4.1 Action-effectiveness Evaluation 10.4.2 Labor-productivity Metrics 10.5 Conclusions Acknowledgment References Chapter 11: Applications of IoT Based Frameworks in Industry 4.0: Applications of IoT Based Frameworks 11.1 Introduction 11.2 Literature Review 11.3 Proposed Framework 11.3.1 Radio Model 11.3.1.1 Degree of Proximity 11.3.1.2 Working Phases of the Proposed Framework 11.4 Applications 11.4.1 Application in Agricultural Industry 11.4.2 Application in Healthcare 11.4.3 Application in the Oil and Gas Industry 11.5 Results 11.6 Limitations 11.7 Conclusion and Future Work References Chapter 12: Impact of Deep Learning and Machine Learning in Industry 4.0: Impact of Deep Learning 12.1 Introduction 12.2 Industry 4.0 and Its Key Players 12.3 Literature Survey 12.4 Challenges and Role of Machine Learning and Deep Learning in IR 4.0 12.4.1 Challenges in IR 4.0 12.4.2 Role of Machine Learning and Deep Learning in IR 4.0 12.4.2.1 Machine Learning Methods in IR 4.0 12.4.2.2 Deep Learning Methods in IR 4.0 12.4.2.2.1 Convolutional Neural Networks 12.4.2.2.2 Auto-encoders 12.4.2.2.3 Recurrent Neural Networks 12.4.2.2.4 Deep Reinforcement Learning 12.4.2.2.5 Generative Adversarial Networks 12.4.3 Advantages of Machine Learning and Deep Learning in IR 4.0 12.4.4 Applications of Machine Learning and Deep Learning in IR 4.0 12.5 Proposed Model for IR 4.0 12.6 Conclusions and Future Work References Chapter 13: IoT Applications and Recent Advances 13.1 Introduction 13.2 IoT Services and Applications 13.2.1 Classification of IoT Services 13.2.1.1 Identity-related Services 13.2.1.2 Information Aggregation Services 13.2.1.3 Collaborative-aware Services 13.2.1.4 Ubiquitous Services 13.2.2 Prominent Applications of the IoT 13.2.2.1 The IIoT 13.2.2.2 The Internet of Medical Things (IoMT) 13.2.2.3 Smart Cities 13.3 Smart Environment Systems 13.3.1 Water Contamination Monitoring System 13.3.2 Air Pollution Monitoring System 13.4 Smart Intelligent Healthcare Systems 13.4.1 Research Frontiers in Smart Healthcare 13.4.2 The Role of Big Data Analytics in Smart Healthcare 13.4.3 The Role of Security and Privacy in Smart Healthcare Systems 13.5 IoT Communication and Network Protocols 13.5.1 Network Connectivity Protocols 13.5.1.1 Bluetooth 13.5.1.2 Wi-Fi 13.5.1.3 Zigbee 13.5.1.4 Z-Waves 13.5.1.5 Near Field Communication (NFC) 13.5.1.6 LoRa 13.5.1.7 6LowPAN 13.5.2 IoT Communication Protocols 13.5.2.1 MQTT 13.5.2.2 SMQTT 13.5.2.3 AMQP 13.5.2.4 CoAP 13.6 IoT Industrial Applications and Network Edges 13.7 Summary 13.8 Conclusions and Future Scope References Chapter 14: A Spatio-temporal Model for the Analysis and Classification of Soil Using the IoT 14.1 Introduction 14.2 Literature Survey 14.3 Proposed Methodology 14.4 Experimental Work 14.4.1 Experimental Setup 14.4.2 Procedure 14.5 Comparative Study and Results 14.6 Data Analysis 14.7 Discussion 14.8 Conclusion and Future Work References Chapter 15: A Critical Survey of Autonomous Vehicles 15.1 Introduction 15.2 History of Autonomous Vehicles 15.3 Technologies Used in Autonomous Vehicles 15.3.1 Sensors in Autonomous Vehicles 15.3.2 Forms of Camera 15.3.2.1 Omnidirectional Cameras 15.3.2.2 Cameras for Accidental Situations 15.3.3 Vehicular ad hoc Networks (VANET) in Autonomous Vehicles 15.4 Datasets Used for Autonomous Driving 15.5 Limitations and Challenges to Implementation 15.5.1 Cost of Vehicles 15.5.3 Litigation, Liability, and General Opinion 15.5.4 Security 15.5.5 Privacy 15.6 Research Gaps 15.7 Conclusion Bibliography Chapter 16: A Meta-learning Approach for Algorithm Selection for Capacitated Vehicle Routing Problems 16.1 Introduction 16.2 The Capacitated Vehicle Routing Problem 16.3 Meta-learning and Algorithm Selection 16.4 Meta-learning Process 16.4.1 Meta-features of the CVRP 16.4.2 Meta-labels 16.4.3 Meta-learning Technique 16.5 Meta-learning Experiment 16.5.1 Meta-knowledge Acquisition 16.5.2 Learning Phase 16.6 Results and Discussion 16.7 Conclusion, Limitations, and Future Scope References Chapter 17: Early Detection of Autism Disorder Using Predictive Analysis 17.1 Introduction 17.2 Literature Review 17.3 Dataset Description 17.4 Algorithm Description 17.4.1 Support Vector