Paradigms of Smart and Intelligent Communication, 5G and Beyond

Preface
Contents
About the Editors
1 Artificial Cognitive Computing for Smart Communications, 5G and Beyond
1.1 Introduction
1.2 The Cognitive Computing Components
1.3 The Architecture of Cognitive
1.4 Cognitive Computing for Smart Communications
1.4.1 The Cognitive Computing for the Society—Use Cases
1.4.2 The Cognitive Analytics as Parts of Cognitive Computing
1.5 Impact of Covid-19 on Cognitive Computing Market 6 Cooperative and Cognitive Network for 5G Network
1.6 Challenges and Future Aspects of Cognitive Computation on 5G and Communication
1.7 Summary
References
2 Green IoT Networks Using Machine Learning, Deep Learning for 5G Networks
2.1 Introduction
2.2 Recent Advances in 5G IoT Ecosystem
2.3 Green IoT Enabling Technologies
2.4 IoT Ecosystem Energy Management Techniques
2.4.1 Power Saving Techniques
2.4.2 Power Gathering Methods
2.5 Energy Management in IoT Cloud Computing Techniques
2.5.1 Cloud Computing (CC)
2.5.2 Fog Computing (FC)
2.5.3 Edge Computing (EC)
2.6 Savvy Power Management Techniques for Internet of Things
2.6.1 Machine Learning
2.6.2 Deep Learning
2.7 Application of Energy Management in Various IoT Applications
2.7.1 Smart Home
2.7.2 Agriculture
2.7.3 Healthcare
2.7.4 Industrial IoT (IIoT)
2.8 Summary
References
3 Integration of IoT and 5G Communication
3.1 Introduction
3.1.1 The Advantages of 5G
3.1.2 Enable Factors of 5G for IoT
3.2 5G Applications in IoT
3.3 Technological Development with a 5G Antenna
3.4 Summary
References
4 Role of IoT and Antenna Array in Smart Communication and 5G
4.1 Introduction
4.2 Basic Structure of IoT with Its Protocols
4.2.1 Constrained Application Protocol (CoAP)
4.2.2 Message Queue Telemetry Transport Protocol (MQTT)
4.2.3 Advanced Message Queuing Protocol (AMQP)
4.2.4 Data Distribution Service (DDS) Protocol
4.3 Employment of IoT and Antenna Array in 5G
4.4 Design and Simulation of Antenna and Antenna Array Suitable for 5G
4.4.1 Design of 5 GHz Circular Patch Antenna
4.4.2 Design of 5 GHz 2 × 2 Microstrip Patch Antenna Finite Array
4.5 Applications and Examples of IoT in the Smart Communication and 5G
4.5.1 Role of Smart Communication Technologies for Smart Retailing
4.5.2 Impact of IoT on 5G
4.5.3 5G Challenges
4.6 Application of 5G Over IoT in the Different Areas
4.6.1 Automated Self-driving Cars and Other Vehicles
4.6.2 Smart Automated Healthcare
4.6.3 Smart Logistics and Supply Chain Management
4.6.4 Clean and Smart Cities and Town
4.6.5 Smart Marketing and Retail or Chain Store
4.6.6 Intelligent Automotive and Smart Industries
4.6.7 Smart Agriculture
4.6.8 Establishment Between 5G and IoT Eco-system
4.7 Future Enhancement in 5G Using Antenna Array
References
5 Machine Learning and Deep Reinforcement Learning in Wireless Networks and Communication Applications
5.1 Introduction
5.1.1 Deep Learning
5.1.2 Reinforcement Learning (RL)
5.1.3 Deep Reinforcement Learning (DRL)
5.1.4 From the RL to the DRL
5.1.5 Machine Learning (ML)
5.2 Applications Deep Reinforcement Learning Techniques
5.2.1 Application in Wireless Network
5.3 DRL Applications for Future-Generation Mobile Networks
5.3.1 Power Management and Power Control
5.4 Future Prediction of the Wireless Networks
5.5 Wireless Mobile Communications and the Future of the Indian Cellular Market
5.5.1 The Growth Factor of the Telecom Sector in India
5.5.2 Methodology Used in the Overall World
5.5.3 Market Size Especially in India
5.5.4 Growth Factor of Telecommunication in India
5.5.5 Major Market Players or Companies of Telecommunication in India
5.6 Summary
References
6 Detection of Consumption of Alcohol Using Artificial Intelligence
6.1 Introduction
6.2 Ways to Detect Consumption of Alcohol
6.2.1 Breathalyzer
6.2.2 Identification Through Infrared Face Images
6.3 Methodology
6.3.1 Using IR Sensor Thermal Imaging Cameras
6.3.2 Using Breathalyzers
6.4 Summary
References
7 Application of Machine Learning in Finger Vein Pattern Recognition
7.1 Introduction
7.1.1 Literature Survey
7.1.2 Problem Formulation
7.2 Methodology
7.2.1 Feature Withdrawal Techniques
7.3 Calculation and Verification of Accuracy
7.3.1 Machine Learning Algorithm
7.4 Results and Discussion
7.4.1 Accuracy and Calculation
7.5 Results Analysis
7.6 Summary
References
8 Machine Learning Techniques for Anomaly Detection Application Domains
8.1 Introduction
