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Submit a ProposalEnergy-Efficient Communication Networks
One Line Description
Energy-Efficient Communication Networks is essential for anyone looking to understand and implement cutting-edge energy optimization strategies for communication systems, ensuring they meet growing energy demands while seamlessly integrating renewable energy sources and enhancing battery life in embedded applications.
Audience
Students, educators, researchers, engineers, and industry professionals working to improve energy in the information and technology sector
Students, educators, researchers, engineers, and industry professionals working to improve energy in the information and technology sector
Description
Renewable energy, including solar, wind, and geothermal energy, for communication networks is a key area of exploration for meeting the demands of their increasing energy requirements. Scheduling and power cycle optimization are instrumental in deciding the effectiveness of these networks. Apart from communication, embedded systems running on batteries designed for data processing applications also face restrictions in terms of battery life—targeting low-energy consumption-based systems is particularly important here. The increased usage of sensor networks for personal and commercial applications has resulted in a surge of development to create energy-aware protocols and algorithms.
Energy-Efficient Communication Networks introduces energy optimization concepts for current and future communication networks and explains how to optimize electricity for wireless sensor networks and incorporate renewable energy sources into conventional communication networks. It gives readers a better understanding of the difficulties, limitations, and possible bottlenecks that may occur while developing a communication system under power constraints, as well as insights into the traditional and recently developed communication systems from an energy optimization point of view.
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Renewable energy, including solar, wind, and geothermal energy, for communication networks is a key area of exploration for meeting the demands of their increasing energy requirements. Scheduling and power cycle optimization are instrumental in deciding the effectiveness of these networks. Apart from communication, embedded systems running on batteries designed for data processing applications also face restrictions in terms of battery life—targeting low-energy consumption-based systems is particularly important here. The increased usage of sensor networks for personal and commercial applications has resulted in a surge of development to create energy-aware protocols and algorithms.
Energy-Efficient Communication Networks introduces energy optimization concepts for current and future communication networks and explains how to optimize electricity for wireless sensor networks and incorporate renewable energy sources into conventional communication networks. It gives readers a better understanding of the difficulties, limitations, and possible bottlenecks that may occur while developing a communication system under power constraints, as well as insights into the traditional and recently developed communication systems from an energy optimization point of view.
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Author / Editor Details
Shakti Raj Chopra, PhD is an associate professor at Lovely Professional University with over 18 years of academic experience. He has published over 45 research papers in international journals and conferences. Additionally, he has worked on several consultancy projects and participated in over 20 national and international webinars. His areas of interest include cognitive radio, blockchain, artificial intelligence, and machine learning.
Shakti Raj Chopra, PhD is an associate professor at Lovely Professional University with over 18 years of academic experience. He has published over 45 research papers in international journals and conferences. Additionally, he has worked on several consultancy projects and participated in over 20 national and international webinars. His areas of interest include cognitive radio, blockchain, artificial intelligence, and machine learning.
Krishan Arora, PhD is an associate professor and Head of the Department of Power Systems in the School of Electronics and Engineering at Lovely Professional University with over 16 years of academic experience. He has published over 70 research papers in international journals and conferences, organized several workshops, internships, and lectures, and participated in over 20 nationla and international webinars. His areas of research include electrical machines, non-conventional energy sources, load frequency control, and automatic generation control.
Suman Lata Tripathi, PhD is a professor at Lovely Professional University with more than 20 years of academic experience. She has published over 74 research papers in international journals, 13 patents, two copyrights, and has authored and edited over 17 books. She also serves as a session chair, conference-steering committee member, editorial board member, and peer reviewer for international journals. Her areas of interest include microelectronics device modeling and characterization, low-power VLSI circuit design, and embedded system design.
Vikram Kumar, PhD is a post-doctoral researcher and guest lecturer at the University of Calgary, Canada. He has published 82 research papers in international journals, three patents, and four copyrights. Additionally, he has presented research work in over two dozen national and international conferences and serves as an editorial board member of several journals. His areas of interest include multidisciplinary design and optimization, artificial intelligence and machine learning for numerical and engineering optimization, and information and communication technology for smart grid applications.
