5G and Beyond: The Futrure of IoT

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
Part I: Fundamental Architectural Concepts for 5G and IoT
Chapter 1: The Impact of Artificial Intelligence on 5G-Enabled IoT Networks
1.1 Introduction
1.1.1 Artificial Intelligence: State of the Art and Prospects
1.1.2 Important Subsets of AI
1.1.3 Background of AI
1.1.4 Current Research in 5G
1.2 Role of AI and 5G in Digital Transformation Across Industries
1.3 Impact of Machine Learning for a 5G Future
1.3.1 Categorization of Machine Learning Models for 5G Deployment
1.4 Potential and Limitations of AI and Machine Learning for 5G
1.4.1 Potential of AI
1.4.2 Limitations of Using AI and ML
1.5 Requirements and Key Enabling Technology in 5G IoT
1.6 Artificial Intelligence Driven Cases for Real-Time Business and 5G IoT
1.6.1 COVID-19, Digital Healthcare and the Role of 5G
1.6.2 Real-World Business Use Cases for AI
1.7 Conclusions
References
Chapter 2: Attacks, Security Concerns, Solutions, and Market Trends for IoT
2.1 Introduction
2.1.1 Layered Architecture of IoT Network
2.1.2 Building Blocks of IoT System
2.2 IoT Devices
2.2.1 IoT Device Lifecycle
2.2.2 Benefits of IoT Devices
2.2.3 Drawbacks of IoT Devices
2.3 IoT Network Technologies
2.4 Data Aggregation in IoT
2.5 Attacks and Security Threats in IoT
2.5.1 Attacks on Different Network Layers
2.5.2 Attacking Tools Used in IoT
2.5.3 Solutions to IoT Security Attacks
2.5.3.1 Traditional Defense Techniques
2.5.3.2 Current Defense Techniques
2.6 Optimization of IoT Network
2.6.1 WOA Algorithm
2.6.2 Simulated Annealing
2.7 Challenges in IoT
2.8 IoT Market Analysis
References
Chapter 3: Intelligence and Security in the 5G-Oriented IoT
3.1 Introduction
3.1.1 5G Integration with IoT
3.2 Detailed Components of IoT–5G Integration
3.2.1 Layered Architecture
3.3 Properties
3.3.1 Quality of Service
3.3.2 Functional Requirements
3.3.3 Non-Functional Requirements
3.4 Security
3.4.1 Recognition Layer
3.4.2 Connectivity Layer
3.4.3 Support Layer
3.4.4 Application Layer
3.4.5 Business Layer
3.5 Intelligence in the 5G-Oriented Internet of Things
3.6 Tools
3.6.1 Hadoop
3.6.2 Spark
3.6.3 Hive
3.6.4 R Studio
3.6.5 Python
3.6.6 CupCarbon
3.7 Open Issues and Future Research Directions
3.8 Conclusion
References
Chapter 4: Advances in Mobile Communications from a 5G Perspective
4.1 Introduction
4.2 Cognitive Radio Perspectives
4.2.1 CR Functionalities
4.2.1.1 Spectrum Sensing
4.2.1.2 Spectrum Management
4.2.1.3 Spectrum Sharing
4.2.1.4 Spectrum Mobility
4.2.2 Cognitive Radio in 5G
4.2.2.1 Antennas for CR in 5G
4.2.2.2 Cognitive Engines
4.2.2.3 Improved PHY Technologies
4.3 Small-Cell Coverage in 5G Communications
4.3.1 Trends in Small Cells
4.3.2 Technical Aspects of Small Cells
4.3.2.1 Carrier Aggregation
4.3.2.2 Multi-Cell Cooperation
4.3.2.3 Massive MIMO
4.3.2.4 Multiple Access Techniques
4.4 Small Cells and 5G
4.5 IoT Perspective
4.6 Directing CRNs toward IoT
4.7 How CRNs Fulfill IoT Requirements
4.7.1 Channel Allocation
4.7.2 Protocol Design
4.7.3 Energy Harvesting
4.8 Small Cells Fulfilling the IoT Requirement
4.9 Small-Cell Deployment through CR
4.10 Conclusions
References
Chapter 5: The Role of IoT in Smart Technologies
5.1 Introduction
5.1.1 Components of the Smart Home
5.2 Communication Protocols and their Features