8.2 Anomaly: What Is It?
8.2.1 Point Anomalies
8.2.2 Contextual Anomalies
8.2.3 Collective Anomalies
8.3 Aspects of Anomaly Detection and Challenges
8.3.1 Aspects of Anomaly Detection
8.3.2 Challenges Faced in Anomaly Detection
8.4 Application Domains
8.4.1 Medical and Public Health Anomaly Detection
8.4.2 Intrusion Detection
8.4.3 Industrial Damage Detection
8.4.4 Fault Detection in Mechanical Units
8.4.5 Structural Defect Detection
8.4.6 Fraud Detection
8.4.7 Sensor Networks
8.4.8 Image Processing
8.4.9 Text Data
8.4.10 Data Leakage Prevention
8.5 Anomaly Detection Techniques
8.5.1 Supervised Methods
8.5.2 UnSupervised Methods
8.6 Pros and Cons of Supervised and Unsupervised Techniques
8.7 Summary
References
9 Application of AI & ML in 5G Communication
9.1 Introduction
9.2 Evolution from 1 to 5G
9.3 5th Generation Wireless Network Technology
9.4 5G Wireless Networks Security
9.5 Impact of AI/ML in 5G Wireless Network Technology
9.6 Role of AI on 5G Networks
9.6.1 Relevance of 5G to the Field of AI
9.7 5G Security: AI/ML Applications
9.8 Machine Learning for 5G Technology: A Case Study
9.8.1 Deep Convolutional Neural Networks Application to Detect Signal Modulation Types
9.8.2 Modulation Recognition
9.9 Modulation Classifier Consideration & Model Architecture
9.10 Results Analysis
9.11 Challenges and Future of 5G Wireless Technology
9.11.1 ML Servies for 5G Wireless Communications Include
9.11.2 Challenges for ML Application in 5G Technology
9.12 Summary
References
10 Software Defined Network-Based Management Architecture for 5G Network
10.1 Introduction
10.2 Software Defined Network
10.2.1 SDN Architecture
10.2.2 SDN Management Architecture
10.2.3 How SDN Works
10.2.4 Benefits of SDN
10.3 5G Mobile Network
10.3.1 5G Architecture
10.3.2 Features of 5G Mobile Technology
10.3.3 How 5G Works
10.3.4 Challenges in 5G Network
10.4 SDN Implementation in 5G Mobile Network
10.4.1 SDN Management Architecture (Proposed Approach)
10.4.2 SDN Management Architecture Operation
10.4.3 SDN-Based Management for 5G Mobile Network
10.4.4 SDN Benefits for 5G
10.5 Conclusion and Future Work (Summary)
References
11 Reversible Logic Based Single Layer Flip Flops and Shift Registers in QCA Framework for the Application of Nano-communication
11.1 Introduction
11.2 Preliminary Overview
11.2.1 Reversible Logic
11.2.2 QCA Background
11.3 QCA Layout of Reversible Fredkin Gate—A Novel Approach
11.3.1 Fault Characterization
11.3.2 Energy Dissipation Analysis of the Presented QCA Structure
11.3.3 QCA Layout of Fredkin Gate with 2D Clocking Scheme
11.4 Proposed Reversible QCA Circuits
11.4.1 QCA Based Reversible D Latch
11.4.2 QCA Based Reversible Master Slave D Flip Flop
11.4.3 QCA Based Reversible DET Flip Flop
11.4.4 Design of Proposed Reversible Shift Registers
11.5 Performance Analysis of Proposed Reversible QCA Circuits
11.6 Summary
References
12 Machine Learning Technique for Few-Mode Fiber Design with Inverse Modelling for 5G and Beyond
12.1 Introduction
12.1.1 Optical Fiber in 5G and Beyond
12.1.2 Types of Fiber Used in 5G Networks
12.1.3 Role of Few-Mode Fiber in 5G Networks
12.1.4 State-Of-Art in the Design of Weakly-Coupled FMFs
12.1.5 Machine-Learning in FMF Design
12.2 Proposed FMF Structure
12.2.1 T-FMF Structure
12.2.2 Design Methodology
12.2.3 Machine Learning Model
12.3 Discussion of Proposed model with RMSE and MSE
12.4 Summary
References
13 IoT for Landslides: Applications, Technologies and Challenges
13.1 Introduction
13.2 Related Concepts
13.2.1 Internet of Things
13.2.2 IoT Application for Landslide Prevention
13.3 IoT Technology for Landslide Studies
13.3.1 Overview
13.3.2 Sensor Network
13.3.3 Fibre Optic Sensing Technology
13.3.4 Cloud Computing Platform
13.4 Challenges with IoT-Based Monitoring System
13.5 Summary
References
14 A Review: Dust Cleaning Approach of Solar Photovoltaic System Using IOT & ML
14.1 Introduction
14.2 Natural Cleaning System
14.3 Manual Cleaning System
14.4 Mechanical Cleaning Techniques
14.5 Sprinkle System
14.6 Cleaning Approach Based on IOT
14.7 Cleaning Approach Based on Machine Learning
14.8 Summary
References
15 Prediction of Heart Disease Using Hybrid Machine Learning Technique
15.1 Introduction
15.2 Related Work
15.3 Methodology and Data Set Analysis
15.3.1 Experimental Procedures
15.4 Feature Engineering
15.4.1 Performance Analysis
15.5 Predictive Analysis
15.6 Conclusion
References