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Table of Contents
Preface
Contributor Lists
1. Efficient Energy Management in Hyperledger Fabric Blockchain Networks: A Proposed Optimized Solution
Kamurthi Ravi Teja and Shakti Raj Chopra
1.1 Introduction
1.2 Methodology
1.3 Experimental Analysis
1.3.1 Existing Problem in the Network
1.3.2 Proposed Hyperledger Fabric Network Approach
1.4 Results and Discussion
1.5 Conclusion
References
2. Framework for UAV-Based Wireless Power Harvesting
Tanishk Singhal and Harpreet Singh Bedi
2.1 Introduction
2.2 Literature Review
2.2.1 Proposed Framework
2.2.2 Integration with UAV Systems
2.2.3 Methodology
2.3 Results and Discussion
2.4 Conclusion
References
3. Future Generation Technology and Feasibility Assessment
Pradeep Singh, Krishan Arora and Umesh C. Rathore
3.1 Introduction
3.1.1 Technological Breakthroughs
3.1.2 Economic Viability and Feasibility
3.1.3 Regulatory Environments
3.1.4 Atmospheric Reliability
3.1.5 Customer Requirements
3.1.6 Societal Acceptability
3.2 Next-Generation Electrical Technologies
3.2.1 Smart Grids
3.2.1.1 Components and Features
3.2.1.2 Advantages
3.2.1.3 Challenges
3.2.2 Renewable Energy Integration
3.2.2.1 Grid Integration
3.2.2.2 Power Electronics and Control System
3.2.2.3 Energy Storage
3.2.2.4 Transmission and Distribution
3.2.2.5 Challenges
3.2.3 Energy Storage
3.2.3.1 Types of Energy Storage
3.2.3.2 Applications of Energy Storage
3.2.3.3 Advancements and Challenges
3.2.4 Electric Vehicles
3.2.4.1 Types of Electric Vehicles
3.2.4.2 Key Components and Systems
3.2.4.3 Challenges
3.2.5 Power Electronics
3.2.5.1 Components and Systems
3.2.5.2 Applications
3.2.5.3 Challenges and Future Trends
3.2.6 Internet of Things (IoT) and Connectivity
3.2.6.1 Internet of Things (IoT)
3.2.6.2 Connectivity in Electrical Engineering
3.2.6.3 Advantages and Challenges
3.3 Artificial Intelligence
3.3.1 Types of Artificial Intelligence
3.3.1.1 Type I
3.3.1.2 Type II (Based on Functionalities)
3.3.2 Applications of AI in Electrical Engineering
3.3.2.1 Design and Development
3.3.2.2 Predictive Maintenance
3.3.2.3 Power System and Grid Management
3.3.2.4 Automation and Control Systems
3.3.2.5 Energy Efficiency
3.4 Machine Learning
3.4.1 Types of Machine Learning
3.4.1.1 Supervised Machine Learning
3.4.1.2 Unsupervised Machine Learning
3.4.1.3 Semi-Supervised Learning
3.4.1.4 Reinforcement Learning
3.4.2 Applications of Machine Learning in Electrical Engineering
3.4.2.1 Predictive Maintenance
3.4.2.2 Power System Optimization
3.4.2.3 Control Systems and Optimization
3.4.2.4 Energy Efficiency
3.4.2.5 Design and Development
3.5 Conclusion
References
4. IoT-Enabled Weather Forecasting Systems in Future Networks: Constraints and Solutions
Yogesh Kumar Verma, Archana Kanwar and Manoj Kumar Shukla
4.1 Introduction
4.2 Need of IoT-Based Weather Forecasting System
4.3 Methodology and Results
4.4 Conclusion
References
5. Cognitive Radio-Based NOMA Communication Networks
Indu Bala
5.1 Introduction to Cognitive Radio and NOMA Networks
5.1.1 Motivation for Integrating Cognitive Radio with NOMA
5.2 Fundamentals of Cognitive Radio Technology
5.2.1 Spectrum Sensing Techniques in Cognitive Radio
5.2.2 Dynamic Spectrum Access (DSA)
5.2.3 Spectrum Management
5.2.4 Cognitive Radio Architectures and Protocols
5.3 Principles of Non-Orthogonal Multiple Access (NOMA)
5.3.1 Orthogonal Multiple Access versus NOMA
