Applications of 5G and Beyond in Smart Cities

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Editor Biographies
Contributors
Preface
Acknowledgement
Chapter 1 Introduction to 5G and Beyond
1.1 Introduction to 5G Network and Applications
1.2 5G NR Terminologies in the Physical Layer
1.3 5G NR Requirements – IMT2020
1.4 5G NR Spectrum
1.4.1 Frequency Range 1 (FR1)
1.4.2 Frequency Range 2 (FR2)
1.5 5G NR Network Architecture
1.5.1 5G Access Network
1.5.2 5G Core Network
1.5.3 5G Core Network Components
1.6 5th Generation Network Applications
1.6.1 Smart City – Next Vision of Living
1.6.2 How is 5G a Game-Changer in the Concept of the Smart City?
1.6.3 Typical Activities in a Smart City
1.7 Manufacturing
1.7.1 Smart Factory Uses
1.8 Agriculture
1.8.1 How 5G Can Change the Agriculture Sector: Smart Farming
1.9 Media and Entertainment
1.10 Healthcare
1.10.1 Healthcare Applications
1.10.1.1 Remote Monitoring
1.10.1.2 Advance Remote Procedures with 5G Technology
1.10.1.3 Handling of Patient Data Records
1.10.1.4 Telemedicine Appointments
1.11 Engineering Applications
1.11.1 Remote Operation
1.11.2 Advanced Design Decisions
1.11.3 Timely Deliveries
1.12 Financial Services
1.13 Public Transport
1.14 Public Safety
1.15 Energy and Utilities
1.15.1 Smart Grid
1.15.2 Smart Meters for the Home
1.15.3 Remote Monitoring of Energy Sites
1.16 Summary
Acronyms
References
Chapter 2 The Role of 5G in Smart Transportation
2.1 Introduction
2.2 Role of 5G in Real-Time Traffic Operations
2.3 Role of 5G in Entertainment Systems in Vehicles
2.4 Role of 5G in Driverless cars and Autonomous Vehicles
2.5 Role of 5G in Sensor-Based Intelligent Transportation Applications
2.6 Role of 5G in Accident Prevention Systems
2.7 Role of 5G for Transit Operations
2.8 Role of 5G in Advanced Driver Assistance Systems (ADAS)
2.9 Role of 5G in Logistics Operations
2.10 Summary
References
Chapter 3 Network Management in Smart Cities
3.1 Introduction
3.2 Forensics
3.2.1 Digital Device Forensics
3.2.2 Other Digital Forensics
3.2.3 The Need for IoT Forensics
3.3 Challenges in IoT Forensics
3.3.1 General Issues
3.3.2 Evidence Identification, Collection, and Preservation
3.3.3 Evidence Analysis and Correlation
3.3.4 Presentation
3.4 Opportunities of IoT Forensics
3.5 Cloud Computing Security
3.5.1 Effectively Manage Identities
3.5.2 Key Concerns about Cloud Computing
3.5.3 Trends in Big Data as an Enabling Technology
3.6 Smart Cities
3.6.1 Smart City Concept
3.6.2 Cloud Computing Benefits in the Context of Smart City
3.7 Smarter Grid
3.8 Smart Home
3.9 Smart City Data Plan Challenges
3.9.1 Compatibility between Smart City Devices
3.9.2 Simplicity
3.9.3 Mobility and Geographic Control
3.10 Software-Defined Network-Based Smart City Network Management
3.10.1 Centralized Control
3.10.2 Simplicity and Inerrability
3.10.3 Virtualization
3.10.4 Compatibility
3.10.5 Challenges of SDN in Smart City Applications
3.11 Software-Defined Things Framework
3.11.1 Reactive Smart City Device Management
3.11.2 Smart Mobility and Smart Traffic Management
3.11.3 Smart Environment
3.11.4 Security
3.11.5 Advanced Optical Network Architecture for Next-Generation Internet Access
3.12 Conclusion and Future Work
References
Chapter 4 Energy-Efficient Reinforcement Learning in Wireless Sensor Networks Using 5G for Smart Cities
4.1 Introduction
4.1.1 Problem Statement
4.1.2 Objectives
4.2 Literature Review
4.2.1 Wireless Sensor Network
4.2.2 Artificial Intelligence
4.2.3 Deep Reinforcement Learning
4.2.4 Energy Efficiency in WSN
4.2.5 Reinforcement Learning
4.2.6 5G (5th Generation)
4.2.7 Smart Cities
4.3 Methodology
4.3.1 Clustering
4.3.2 Grouping of Nodes
4.3.3 Q-Learning
4.3.4 Reinforcement Learning
4.3.5 K-Means Clustering
4.4 Design Aspects
4.4.1 Design Considerations
4.4.1.1 Fault Tolerance
4.4.1.2 Lifetime
4.4.1.3 Scalability
4.4.1.4 Data Aggregation
4.4.1.5 Cost
4.4.1.6 Environment
4.4.1.7 Heterogeneity Support
4.4.1.8 Autonomous Operations
4.4.1.9 Limited Memory and Processing Capability
4.4.2 Node Creation
4.4.3 Distance Computation
4.4.4 Energy Parameters
4.4.5 WSN Environment
4.4.6 Q-Learning Agent
4.4.7 Classification of Different Nodes
4.4.8 Data Transfer Directly from Primary Node to Base Station
4.4.9 Selection of Base Nodes