Machine 17.4.2 Logistic Regression 17.4.3 K-Nearest Neighbor (k-NN) 17.4.4 Naive Bayes 17.4.5 Correlation Matrix 17.5 Results and Discussion 17.6 Conclusion 17.7 Future Scope References Chapter 18: Computing Technologies for Prognosticating the Emanation of Carbon Using an ARIMA Model 18.1 Introduction 18.1.1 Formulation of the Problem 18.1.2 Research Objective 18.2 Research Strategy 18.3 Related Work 18.4 Research Framework 18.5 Dataset Description 18.6 Forecasting Model Performance Analysis 18.6.1 Autoregressive Integrated Moving Average (ARIMA) Model 18.7 Research Limitations 18.8 Conclusion and Future Scope References Chapter 19: DWT and SVD-Based Robust Watermarking Using Differential Evolution with Adaptive Optimization 19.1 Introduction to Digital Watermarking 19.2 Background Work 19.3 Materials and Methods 19.3.1 Discrete Wavelet Transform (DWT) 19.3.2 Singular Value Decomposition (SVD) 19.3.3 Differential Evolution (DE) 19.3.4 Embedding Algorithm 19.3.5 Extraction Algorithm 19.4 Results 19.4.1 Imperceptibility Analysis 19.4.2 Robustness Analysis 19.5 Concluding Remarks References Chapter 20: A Novel Framework Based on a Machine Learning Algorithm for the Estimation of COVID-19 Cases 20.1 Introduction 20.2 Literature Review 20.3 Principles of the SIR Model 20.4 Implementation of the Back-end 20.4.1 Data Understanding 20.4.2 Data Preparation 20.4.3 Understanding Exploratory Data Analysis (EDA) 20.4.4 Modeling Spread 20.4.5 Evaluation Walk-through 20.4.6 Overfitting 20.4.7 SIR Modeling 20.5 User Interface (UI) Implementation 20.6 Analysis 20.6.1 Hover-over India Map 20.6.2 Statewise updates 20.7 Results References Chapter 21: Assessing the Impact of Coronavirus on Pollutant Concentration: A Case Study in Malaysia 21.1 Introduction 21.2 Background Study 21.3 Proposed Methodology 21.4 Results and Discussion 21.5 Conclusion 21.6 Limitations and Future Work Notes References Chapter 22: A Comprehensive Review of SLAM Techniques 22.1 Introduction 22.2 SLAM and Its Methodologies 22.2.1 Relative Position Measurement 22.2.2 Absolute Position Measurement 22.2.3 Encoders 22.3 Mapping the Environment and Navigation 22.3.1 Mapping 22.3.2 Localization 22.3.2.1 Vehicle Model 22.3.2.2 Sensor Model 22.3.2.3 State Estimation 22.3.2.4 Support for SLAM Using a Kalman Filter 22.3.2.5 Particle Filter 22.4 Navigation 22.4.1 Definition of the SLAM Problem 22.4.2 Homogeneous Coordinates 22.4.3 Homogeneous Taking Euclidian into Consideration 22.4.4 Transformation 22.4.5 Translation: (Three Parameters) ➔ (Three Translations) 22.4.6 Rotation: (Three Parameters) ➔ (Three Rotations) 22.4.7 Rotation Matrices 22.4.8 Rigid Body Transformation: (Six Parameters) ➔ (Three Translations + Three Rotations) 22.4.9 Similarity Transformation: (Seven Parameters) ➔ (Three Translations + Three Rotations + One Scale) 22.4.10 Affine Transformation: (Twelve Parameters) ➔ (Three Translations + Three Rotations + Three Scales + Three Spheres) ( Mautz & Tilch, 2011) 22.5 Simulation 22.6 Hardware Requirements and Implementation in the Real World 22.6.1 Single Board Computers 22.6.2 Inertial Measurement Unit (IMU) 22.6.2.1 Odometry Model of Robot with Wheel Encoders 22.6.3 Basic Principle of the Accelerometer 22.6.4 Controllers 22.7 Limitations 22.8 Future Scope 22.9 Conclusion References Chapter 23: A Novel Evolutionary Computation Method for Securing the Data in Wireless Networks 23.1 Introduction 23.2 Ant Colony Optimization 23.2.1 Biological Analogy 23.2.2 ACO Algorithm 23.3 Artificial Bee Colony Optimization 23.3.1 Bee Foraging Behavior 23.3.2 Modified Approach 23.3.3 Algorithm 23.4 Cuckoo Approach 23.4.1 Introduction 23.4.2 Cuckoo Reproductive Method 23.4.3 The Lévy Flights Mechanism 23.4.4 Algorithm 23.5 Particle Swarm Optimization (PSO) 23.5.1 Introduction 23.5.2 The History of Particle Swarm Optimization 23.5.3 Algorithm 23.6 Comparative Study of PSO and CS 23.6.1 Experiments 23.7 Comparative Study between Ant Colony Optimization and Cuckoo Search Optimization 23.8 Comparison Between ACO, ABC, PSO, and Cuckoo Search 23.9 Implementation of Cuckoo Search Algorithm 23.10 Conclusion Bibliography Index
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