5.3.2 NOMA Techniques and Variants
5.3.3 Advantages and Challenges of NOMA Networks
5.4 Integration of Cognitive Radio with NOMA
5.4.1 Cognitive Radio Capabilities and Spectrum Sensing in NOMA Networks
5.4.2 Spectrum-Sharing Techniques in Cognitive Radio-NOMA Systems
5.4.3 Cognitive Radio-NOMA Architecture and Protocol Stack
5.4.4 Resource Allocation and Management in Cognitive Radio-NOMA Networks
5.4.4.1 Power Allocation and Control Strategies
5.4.4.2 Spectrum Sensing and Dynamic Spectrum Access in NOMA-CR Networks
5.4.4.3 QoS Provisioning and Optimization Techniques
5.5 Performance Evaluation and Analysis
5.5.1 Metrics for Assessing Cognitive Radio-NOMA Networks
5.5.2 Simulation and Modeling Approaches
5.6 Applications and Use Cases
5.6.1 Cognitive Radio-NOMA in Next-Generation Wireless Systems
5.6.2 Internet of Things (IoT) and Machine-to-Machine (M2M) Communications
5.6.3 Vertical Industry Applications
5.7 Challenges and Future Directions
5.7.1 Interference Management and Coexistence Issues
5.7.2 Security and Privacy Concerns in Cognitive Radio-NOMA Systems
5.7.3 Emerging Trends and Future Research Directions
5.8 Conclusion
References
6. Cognitive Radio (CR) Based Non-Orthogonal Multiple Access (NOMA) Network
Raja Gunasekaran, Ragavi Boopathi, Gobinath Velu Kaliyannan, Dinesh Dhanabalan and Kesavan Duraisamy
6.1 Introduction
6.2 Fundamentals of CR
6.2.1 Spectrum Hole Approach
6.2.2 Physical Layout of CR
6.2.3 Characteristics of CR
6.2.3.1 Cognitive Capability
6.2.3.2 Reconfigurability
6.2.4 CR Paradigms
6.2.5 Multiple Access Scheme
6.3 Spectrum Management System
6.3.1 Spectrum Sensing
6.3.2 Spectrum Decision
6.3.3 Spectrum Sharing
6.3.4 Spectrum Mobility
6.4 Noma Networks
6.4.1 NOMA Classification
6.4.1.1 PD-NOMA
6.4.1.2 CD-NOMA
6.4.2 OMA vs. NOMA
6.4.3 Downlink NOMA
6.4.4 Uplink NOMA
6.4.5 CR-Based NOMA Network
6.5 Enabling Technologies
6.5.1 Millimeter Wave (mmWave)
6.5.2 Intelligent Reflecting Surfaces (IRS)
6.5.3 Simultaneous Wireless Information and Power Transfer (SWIPT)
6.5.4 Cooperative CR-Based NOMA Systems
6.5.5 Satellite Communication (SatCom) CR-Based NOMA Systems
6.6 Conclusion
References
7. Artificial Intelligence and Machine Learning-Based Network Power Optimization Schemes
Jyoti, Aarti Shar, Ramandeep Sandhu, Manish Kumar Sharma and Deepika Ghai
7.1 Introduction
7.2 Network
7.2.1 Working of Network
7.2.1.1 Client-Server Architecture
7.2.1.2 Network Protocols
7.2.1.3 Network Addresses
7.2.2 Network Methods
7.2.2.1 Wireless vs. Wired
7.2.2.2 Network Range
7.3 Decentralized Connection
7.4 Communication Network
7.4.1 Types of Communication Networks
7.4.2 Components of Communication Networks
7.4.3 Communication Protocols
7.4.4 Communication Medium
7.5 Internet of Things (IoT)
7.6 5G and Future Technologies
7.7 Network Power and Unstable Power Supply of Computer Networks
7.8 Adaption of Optimization Schemes to Enhance Network Power
7.9 Related Work
7.10 Traditional Evaluation AI and ML-Based Network Energy Optimization Techniques
7.11 AI- and ML-Based Systems for Network Energy Optimization Techniques
7.11.1 Problem Definition and Objectives
7.12 Conclusion
References
8. Integration of PV Solar Rooftop Technology for Enhanced Performance and Sustainability of Electric Vehicles: A Techno-Analytical Approach
Vinay Anand and Himanshu Sharma
8.1 Introduction
8.1.1 Electric Vehicle
8.2 Literature Review
