Intelligent Techniques for Cyber-Physical Systems

Intelligent Techniques for Cyber-Physical Systems covers challenges, opportunities, and open research directions for cyber-physical systems (CPS). It focuses on the design and development of machine learning and metaheuristics-enabled methods as well as blockchain for various challenges like security, resource management, computation offloading, trust management, and others in edge, fog, and cloud computing, Internet of Things (IoT), Internet of Everything (IoE), and smart cities. It also includes the design and analysis of deep learning-based models, sensing technologies, metaheuristics, and blockchain for complex real-life systems for CPS. Offers perspectives on the research directions in CPS; Provides state-of-the-art reviews on intelligent techniques, machine learning, deep learning, and reinforcement learning-based models for cloud-enabled IoT environment; Discusses intelligent techniques for complex real-life problems in different CPS scenarios; Reviews advancements in blockchain technology and smart cities; Explores machine learning-based intelligent models for combinatorial optimization problems. The book is aimed at researchers and graduate students in computer science, engineering, and electrical and electronics engineering. Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface About the Editors Contributors Chapter 1 Delay-Aware Partial Computational Offloading and Resource Allocation in Fog-Enabled Cyber-Physical Systems 1.1 Introduction and Background 1.2 Fog Computing in IoT Networks 1.2.1 Fog Computing Network Scenario 1.2.2 Fog Network Characteristics 1.3 Literature Overview of Offloading and Task Scheduling 1.4 Computation Model 1.4.1 Latency Model in Smart Device 1.4.2 Latency Model in Fog Networks 1.4.2.1 Task Up-Link Delay 1.4.2.2 Queuing Delay at Fog Controller 1.4.2.3 Task Computation Latency 1.4.2.4 Total Task Computation Latency 1.5 Knapsack Optimization-Based Resource Allocation 1.6 Results and Discussion 1.6.1 Numerical Parameters 1.6.2 Total Latency for Varying Number of Fog Nodes 1.6.3 Effect of Varying Task Sizes on End-to-End Latency 1.6.4 Average Latency Performance 1.7 Conclusion References Chapter 2 Enhancing the Security of Cryptographic Algorithms in Cloud-Based Cyber-Physical Systems (CCPSs) 2.1 Introduction 2.2 Related Work 2.3 Algorithms 2.3.1 Shannon Entropy 2.3.2 Whale Optimization Algorithm 2.3.2.1 Encircling Prey 2.3.2.2 Exploitation Phase 2.3.2.3 Search for Prey (Exploration Phase) 2.3.3 Grey Wolf Optimization 2.3.4 Bat Algorithm 2.4 Problem Formulation 2.5 Proposed Work 2.5.1 Proposed Framework 2.5.2 Key Generation Using Whale Optimization Algorithm 2.6 Simulation and Results 2.6.1 Result 2.6.2 NIST Statistical Test 2.6.3 Observations 2.7 Conclusion References Chapter 3 Containerized Deployment of Microservices in Cloud Computing 3.1 Introduction 3.2 Background 3.2.1 Microservices 3.2.2 Containers 3.2.3 Docker 3.2.3.1 Definition 3.2.3.2 Docker Container Architecture 3.2.3.3 Docker Containers and Images 3.2.3.4 Application and Research Areas 3.2.4 Optimization Techniques 3.2.4.1 Dynamic Bin Packing 3.2.4.2 Particle Swarm Optimization 3.3 Related Work 3.3.1 Application Deployment 3.3.2 Container-Based Scheduling 3.3.3 Advancement in Optimization Methods 3.4 