Artificial Intelligent Techniques for Wireless Communication and Networking (2022) [Kanthavel et al] [9781119821274]

• Year: 2022
• Tongue: English
• Leaves: 361
• Category: Library

✦ Table of Contents

Cover
Half-Title Page
Series Page
Title Page
Copyright Page
Contents
Preface
1 Comprehensive and Self-Contained Introduction to Deep Reinforcement Learning
1.1 Introduction
1.2 Comprehensive Study
1.2.1 Introduction
1.2.2 Framework
1.2.3 Choice of the Learning Algorithm and Function Approximator Selection
1.2.3.1 Auxiliary Tasks
1.2.3.2 Modifying the Objective Function
1.3 Deep Reinforcement Learning: Value-Based and Policy-Based Learning
1.3.1 Value-Based Method
1.3.2 Policy-Based Method
1.4 Applications and Challenges of Applying Reinforcement Learning to Real-World
1.4.1 Applications
1.4.2 Challenges
1.5 Conclusion
References
2 Impact of AI in 5G Wireless Technologies and Communication Systems
2.1 Introduction
2.2 Integrated Services of AI in 5G and 5G in AI
2.2.1 5G Services in AI
2.2.1.1 Next-Generation Edge Convergence With AI Systems on Chip
2.2.1.2 Massive Device Concurrency Replenishing AI Data Lakes in Real Time
2.2.1.3 Ultra-Fast, High-Volume Streaming for Low-Latency AI
2.2.2 AI Services in 5G
2.2.2.1 Distributed AI
2.2.2.2 AI for IT Operations (AIOps)
2.2.2.3 Network Slicing
2.2.3 Evolution With AI in the 5G Era
2.2.3.1 Agile Network Construction
2.2.3.2 Intelligent Operations and Management
2.2.3.3 Smart Operations
2.3 Artificial Intelligence and 5G in the Industrial Space
2.4 Future Research and Challenges of Artificial Intelligence in the Mobile Networks
2.4.1 Research Directions
2.4.1.1 AI Is Being Adopted Into Mobile Networks by Communication Service Provider Now
2.4.1.2 AI and Customer Experience
2.4.1.3 Recouping the Network Investments That 5G Demands
2.4.1.4 Data Challenges Presented by Artificial Intelligence Adoption
2.4.1.5 Network Intelligence and Automation
2.4.2 Challenges to a 5G-Powered AI Network
2.4.2.1 Dealing With Interference
2.4.2.2 Dealing With Latency
2.4.2.3 Solving Latency
2.5 Conclusion
References
3 Artificial Intelligence Revolution in Logistics and Supply Chain Management
3.1 Introduction
3.2 Theory—AI in Logistics and Supply Chain Market
3.2.1 AI Impacts
3.2.2 Revolutionizing Global Market
3.2.3 Role of AI
3.2.4 AI Trends in Logistics
3.2.4.1 Anticipatory Logistics
3.2.4.2 Self-Learning Systems
3.2.5 AI Trends in Supply Chain
3.3 Factors to Propel Business Into the Future Harnessing Automation
3.3.1 Logistics
3.3.1.1 Predictive Capabilities
3.3.1.2 Robotics
3.3.1.3 Big Data
3.3.1.4 Computer Vision
3.3.1.5 Autonomous Vehicles
3.3.2 Supply Chain
3.3.2.1 Bolstering Planning & Scheduling Activities
3.3.2.2 Intelligent Decision-Making
3.3.2.3 End-End Visibility
3.3.2.4 Actionable Analytical Insights
3.3.2.5 Inventory and Demand Management
3.3.2.6 Boosting Operational Efficiencies
3.4 Conclusion
References
4 An Empirical Study of Crop Yield Prediction Using Reinforcement Learning
4.1 Introduction
4.2 An Overview of Reinforcement Learning in Agriculture
4.2.1 Reinforcement Terminology and Definitions
4.2.2 Review on Agricultural Reinforcement Learning
4.3 Reinforcement Learning Startups for Crop Prediction
4.3.1 Need for Crop Prediction
4.3.2 Reinforcement Learning Impacts on Agriculture
4.3.3 Deep Q Networks for Crop Prediction
4.4 Conclusion
References
5 Cost Optimization for Inventory Management in Blockchain and Cloud
5.1 Introduction
5.2 Blockchain: The Future of Inventory Management
5.2.1 Issues Faced in Inventory Management
5.2.2 Inventory Management Scenario
5.2.3 A Primer on Blockchain Technology
5.2.4 Blockchain for Proactive Inventory Management
5.3 Cost Optimization for Blockchain Inventory Management in Cloud
5.3.1 Optimizing Blockchain Inventory Management
5.3.2 Best Practices of Blockchain Inventory Cost Optimization in Cloud
5.3.2.1 Criticality Analysis
5.3.2.2 Demand Forecasting
5.3.2.3 Lead Time Forecasting
5.3.2.4 Issue Size Forecasting
5.3.2.5 Economic Modeling
5.3.2.6 Optimization of Reordering Parameters