8.2.1 Numerous Challenges Faced by Electric Vehicles
8.3 Methods and Methodology
8.3.1 Structure of an Electric Vehicle Driven by Induction Motor
8.3.1.1 Solar Panel
8.3.1.2 Battery System
8.3.1.3 Motor Controller
8.3.1.4 Induction Motor
8.3.1.5 Power Electronics
8.3.1.6 Charging System
8.3.1.7 Energy Management System
8.3.1.8 Regenerative Braking System
8.3.1.9 Vehicle Control Unit
8.3.1.10 Mechanical Design
8.3.2 Contribution
8.4 Result and Discussion
8.4.1 Modeling and Simulation of Induction Motor Used in Electric Vehicles
8.4.1.1 Dynamic Equations
8.4.1.2 Electric Dynamics
8.4.1.3 Magnetic Dynamic
8.4.1.4 Mechanical Dynamics
8.4.1.5 Equation of Motion
8.4.1.6 Electromagnetic Torque Equation
8.4.1.7 Synchronous Speed
8.4.1.8 Rotor Speed
8.4.1.9 Torque-Speed Characteristics
8.4.1.10 Load Torque
8.4.2 Outcomes and a Comparative Analysis of Our Proposed Photovoltaic (PV)-Based Electric Vehicle (EV) System
8.4.2.1 Simulation of an Induction Motor with Inverter
8.5 Conclusion
References
9. The Viability of Advanced Technology for Future Generations
Manjushree Nayak and Ashutosh Pattnaik
9.1 Introduction
9.2 Communication Systems
9.2.1 5G
9.2.2 6G
9.2.3 Quantum Communications
9.2.4 Satellite Communication
9.2.5 Holography
9.2.6 Brain Computer Interface (BCI)
9.2.7 Artificial Intelligence (AI)
9.2.8 Internet of Things (IOT)
9.3 Conclusion
References
10. Power Optimization and Scheduling for Multi-Layer, Multi-Dimensional 6G Communication Networks
Harpreet Kaur Channi, Pulkit Kumar and Ramandeep Sandhu
10.1 Introduction
10.1.1 Background
10.1.2 Motivation
10.2 Literature Review
10.2.1 Evolution of Communication Networks
10.2.2 Key Features and Requirements of 6G
10.2.3 Previous Approaches to Power Optimization and Scheduling
10.3 Multi-Layer, Multi-Dimensional 6G Communication Networks
10.3.1 Architecture Overview
10.3.2 Integration of Multiple Layers
10.3.3 Consideration of Various Dimensions
10.4 Power Optimization in MLMD 6G Networks
10.4.1 Challenges in Power Consumption
10.4.2 Machine Learning Approaches
10.4.3 Adaptive Power Management
10.5 Scheduling Strategies for MLMD 6G Networks
10.5.1 Dimensions Considered in Scheduling
10.5.2 Resource Allocation Algorithms
10.5.3 Interference Mitigation Techniques
10.6 Proposed Framework
10.6.1 Integration of Power Optimization and Scheduling
10.6.2 Machine Learning in Dynamic Adaptation
10.6.3 Intelligent Resource Allocation
10.7 Challenges and Future Directions
10.7.1 Challenges and Limitations
10.7.2 Potential Enhancements
10.7.3 Future Trends in 6G Communication Networks
10.8 Conclusion
References
11. Industry 4.0: Future Opportunities and Challenges
Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani, Shippu Sachdeva and Suman Lata Tripathi
11.1 Introduction
11.2 Future Opportunities of Industrial 4.0
11.3 Increased Productivity and Efficiency
11.4 Innovation
11.5 Data-Driven Decision-Making
11.6 Supply Chain Optimization
11.7 Future Challenges of Industrial 4.0
11.8 Data Security and Privacy
11.9 Skills Gap and Workforce Training
11.10 Interoperability and Standardization
11.11 Ethical and Social Implications
11.12 Infrastructure Investment
11.13 Regulatory and Legal Challenges
11.14 Dependency on Technology
11.15 Conclusion
References
12. MIMO and Its Significance
Shahid Hamid and Shakti Raj Chopra
12.1 Introduction
12.2 MIMO
12.3 Signal Model for MIMO
12.4 Standard MIMO Configurations