The Proposal 3.4.1 System Model 3.4.2 Problem Statement and Formulation 3.4.2.1 Application Deployment Cost Formulation 3.4.2.2 Resource Wastage Formulation 3.4.2.3 Power Consumption Modeling 3.4.2.4 Binary PSO Formulation 3.4.3 The Proposed Methods 3.4.3.1 Microservices-to-Container Mapping 3.4.3.2 Container-to-PM Mapping 3.5 Result and Analysis 3.5.1 Simulation Settings 3.5.2 Analysis and Results 3.5.2.1 Comparison of Resource Wastage 3.6 Conclusion References Chapter 4 RSS-Based Smart Device Localization Using Few-Shot Learning in IoT Networks 4.1 Introduction 4.1.1 Motivation and Literature Review 4.2 Localization Methodology 4.2.1 Fingerprinting-Based Localization 4.2.2 k-Nearest Neighbours (k-NN) 4.2.3 Decision Tree (DT) 4.2.4 Multi-Layer Perceptron (MLP) 4.2.5 Siamese Network-Based Few-Shot Approach for Localization 4.2.5.1 Few-Shot Approach with the Siamese Network 4.3 Results 4.4 Conclusions References Chapter 5 Data-Driven Risk Modelling of Cyber-Physical Systems 5.1 Introduction 5.2 Procedure for Risk Modelling 5.2.1 Reinforcement Learning Technique 5.3 Case Study 5.4 Significance of the Approach 5.5 Issues in the Implementation of Reinforcement Learning in Risk Modelling 5.6 Summary of the Chapter References Chapter 6 Automation of the Process of Analysis of Information Security Threats in Cyber-Physical Systems 6.1 Introduction 6.2 Related Works 6.3 Examination of the Architecture of Cyber-Physical Systems 6.4 Development of a Methodology for Ensuring the Safety of CPS 6.4.1 Assessment of the Level of Criticality 6.4.2 Analysis of the Level of Heterogeneity of the System 6.4.3 The Complexity of the Attack 6.4.4 Assessment of the Degree of Negative Consequences of the Implementation of the Threat 6.4.5 Determining the Value of an Information Resource 6.5 Cyber-Physical System Threat Database Development 6.5.1 Database Architecture 6.5.2 Threat Catalog 6.6 Conclusion Acknowledgments References Chapter 7 IoT in Healthcare: Glucose Tracking System 7.1 Introduction 7.1.1 Organization of the Chapter 7.2 Literature Study 7.3 How IoT Performs Its Sensing Task? 7.4 Use of Smartwatch in Glucose Tracking 7.4.1 They Do More Than Just Keep Time 7.4.2 A Travel Companion that is Always with You 7.4.3 Finding a Phone, Key, or Another Item is Even Simpler 7.4.4 Answer Calls and Messages Right Away 7.4.5 Review Your Social Media Notifications 7.4.6 You Remain Connected Even as You Work 7.4.7 It Gives You More Time to Be Connected Compared to Your Phone 7.4.8 You Have Access to Plenty of Entertainment 7.4.9 Remind You via Email 7.4.10 They Function as Reliable Fitness Trackers 7.5 IoT in the Healthcare 7.6 Diabetics Tracking Using IoT Tracking Device 7.7 Integrating Wireless Sensors for Tracking 7.8 Smart IoT-Enabled Sensors 7.8.1 Increasing Efficiency 7.8.2 Enhancing Safety 7.9 Monitors for the Healthcare Industry 7.9.1 Digital Patient Surveillance 7.10 Screening Blood Sugar 7.11 Findings and Analysis 7.11.1 Impacts of IoT in Healthcare Glucose Tracking System 7.12 Proposed Glucose Tracking System 7.13 Conclusion References Chapter 8 Intelligent Application to Support Smart Farming Using Cloud Computing: Future Perspectives 8.1 Introduction 8.2 Methods and Materials 8.2.1 Arduino Uno 8.2.2 DHT11/DHT22 Humidity Sensor 8.2.3 YL-69 Soil Moisture Sensor 8.2.4 