4.5 Results
4.6 Conclusion
References
Chapter 5 The Role of 5G Networks in Healthcare Applications
5.1 Introduction
5.2 5G Networks
5.3 Applications of 5G Networks
5.3.1 Smart Healthcare Applications
5.3.2 Internet of Things (IoT) Devices in Healthcare Applications
5.3.3 5G Networks in Healthcare Applications
5.4 Challenges in the Deployment of 5G Networks in Healthcare Applications
5.4.1 Security and Data Privacy Issues
5.4.2 Ethical Issues
5.5 Recommendations for Leadership
5.6 Conclusion
Reference list
Chapter 6 Energy Consumption in Smart City Projects in the Era of 5G: An Analysis of User-Generated Content
6.1 Introduction
6.2 Literature Review
6.3 Research Development and Findings
6.4 Discussion and Implications
6.4.1 Discussion
6.4.2 Theoretical Implications
6.4.3 Managerial Implication
6.5 Conclusion
References
Chapter 7 The Role of 5G in Railway Applications
7.1 Introduction
7.2 Scope of 5G for IoT-Based Railway Monitoring
7.3 Scope of 5G for WSN-Based Railway Monitoring
7.4 Spectrum Requirement of 5G for Railway Monitoring
7.5 5G Physical Layer Support for Railway Monitoring
7.6 5G Railway Monitoring Application Challenges
7.7 Trending Research
7.8 Requirements of Smart Railway Monitoring Systems Using 5G
7.8.1 Physical Infrastructure
7.8.2 Emerging Technologies
7.8.3 Security System
7.8.4 Software Analytics
7.8.5 Research Methodology
7.9 Conclusion
References
Chapter 8 Implications of Progressive Data Transfer Technologies for IoT-Based Wastewater Management in Smart Cities
8.1 Introduction
8.2 Conventional Vs. Smart Technologies for Water Management
8.2.1 Conventional Wastewater Management
8.2.2 Smart Wastewater Management
8.3 Role Of Sensors and Single-Board Computers (SBCs) in the Development of Smart Water Management Infrastructure
8.4 Factors Promoting the Implementation of Smart Technologies in Water Management and Remediation
8.5 Role of 5G in Smart Water Management
8.6 Limitations and Future Perspectives
8.7 Conclusion
Acknowledgments
Conflict of Interest
References
Chapter 9 Smart Grid Design with Hybrid Renewable Energy Management Systems for Smart Cities
9.1 Introduction
9.2 Materials and Methods
9.2.1 System Design
9.2.2 Energy Management System in Smart City Applications
9.2.3 Techniques and Possibilities
9.3 Discussion
9.4 Conclusion
References
Chapter 10 MIMO-NOMA With mmWave Transmission
10.1 Introduction
10.2 Types of NOMA
10.2.1 Power Domain
10.2.2 Code Domain
10.3 NOMA with Multiple-Antenna Transmission
10.3.1 Downlink Channel
10.3.2 User Pairing
10.3.3 Power Allocation
10.3.4 mmWave Transmission
10.4 Transmission Beamforming
10.4.1 mmWave Analog Beamforming
10.4.2 Hybrid Beamforming in mmWave
10.4.3 Multi-Beamforming
10.5 Simulation Results
10.6 Deployment and Practical Issues for Smart City
10.6.1 Application of Pairing Greater than Three Users
10.6.2 Successive Interference Cancelation (SIC)
10.6.3 User Mobility
10.6.4 Channel State Information
10.6.5 Multiple-Cell NOMA
10.7 Conclusion
References
Chapter 11 Traditional and Modern Techniques for Visible Light Positioning Systems
11.1 Introduction: An Overview of Visible Light Positioning Techniques
11.2 Localization Process and Operational Framework
11.3 Traditional Methods for Positioning
11.3.1 Angle of Arrival (AoA)
11.3.2 Time of Arrival (ToA)
11.3.3 Time Difference of Arrival (TDoA)
11.3.4 Received Signal Strength (RSS)
11.4 Positioning Algorithms – An Overview
11.4.1 Mathematical Method:
11.4.2 Sensor-Assisted Method
11.4.3 Optimization Method
11.5 Review of Machine Learning and Artificial Intelligence
11.5.1 Supervised Learning
11.5.2 Unsupervised Learning
11.5.3 Semi-Supervised Learning
11.5.4 Reinforcement Learning
11.5.5 Deep Learning
11.5.5.1 Dense Neural Networking (DNN)
11.5.5.2 Convolutional Neural Networking (CNN)
11.5.5.3 Recurrent Neural Networking (RNN)
11.5.6 Auto-Encoders
11.5.7 Extreme Learning Machine
11.6 Machine Learning and Artificial Intelligence-Based Positioning Algorithms
11.6.1 Indoor Localization with Supervised Learning Approaches
11.6.2 Indoor Localization with the Deep Learning Approaches
11.6.3 Indoor Localization with Unsupervised Learning Approaches
11.7 Comparison of Machine Learning-Based Positioning Algorithms
11.8 Conclusion and Future Work
References
Index