12.5 Why MIMO
12.6 Results
References
Index
Back to Top
Preface
Contributor Lists
1. Efficient Energy Management in Hyperledger Fabric Blockchain Networks: A Proposed Optimized Solution
Kamurthi Ravi Teja and Shakti Raj Chopra
1.1 Introduction
1.2 Methodology
1.3 Experimental Analysis
1.3.1 Existing Problem in the Network
1.3.2 Proposed Hyperledger Fabric Network Approach
1.4 Results and Discussion
1.5 Conclusion
References
2. Framework for UAV-Based Wireless Power Harvesting
Tanishk Singhal and Harpreet Singh Bedi
2.1 Introduction
2.2 Literature Review
2.2.1 Proposed Framework
2.2.2 Integration with UAV Systems
2.2.3 Methodology
2.3 Results and Discussion
2.4 Conclusion
References
3. Future Generation Technology and Feasibility Assessment
Pradeep Singh, Krishan Arora and Umesh C. Rathore
3.1 Introduction
3.1.1 Technological Breakthroughs
3.1.2 Economic Viability and Feasibility
3.1.3 Regulatory Environments
3.1.4 Atmospheric Reliability
3.1.5 Customer Requirements
3.1.6 Societal Acceptability
3.2 Next-Generation Electrical Technologies
3.2.1 Smart Grids
3.2.1.1 Components and Features
3.2.1.2 Advantages
3.2.1.3 Challenges
3.2.2 Renewable Energy Integration
3.2.2.1 Grid Integration
3.2.2.2 Power Electronics and Control System
3.2.2.3 Energy Storage
3.2.2.4 Transmission and Distribution
3.2.2.5 Challenges
3.2.3 Energy Storage
3.2.3.1 Types of Energy Storage
3.2.3.2 Applications of Energy Storage
3.2.3.3 Advancements and Challenges
3.2.4 Electric Vehicles
3.2.4.1 Types of Electric Vehicles
3.2.4.2 Key Components and Systems
3.2.4.3 Challenges
3.2.5 Power Electronics
3.2.5.1 Components and Systems
3.2.5.2 Applications
3.2.5.3 Challenges and Future Trends
3.2.6 Internet of Things (IoT) and Connectivity
3.2.6.1 Internet of Things (IoT)
3.2.6.2 Connectivity in Electrical Engineering
3.2.6.3 Advantages and Challenges
3.3 Artificial Intelligence
3.3.1 Types of Artificial Intelligence
3.3.1.1 Type I
3.3.1.2 Type II (Based on Functionalities)
3.3.2 Applications of AI in Electrical Engineering
3.3.2.1 Design and Development
3.3.2.2 Predictive Maintenance
3.3.2.3 Power System and Grid Management
3.3.2.4 Automation and Control Systems
3.3.2.5 Energy Efficiency
3.4 Machine Learning
3.4.1 Types of Machine Learning
3.4.1.1 Supervised Machine Learning
3.4.1.2 Unsupervised Machine Learning
3.4.1.3 Semi-Supervised Learning
3.4.1.4 Reinforcement Learning
3.4.2 Applications of Machine Learning in Electrical Engineering
3.4.2.1 Predictive Maintenance
3.4.2.2 Power System Optimization
3.4.2.3 Control Systems and Optimization
3.4.2.4 Energy Efficiency
3.4.2.5 Design and Development
3.5 Conclusion
References
4. IoT-Enabled Weather Forecasting Systems in Future Networks: Constraints and Solutions
Yogesh Kumar Verma, Archana Kanwar and Manoj Kumar Shukla
4.1 Introduction
4.2 Need of IoT-Based Weather Forecasting System
4.3 Methodology and Results
4.4 Conclusion
References
5. Cognitive Radio-Based NOMA Communication Networks
Indu Bala
5.1 Introduction to Cognitive Radio and NOMA Networks
5.1.1 Motivation for Integrating Cognitive Radio with NOMA
5.2 Fundamentals of Cognitive Radio Technology
5.2.1 Spectrum Sensing Techniques in Cognitive Radio
5.2.2 Dynamic Spectrum Access (DSA)
5.2.3 Spectrum Management
5.2.4 Cognitive Radio Architectures and Protocols
5.3 Principles of Non-Orthogonal Multiple Access (NOMA)
5.3.1 Orthogonal Multiple Access versus NOMA