Camera 8.2.5 Cloud Storage 8.3 JSON 8.4 React Native 8.5 Web User Interface 8.6 Open Weather Map API 8.7 Results and Discussion 8.8 Conclusion References Chapter 9 Cybersecurity in Autonomous Vehicles 9.1 Introduction 9.2 Cyber Threats 9.2.1 Communicating Channel 9.2.2 LiDAR Attack 9.2.3 Packet Sniffing and Fuzzing Attacks 9.2.4 Signal Jamming and Spoofing Attacks 9.2.5 DoS (Denial-of-Service) Attacks 9.2.6 Credential Acquiring Attack 9.2.7 Attacks via Update 9.2.8 Remote Access Attacks 9.2.9 Location- and Timing-Based Attacks 9.2.10 Misguiding Attacks 9.2.11 Visual and Audio Attacks 9.2.12 Third-Party Download Attacks 9.2.13 Threats Via Wi-Fi Hotspots 9.3 Methods to Enhance Cybersecurity 9.3.1 In-Vehicle Device and Secure Communication 9.3.2 Application for User Authentication 9.3.3 Deployment of Firewall 9.3.4 Source Signal Block and Distance Bounding 9.3.5 Deployment and Installation of Gateway for CAN 9.3.6 Automated DDN Tests and Procedures 9.3.7 Data Privacy Prevention 9.4 Cryptographic Lightweight Techniques 9.5 Conclusion References Chapter 10 Use of Virtual Payment Hubs Over Cryptocurrencies 10.1 Introduction 10.2 Literature Review 10.3 Proposed Methodology 10.3.1 System's Functionality 10.3.2 Security and Efficiency 10.3.3 Structure of the System 10.4 An Overview of the Technical Details 10.5 Conclusion References Chapter 11 Akaike's Information Criterion Algorithm for Online Cashback in Vietnam 11.1 Introduction 11.2 Literature Review 11.2.1 What Is Cashback? 11.2.2 Using Behavior of Cashback (UBC) 11.2.3 Ease of Use (EU) 11.2.4 Personal Capacity (PC) 11.2.5 Perceived Risk (PR) 11.2.6 Using Intention of Cashback (UIC) 11.3 Methods 11.3.1 Sample Approach 11.3.1.1 Blinding 11.4 Results 11.4.1 Overview of the Cashback Program in the World 11.4.2 The Cashback Program in Vietnam 11.4.3 Akaike's Information Criterion (AIC) Selection 11.4.4 Variance Inflation Factor (VIF) 11.4.5 Heteroskedasticity 11.4.6 Autocorrelation 11.4.7 Model Evaluation 11.4.8 Discussion 11.5 Conclusion 11.6 Implications 11.6.1 Implication for PR 11.6.2 Implication for Personal Capacity and Perceived Risk 11.6.3 Limitations and Next Research Directions References Chapter 12 Capacitated Vehicle Routing Problem Using Algebraic Harris Hawks Optimization Algorithm 12.1 Introduction 12.2 Literature Review 12.3 Problem Formulation 12.4 Proposed Work 12.4.1 Permutation Group Preliminaries 12.4.2 Algebraic Harris Hawks Optimization Algorithm 12.4.2.1 Phase I: Exploration Phase 12.4.2.2 Phase II: Exploitation Phase 12.4.2.3 Soft Besiege 12.4.2.4 Hard Besiege 12.4.2.5 Soft Besiege with Progressive Rapid Dives 12.4.2.6 Hard Besiege with Progressive Rapid Dives 12.5 Experimental Study 12.5.1 System Settings and the State-of-the-Art Algorithms 12.5.2 Simulation Routing Results 12.5.3 Observations 12.6 Conclusion References Chapter 13 Technology for Detecting Harmful Effects on the UAV Navigation and Communication System 13.1 Introduction 13.2 UAV Threat and Vulnerability Analysis 13.2.1 Development of an Attack Vector for UAVs 13.3 Analysis of an Anomaly Detection Method 13.3.1 Analysis of Analogs of the Developed Technology for Detecting Harmful Effects on the UAV Navigation and Communication System 13.3.2 Implementation of Technology for Detecting Harmful Effects on the UAV Navigation