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 โ 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