5.3.2 NOMA Techniques and Variants
5.3.3 Advantages and Challenges of NOMA Networks
5.4 Integration of Cognitive Radio with NOMA
5.4.1 Cognitive Radio Capabilities and Spectrum Sensing in NOMA Networks
5.4.2 Spectrum-Sharing Techniques in Cognitive Radio-NOMA Systems
5.4.3 Cognitive Radio-NOMA Architecture and Protocol Stack
5.4.4 Resource Allocation and Management in Cognitive Radio-NOMA Networks
5.4.4.1 Power Allocation and Control Strategies
5.4.4.2 Spectrum Sensing and Dynamic Spectrum Access in NOMA-CR Networks
5.4.4.3 QoS Provisioning and Optimization Techniques
5.5 Performance Evaluation and Analysis
5.5.1 Metrics for Assessing Cognitive Radio-NOMA Networks
5.5.2 Simulation and Modeling Approaches
5.6 Applications and Use Cases
5.6.1 Cognitive Radio-NOMA in Next-Generation Wireless Systems
5.6.2 Internet of Things (IoT) and Machine-to-Machine (M2M) Communications
5.6.3 Vertical Industry Applications
5.7 Challenges and Future Directions
5.7.1 Interference Management and Coexistence Issues
5.7.2 Security and Privacy Concerns in Cognitive Radio-NOMA Systems
5.7.3 Emerging Trends and Future Research Directions
5.8 Conclusion
References
6. Cognitive Radio (CR) Based Non-Orthogonal Multiple Access (NOMA) Network
Raja Gunasekaran, Ragavi Boopathi, Gobinath Velu Kaliyannan, Dinesh Dhanabalan and Kesavan Duraisamy
6.1 Introduction
6.2 Fundamentals of CR
6.2.1 Spectrum Hole Approach
6.2.2 Physical Layout of CR
6.2.3 Characteristics of CR
6.2.3.1 Cognitive Capability
6.2.3.2 Reconfigurability
6.2.4 CR Paradigms
6.2.5 Multiple Access Scheme
6.3 Spectrum Management System
6.3.1 Spectrum Sensing
6.3.2 Spectrum Decision
6.3.3 Spectrum Sharing
6.3.4 Spectrum Mobility
6.4 Noma Networks
6.4.1 NOMA Classification
6.4.1.1 PD-NOMA
6.4.1.2 CD-NOMA
6.4.2 OMA vs. NOMA
6.4.3 Downlink NOMA
6.4.4 Uplink NOMA
6.4.5 CR-Based NOMA Network
6.5 Enabling Technologies
6.5.1 Millimeter Wave (mmWave)
6.5.2 Intelligent Reflecting Surfaces (IRS)
6.5.3 Simultaneous Wireless Information and Power Transfer (SWIPT)
6.5.4 Cooperative CR-Based NOMA Systems
6.5.5 Satellite Communication (SatCom) CR-Based NOMA Systems
6.6 Conclusion
References
7. Artificial Intelligence and Machine Learning-Based Network Power Optimization Schemes
Jyoti, Aarti Shar, Ramandeep Sandhu, Manish Kumar Sharma and Deepika Ghai
7.1 Introduction
7.2 Network
7.2.1 Working of Network
7.2.1.1 Client-Server Architecture
7.2.1.2 Network Protocols
7.2.1.3 Network Addresses
7.2.2 Network Methods
7.2.2.1 Wireless vs. Wired
7.2.2.2 Network Range
7.3 Decentralized Connection
7.4 Communication Network
7.4.1 Types of Communication Networks
7.4.2 Components of Communication Networks
7.4.3 Communication Protocols
7.4.4 Communication Medium
7.5 Internet of Things (IoT)
7.6 5G and Future Technologies
7.7 Network Power and Unstable Power Supply of Computer Networks
7.8 Adaption of Optimization Schemes to Enhance Network Power
7.9 Related Work
7.10 Traditional Evaluation AI and ML-Based Network Energy Optimization Techniques
7.11 AI- and ML-Based Systems for Network Energy Optimization Techniques
7.11.1 Problem Definition and Objectives
7.12 Conclusion
References
8. Integration of PV Solar Rooftop Technology for Enhanced Performance and Sustainability of Electric Vehicles: A Techno-Analytical Approach
Vinay Anand and Himanshu Sharma
8.1 Introduction
8.1.1 Electric Vehicle
8.2 Literature Review