5.3.2.7 Exception Management
5.3.2.8 Inventory Segmentation
5.3.2.9 Spares Risk Assessment
5.3.2.10 Spares Pooling
5.3.2.11 Knowledge Capture
5.3.2.12 Reporting Inventory
5.4 Cost Reduction Strategies in Blockchain Inventory Management in Cloud
5.4.1 Reduce Useless Inventory
5.4.2 Let Experts Manage Inventory (VMI)
5.4.3 Develop Relationships With Suppliers
5.4.4 Install Industrial Vending Machines
5.4.5 Order Smaller, More Frequently
5.5 Conclusion
References
6 Review of Deep Learning Architectures Used for Identification and Classification of Plant Leaf Diseases
6.1 Introduction
6.2 Literature Review
6.3 Proposed Idea
6.4 Reference Gap
6.5 Conclusion
References
7 Generating Art and Music Using Deep Neural Networks
7.1 Introduction
7.1.1 Traditional Approach
7.1.2 Modern Computing Approach
7.2 Related Works
7.2.1 Feeling Investigation on Social Media
7.3 System Architecture
7.3.1 Art Module
7.3.2 Music Module
7.4 System Development
7.4.1 Upload Module
7.4.2 Conversion Module
7.4.3 Transformation Module
7.4.4 Loading Dataset Module
7.4.5 Optimizing Module
7.4.5.1 Calculating Loss Function
Lcontent(c,x) & Lstyle(s,x)
7.5 Algorithm-LSTM
7.6 Result
7.6.1 Sample Input and Output
7.7 Conclusions
References
8 Deep Learning Era for Future 6G Wireless Communications—Theory, Applications, and Challenges
8.1 Introduction
8.2 Study of Wireless Technology
8.2.1 Overview
8.2.2 Background Study
8.2.3 6G Wireless Technology
8.2.4 Influence of AI in 6G Wireless Communication
8.3 Deep Learning Enabled 6G Wireless Communication
8.3.1 Deep Learning Techniques for 6G Networks
8.3.2 Predicted Services in the 6G Era
8.3.2.1 Internet-of-(Every)Thing
8.3.2.2 Connected Vehicles
8.3.2.3 Smart Cities
8.3.2.4 Robotics and Industry
8.4 Applications and Future Research Directions
Conclusion
References
9 Robust Cooperative Spectrum Sensing Techniques for a Practical Framework Employing Cognitive Radios in 5G Networks
9.1 Introduction
9.2 Spectrum Sensing in Cognitive Radio Networks
9.3 Collaborative Spectrum Sensing for Opportunistic Access in Fading Environments
9.4 Cooperative Sensing Among Cognitive Radios
9.5 Cluster-Based Cooperative Spectrum Sensing for Cognitive Radio Systems
9.6 Spectrum Agile Radios: Utilization and Sensing Architectures
9. 7 Some Fundamental Limits on Cognitive Radio
9.8 Cooperative Strategies and Capacity Theorems for Relay Networks
9.9 Research Challenges in Cooperative Communication
9.10 Conclusion
References
10 Natural Language Processing
10.1 Introduction
10.2 Conclusions
References
11 Class Level Multi-Feature Semantic Similarity-Based Efficient Multimedia Big Data Retrieval
11.1 Introduction
11.2 Literature Review
11.3 Class Level Semantic Similarity-Based Retrieval
11.4 Results and Discussion
11.4.1 Performance Analysis vs Number of Classes
11.4.2 Performance Analysis vs. Number of Terms/Relations
Conclusion
References
12 Supervised Learning Approaches for Underwater Scalar Sensory Data Modeling With Diurnal Changes
12.1 Introduction
12.2 Literature Survey
12.2.1 Underwater Channel Models
12.3 Proposed Work
12.3.1 Statistical Analysis Using Software Tools
12.4 Results
12.4.1 Statistical Works
12.4.1.1 Auto Correlation and Partial Auto Correlation Analysis of Depth and Temperature
12.4.2 Attenuation Results
12.5 Conclusion and Future Work
References
13 Multi-Layer UAV Ad Hoc Network Architecture, Protocol and Simulation
13.1 Introduction
13.1.1 Flying Ad Hoc Networks (FANETs)
13.1.2 Multi-Layer-Based UAVs Ad Hoc Network
13.2 Background
13.3 Issues and Gap Identified
13.4 Main Focus of the Chapter
13.5 Mobility
13.5.1 Mobility Model
13.5.2 Reference-Point-Group Mobility Model (RPGM)
13.5.3 Spatial Dependency-Based Mobility Model
13.5.3.1 Degree of Spatial-Dependency
13.6 Routing Protocol
13.6.1 Data-Centric-Routing-Protocol (DCRP)
13.7 High Altitude Platforms (HAPs)
13.7.1 Characteristics of HAP
13.7.2 Advantages of HAPs
13.8 Connectivity Graph Metrics
13.8.1 Link Changes Counts
13.8.2 Link Duration
13.8.3 Path-Availability