and Intercommunication System 13.4 Results and Discussion 13.5 Conclusion Acknowledgments References Chapter 14 Current and Future Trends of Intelligent Transport System Using AI in Rural Areas 14.1 Introduction 14.1.1 VANET Characteristics 14.1.2 VANET Routing Protocols 14.1.3 Classification of Ad-Hoc Routing Protocol 14.2 Literature Review 14.3 Artificial Intelligence and Intelligent Transport System 14.3.1 Artificial Intelligence and VANET 14.3.1.1 AI and Driverless Vehicles 14.3.1.2 Operations and Difficulties of AI in Transport 14.3.1.3 Benefits of AI in Road Transport 14.3.2 Intelligent Transport System 14.3.2.1 Goals of Intelligent Transport System 14.3.2.2 Applications of Intelligent Transport System 14.3.2.3 Current Scenario of ITS in India 14.4 Background Study 14.4.1 Problem Statement 14.4.1.1 Challenges in Implementing ITS in India 14.4.2 SUMO Tool 14.4.3 Simulation Results 14.4.3.1 Traffic Model 14.4.3.2 Modification of Trust Signals 14.5 Conclusion and Future Work References Chapter 15 Future Technology: Internet of Things (IoT) in Smart Society 5.0 15.1 Introduction 15.2 Smart Society 15.2.1 Pillars of Intelligent Society 15.2.2 Characteristics of Smart Society 15.2.3 Smart Society and Sustainable Development 15.3 Internet of Things (IoT) 15.3.1 Interaction Between Rural and Urban Regions Through ICT 15.3.2 Digital Gap Between Rural and Urban Areas 15.4 Literature Review: Past Challenges in the Smart Society in Developing Countries 15.4.1 Policies and Regulations of Information & Communication Technology 15.4.2 Financial Ambitions 15.4.3 Standardization 15.4.4 Human Capital 15.4.5 Sustainable Development Via ICT 15.4.6 The Role of Artificial Intelligence in a Smart Society 15.4.6.1 How AI-Based Smart Home Systems Work 15.4.6.2 Smart Devices with a Location Function 15.4.6.3 Voice-Enabled Devices 15.4.6.4 Intelligent Security System 15.4.6.5 Face Detection 15.4.6.6 Detecting Motion 15.4.6.7 Regulation of Biometric Access 15.4.6.8 Recognition of Voice 15.5 Smart Society Challenges 15.5.1 Challenges Resolved by AI and IoT 15.5.1.1 The AI in a Smart Town 15.5.1.2 Smart Management of Water 15.5.1.3 Smart Lighting System 15.5.1.4 Smart Traffic Control 15.5.1.5 Smart Parking Space 15.5.1.6 Smart Management of Waste 15.5.1.7 Smart Police Force 15.5.1.8 Smart Governance 15.5.1.9 Smart Society Reflect to Smart Nation 15.6 The Case Study of Society 5.0 in the Real World 15.6.1 Society 5.0 Enables a Commitment to Sustainability 15.6.2 Case Study: Hitachi-UTokyo AI-Based Modern Society 15.7 Conclusion 15.8 Limitation References Chapter 16 IoT, Cloud Computing, and Sensing Technology for Smart Cities 16.1 Introduction 16.2 Cloud Infrastructure, Management, and Operations 16.2.1 Cloud Infrastructure and Management 16.2.2 Cloud Infrastructure Management Tools 16.2.3 Cloud Operations 16.3 Cloud-Based IoT Solutions 16.3.1 Thingworx 8 16.3.2 Microsoft Azure IoT Suite 16.3.3 Google Cloud's IoT Platform 16.3.4 IBM Watson 16.3.5 AWS IoT Platform 16.3.6 Cisco IoT Cloud Connect 16.3.7 Sales Force IoT Cloud 16.3.8 Kaa IoT 16.3.9 Thingspeak 16.3.10 GE Predix IoT 16.4 Applications of IoT, Cloud Computing, and Sensing Technology for Smart Cities 16.4.1 Work From Home with IoT 16.4.2 Smart Healthcare 16.4.3 IoT in Retail 16.4.4 Smart Education 16.4.5 Smart