5.2.1 Desirable Attributes for IoT Communication Protocols
5.3 Basics of Prime IoT Communication Protocols
5.3.1 Bluetooth [6]
5.3.2 Bluetooth Low Energy [7]
5.3.3 ZigBee [8]
5.3.4 Z-Wave [9]
5.3.5 IPv6LowPAN [10]
5.3.6 Thread [11]
5.3.7 WiFi (Wireless Fidelity) [12]
5.3.8 Cellular [13]
5.3.9 Near Field Communication (NFC) [14]
5.3.10 Sigfox [15]
5.3.11 LoRaWAN [16]
5.4 Risks with Wireless Protocols in the Context of IoT
5.5 Conclusion
References
Part II: Applied Scenarios of 5G and IoT
Chapter 6: Realization of New Radio 5G-IoT Connectivity Using mmWave-Massive MIMO Technology
6.1 Introduction
6.2 Waveform Design Approaches
6.2.1 OFDM
6.2.2 FBMC
6.2.3 GFDM
6.2.4 UFMC
6.3 Spatial Multiplexing
6.4 Precoding
6.4.1 Digital Precoding
6.4.1.1 SU Digital Precoding
6.4.1.2 MU Digital Precoding
6.4.2 Analog Beamforming
6.4.2.1 Beam Steering
6.4.2.2 Beam Training
6.4.3 Hybrid Precoding
6.4.3.1 SU Hybrid Precoding
6.4.3.2 MU Hybrid Precoding
6.4.3.2.1 Two-Stage Hybrid Precoding
6.5 Channel Measurement and Modeling
6.5.1 Channel Measurement
6.5.2 Channel Modeling
6.6 Channel Estimation
6.7 Training-Based Channel Estimation
6.7.1 Blind Channel Estimation
6.7.2 Compressive Sensing-Based CE Scheme
6.8 Conclusions
Abbreviations
References
Chapter 7: Algebraically Constructed Short Sequence Families for 5G NOMA Techniques
7.1 Introduction
7.2 Non-Orthogonal Multiple Access (NOMA) System
7.2.1 Sparse Code Multiple Access (SCMA)
7.2.2 Pattern Division Multiple Access (PDMA)
7.2.3 Multiple-User Shared Access (MUSA)
7.3 Sequence Construction: Modify Frequency Hop Codes and Lagrange Sequences for MUSA and PDMA Systems
7.3.1 Sidelnikov, Legendre, and Complex Legendre Sequence Definition
7.3.2 Generalized Welch (GW) Shifting Sequence Construction
7.3.3 Construction of Short Patterns for PDMA and MUSA
7.4 Performance Comparison of Different PDMA Patterns
7.5 Conclusion
Acknowledgment
References
Chapter 8: Ambient Backscatter Communication: A Solution for Energy-Efficient 5G-Enabled IoT
8.1 Introduction
8.1.1 Types of BackCom Systems
8.1.2 Monostatic BackCom System (MBCS)
8.1.2.1 Bistatic BackCom System (BBCS)
8.1.2.2 Ambient BackCom System (ABCS)
8.1.3 Overview of BackCom Systems
8.1.4 Backscatter Transmitter
8.1.5 Backscatter Receiver
8.2 Broad Areas of BackCom Research
8.2.1 Signal Processing
8.2.1.1 Channel Coding
8.2.1.2 Interference
8.2.1.3 Channel Decoding
8.2.1.4 Signal Detection
8.2.2 BackCom: Wireless Communications
8.2.2.1 Modulation
8.2.2.2 Multiple Access Techniques in BackCom Systems
8.2.3 BackCom: Wireless Information and Power Transfer
8.2.4 Task Scheduling and Resource Allocation
8.3 Mathematical Aspects of ABCS
8.4 Upcoming Backscatter Communication Techniques
8.4.1 Visible Light BackCom Systems (VLBCS)
8.4.2 Relay-Assisted BackCom System
8.4.3 mm-wave-Based BackCom
8.4.4 Long Range (Lo-Ra) BackCom
8.4.5 Ultra-Wide Band (UWB) BackCom
8.4.6 Full-Duplex BackCom
8.4.7 Cognitive Radio Network (CRN) with BackCom
8.4.8 Non-Orthogonal Multiple Access (NOMA) in BackCom
8.5 Applications of BackCom
8.5.1 BackCom in Medical Science
8.5.2 BackCom for Smart Cities/Smart Homes
8.5.3 BackCom in Smart Factories
8.5.4 BackCom in Precision Agriculture
8.6 Open Research Issues
8.6.1 Interference Management
8.6.2 Physical Layer Security
8.6.3 Machine-Learning Algorithms
8.6.4 Achieving High Data Rates