13.9 Aerial Vehicle Network Simulator (AVENs)
13.10 Conclusion
References
14 Artificial Intelligence in Logistics and Supply Chain
14.1 Introduction to Logistics and Supply Chain
Agriculture
Manufacturing
Artificial Intelligence (AI) Tools
Transportation
14.1.1 Elements of Supply Chain Network
14.1.2 Supply Chain Performances and Costing
14.1.2.1 Performance Measure
14.1.2.2 Procurement Cost
14.1.2.3 Transportation Cost
14.1.2.4 Inventory Cost
14.2 Recent Research Avenues in Supply Chain
14.2.1 Vendor Selection
14.2.2 Transportation
14.2.3 Inventory Routing
14.2.4 Agent-Based Modeling
14.2.5 Reverse Logistics
14.3 Importance and Impact of AI
14.3.1 Benefits
14.3.2 Challenges
14.3.2.1 Supply Chain Risk Estimation
14.3.2.2 Green Supply Chain (GSC)
14.4 Research Gap of AI-Based Supply Chain
14.4.1 Healthcare
14.4.1.1 Pharmaceutical
14.4.1.2 Medical Devices
14.4.1.3 Fast Moving Consumer Goods
14.4.2 Networked Manufacturing
14.4.3 Humanitarian Supply Chain Network
Conclusion
References
15 Hereditary Factor-Based MultiFeatured Algorithm for Early Diabetes Detection Using Machine Learning
15.1 Introduction
15.1.1 Role of Data Mining Tools in Healthcare for Predicting Various Diseases
15.2 Literature Review
15.3 Objectives of the Proposed System
15.4 Proposed System
15.5 HIVE and R as Evaluation Tools
15.6 Decision Trees
15.7 Results and Discussions
15.8 Conclusion
References
16 Adaptive and Intelligent Opportunistic Routing Using Enhanced Feedback Mechanism
16.1 Introduction
16.2 Related Study
16.3 System Model
16.3.1 Dividing Packets Into Blocks
16.3.2 Packet Transmission
16.3.3 Feedback
16.4 Experiments and Results
16.5 Conclusion
References
17 Enabling Artificial Intelligence and Cyber Security in Smart Manufacturing
17.1 Introduction
17.2 New Development of Artificial Intelligence
17.3 Artificial Intelligence Facilitates the Development of Intelligent Manufacturing
17.4 Current Status and Problems of Green Manufacturing
17.4.1 Green Manufacturing
17.4.2 Current Status and Major Problems
17.4.2.1 Isolation of Information Among Multiple Fields
17.4.2.2 Diverse Information Types and Different Kinds of Data
Lack of a Process-Safety-Oriented Decision-Making System
Lack of Early Warning and Risk Tracing Systems
17.5 Artificial Intelligence for Green Manufacturing
17.5.1 Information Integration via Knowledge Graphs
17.5.1.1 Process-Safety Information Extraction
17.5.1.2 Process-Safety Knowledge Fusion
17.5.1.3 Process-Safety Knowledge Processing
17.5.2 Risk Assessment and Decision-Making Using Bayesian Networks
17.5.3 Incident Early Warning Based on Deep Learning
17.6 Detailed Description of Common Encryption Algorithms
17.6.1 Triple DES (3DES)—(Triple Data Encryption Standard)
17.7 Current and Future Works
17.8 Conclusion
References
18 Deep Learning in 5G Networks
18.1 5G Networks
18.2 Artificial Intelligence and 5G Networks
18.3 Deep Learning in 5G Networks
Conclusion
References
19 EIDR Umpiring Security Models for Wireless Sensor Networks
19.1 Introduction
19.2 A Review of Various Routing Protocols
19.2.1 Trust-Based Routing Protocols
19.2.2 Intrusion Detection System
19.2.3 The Network Layer Routing Protocols
19.2.3.1 WRP
19.2.3.2 AODV
19.2.3.3 LEACH Protocols
19.3 Scope of Chapter
19.3.1 Objective
19.3.2 Contributions
19.3.3 Performance Evaluations
19.4 Conclusions and Future Work
References
20 Artificial Intelligence in Wireless Communication
20.1 Introduction
20.2 Artificial Intelligence: A Grand Jewel Mine
20.3 Wireless Communication: An Overview
20.4 Wireless Revolution
20.5 The Present Times
20.6 Artificial Intelligence in Wireless Communication
20.6.1 How the Two Worlds Collided
20.6.2 Cognitive Radios
20.7 Artificial Neural Network
20.8 The Deployment of 5G
20.9 Looking Into the Features of 5G
20.10 AI and the Internet of Things (IoT)
20.11 Artificial Intelligence in Software-Defined Networks (SDN)
20.12 Artificial Intelligence in Network Function Virtualization
20.13 Conclusion
References
Index
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