Agriculture 16.4.5.1 Climate Conditions 16.4.5.2 Precision Agriculture 16.4.5.3 Smart Greenhouse 16.4.5.4 Data Analysis 16.4.5.5 Agriculture Drone 16.4.6 Smart Transportation 16.4.6.1 Traffic Management 16.4.6.2 Automated Toll and Ticketing 16.4.6.3 Self-Driven Cars 16.4.6.4 Transportation Monitoring 16.4.6.5 Security of Public Transportation 16.4.7 Smart Infrastructure 16.4.7.1 IoT Devices – Sensors and Actuators 16.4.7.2 Edge Gateways and IoT Connectivity 16.4.8 Smart Energy 16.4.8.1 Optimization of Energy Resources 16.4.8.2 Empowering Microgrids 16.4.8.3 Smart Meter Technology 16.4.8.4 Proactive Repair Mechanism 16.4.9 Smart Parking 16.4.10 Smart Waste Management 16.4.11 Water Quality Management 16.4.12 Crime Reduction 16.5 The Importance of Cloud-Based IoT for Smart Cities 16.6 The Future of Cloud-Based IoT Technology 16.6.1 Increased Storage Capacity 16.6.2 IoT in the Automobile Industry 16.6.3 Better Security 16.6.4 IoT Advanced Forecast 16.6.5 Smart Eye 16.6.6 Short-Term Growth and Explosive Long-Term Growth 16.6.7 The Impact of the Cloud and IoT on the Economy 16.6.8 Robotics 16.7 Challenges of Combining IoT, Cloud Computing, and Sensing Technology for Smart Cities 16.8 Conclusion References Chapter 17 Utilization of Artificial Intelligence in Electrical Engineering 17.1 Introduction 17.2 Advantages and Limitations of Artificial Intelligence 17.3 AI Technologies in Electrical Engineering 17.3.1 Expert Systems 17.3.2 Artificial Neural Network 17.3.3 Machine Learning 17.3.4 Fuzzy Logic Systems 17.3.5 Deep Learning 17.3.6 Pattern Recognition 17.4 Utilizations of AI in Electrical Engineering 17.4.1 Utilization of AI in Electrical Component and Machine 17.4.2 Utilization of AI in Control of Electrical Machinery 17.4.3 Utilization of AI in Fault Analysis 17.5 ANN Control in Dual 2-L Three-Phase Inverter System to Achieve Multi-Level Output 17.5.1 Dual 2-L Three-Phase VSI 17.5.2 Three-Level Operation Using Dual 2-L VSI 17.5.3 ANN-Based Pulse Width Modulation Technique 17.5.4 Simulation Results and Discussion 17.6 Conclusion References Chapter 18 Major Security Issues and Data Protection in Cloud Computing and IoT 18.1 Introduction 18.2 Cloud-Based IoT 18.3 Cloud-IoT Applications 18.3.1 Health Care 18.3.2 Smart Cities 18.3.3 Smart Homes 18.3.4 Smart Energy and Smart Grid 18.3.5 Automotive and Smart Mobility 18.3.6 Smart Logistics 18.3.7 Environmental Monitoring 18.4 Advantages of IoT and Cloud Integration 18.4.1 Communication 18.4.2 Storage 18.4.3 Processing Capabilities 18.4.4 Scope 18.4.5 Additional Abilities 18.5 Cloud-Based IoT Architecture 18.6 Major Benefits of Cloud-Based IoT 18.6.1 Accessibility 18.6.2 Scalability 18.6.3 Fewer Cables, Papers, and Minerals 18.6.4 Collaboration 18.6.5 Disaster Recovery 18.6.6 Data Mobility 18.6.7 Data Security and Reliability 18.6.8 Cost-Effectiveness 18.6.9 Data Storage 18.7 Implications of Cloud-Based IoT Integration 18.7.1 Security and Privacy 18.7.2 Heterogeneity 18.7.3 Big Data 18.7.4 Performance 18.7.5 Legal Aspects 18.7.6 Large Scale 18.7.7 Dependability 18.7.8 Data Storage 18.7.9 Maintenance 18.8 The Strategies and Problems of Cloud-IoT Security 18.8.1 Data Security 18.8.2 Identity Verification and Privacy 18.8.3 Access Control 18.8.4 Permissions 18.8.5 Secure IoT on Mobile 18.9 Conclusion References Index