8.7 Conclusion
References
Chapter 9: Deployment and Analysis of Random Walk and Random Waypoint Mobility Model for WSN-Assisted IoT Hierarchical Framework
9.1 Introduction
9.2 Related Work
9.3 System Model
9.3.1 Proposed Framework
9.3.2 Communication Constraints
9.3.2.1 Communication Constraints for Local Cluster
9.3.2.2 Communication Constraints across Clusters
9.3.3 Different Network Scenarios
9.3.4 Assumptions
9.3.5 Energy Model
9.3.6 Network Lifetime
9.4 Energy-Efficient Routing
9.5 Result Analysis and Discussion
9.6 Conclusion
References
Chapter 10: Multi-User Detection in Uplink Grant-Free NOMA with Dynamic Random Access Using Sinusoidal Sequences
10.1 Introduction
10.1.1 Background and Motivation
10.1.2 Related Works
10.1.3 Contribution
10.1.4 Notation
10.2 System Model
10.3 System Model with Sinusoidal Sequences
10.3.1 Signal Model for Sinusoidal Spreading Sequences
10.3.2 Sparse Signal Representation with Sinusoidal Sequence
10.4 SPICE-based AUD
10.4.1 Finding Active User Indices Using SPICE
10.4.2 Fast Computation of R using FFT
10.5 Subspace Estimation-Based Fast AUD
10.5.1 Estimating the Number of Active UEs
10.5.2 Estimating the Active UE Indices
10.6 User Activity Detection over RA Opportunity
10.6.1 Statistics Sufficient for User Activity Detection
10.6.2 Refining the Active User Set
10.7 Channel Estimation with Dynamic RA
10.7.1 Channel Estimation
10.7.2 Data Detection
10.7.3 Reliable Recovery of Transmitted Data Symbols
10.7.4 Summary of Proposed AUD, CE, and DD
10.7.5 Scope of Performance Improvement with Prior Noise and Channel Statistics
10.8 Numerical Analysis
10.8.1 Simulation Setup
10.8.2 Simulation Results
10.9 Conclusions
Proof of Lemma 1
Thresholds for SPICE
References
Chapter 11: 5G-Enabled IoT: Applications and Case Studies
11.1 Introduction
11.2 Emerging IoT Applications
11.2.1 Smart Healthcare System
11.2.1.1 Sensor Node Architecture
11.2.1.2 IoT-Based 5G-CCN Architecture
11.2.1.3 Small-Cell Technology in 5G
11.2.1.4 5G-Based Mobile Edge Computing
11.2.2 Smart Agriculture
11.2.2.1 Image Electronic Fence
11.2.2.2 IoT-Based Smart Fish Agriculture
11.2.2.3 AREThOU5A Project
11.2.3 Smart City
11.2.3.1 Smart Camera
11.2.3.2 Smart Grid
11.2.3.3 Intelligent Transportation System
11.2.3.4 Smart Malls
11.2.3.5 Smart Surveillance System
11.2.3.6 Smart Museums
11.2.4 Smart Home
11.2.4.1 Femtocell for Smart Home
11.2.4.2 Home Energy Management System
11.2.4.3 Distributed Mobility Management
11.2.5 Industrial Automation
11.2.5.1 5G-Based Network Slicing
11.2.5.2 Smart Manufacturing
11.2.5.3 Smart Mining Industry
11.2.5.4 Wireless Industrial Automation
11.3 Open Issues and Challenges
11.4 Conclusion
References
Chapter 12: Hands-On Practice Tools for 5G and IoT
12.1 Introduction
12.1.1 5G Mobile Communications
12.1.2 Internet of Things (IoT) Technology
12.2 Challenges and Opportunities
12.2.1 5G Challenges and Opportunities
12.2.1.1 Challenges
12.2.1.2 Opportunities
12.2.2 IoT Challenges and Opportunities
12.2.2.1 Challenges
12.2.2.2 Opportunities
12.3 Paradigms for 5G and IoT Tools
12.3.1 Adaptive IP
12.3.1.1 Basic
12.3.1.2 Installation Requirements
12.3.2 5G Automation
12.3.2.1 Basic
12.3.2.2 Installation Requirements
12.3.3 OpenBalena
12.3.3.1 Basic
12.3.3.2 Installation Requirements
12.3.3.3 Commands
12.4 Conclusions
References
Index