8.2.1 Numerous Challenges Faced by Electric Vehicles
8.3 Methods and Methodology
8.3.1 Structure of an Electric Vehicle Driven by Induction Motor
8.3.1.1 Solar Panel
8.3.1.2 Battery System
8.3.1.3 Motor Controller
8.3.1.4 Induction Motor
8.3.1.5 Power Electronics
8.3.1.6 Charging System
8.3.1.7 Energy Management System
8.3.1.8 Regenerative Braking System
8.3.1.9 Vehicle Control Unit
8.3.1.10 Mechanical Design
8.3.2 Contribution
8.4 Result and Discussion
8.4.1 Modeling and Simulation of Induction Motor Used in Electric Vehicles
8.4.1.1 Dynamic Equations
8.4.1.2 Electric Dynamics
8.4.1.3 Magnetic Dynamic
8.4.1.4 Mechanical Dynamics
8.4.1.5 Equation of Motion
8.4.1.6 Electromagnetic Torque Equation
8.4.1.7 Synchronous Speed
8.4.1.8 Rotor Speed
8.4.1.9 Torque-Speed Characteristics
8.4.1.10 Load Torque
8.4.2 Outcomes and a Comparative Analysis of Our Proposed Photovoltaic (PV)-Based Electric Vehicle (EV) System
8.4.2.1 Simulation of an Induction Motor with Inverter
8.5 Conclusion
References
9. The Viability of Advanced Technology for Future Generations
Manjushree Nayak and Ashutosh Pattnaik
9.1 Introduction
9.2 Communication Systems
9.2.1 5G
9.2.2 6G
9.2.3 Quantum Communications
9.2.4 Satellite Communication
9.2.5 Holography
9.2.6 Brain Computer Interface (BCI)
9.2.7 Artificial Intelligence (AI)
9.2.8 Internet of Things (IOT)
9.3 Conclusion
References
10. Power Optimization and Scheduling for Multi-Layer, Multi-Dimensional 6G Communication Networks
Harpreet Kaur Channi, Pulkit Kumar and Ramandeep Sandhu
10.1 Introduction
10.1.1 Background
10.1.2 Motivation
10.2 Literature Review
10.2.1 Evolution of Communication Networks
10.2.2 Key Features and Requirements of 6G
10.2.3 Previous Approaches to Power Optimization and Scheduling
10.3 Multi-Layer, Multi-Dimensional 6G Communication Networks
10.3.1 Architecture Overview
10.3.2 Integration of Multiple Layers
10.3.3 Consideration of Various Dimensions
10.4 Power Optimization in MLMD 6G Networks
10.4.1 Challenges in Power Consumption
10.4.2 Machine Learning Approaches
10.4.3 Adaptive Power Management
10.5 Scheduling Strategies for MLMD 6G Networks
10.5.1 Dimensions Considered in Scheduling
10.5.2 Resource Allocation Algorithms
10.5.3 Interference Mitigation Techniques
10.6 Proposed Framework
10.6.1 Integration of Power Optimization and Scheduling
10.6.2 Machine Learning in Dynamic Adaptation
10.6.3 Intelligent Resource Allocation
10.7 Challenges and Future Directions
10.7.1 Challenges and Limitations
10.7.2 Potential Enhancements
10.7.3 Future Trends in 6G Communication Networks
10.8 Conclusion
References
11. Industry 4.0: Future Opportunities and Challenges
Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani, Shippu Sachdeva and Suman Lata Tripathi
11.1 Introduction
11.2 Future Opportunities of Industrial 4.0
11.3 Increased Productivity and Efficiency
11.4 Innovation
11.5 Data-Driven Decision-Making
11.6 Supply Chain Optimization
11.7 Future Challenges of Industrial 4.0
11.8 Data Security and Privacy
11.9 Skills Gap and Workforce Training
11.10 Interoperability and Standardization
11.11 Ethical and Social Implications
11.12 Infrastructure Investment
11.13 Regulatory and Legal Challenges
11.14 Dependency on Technology
11.15 Conclusion
References
12. MIMO and Its Significance
Shahid Hamid and Shakti Raj Chopra
12.1 Introduction
12.2 MIMO
12.3 Signal Model for MIMO
12.4 Standard MIMO Configurations
12.5 Why MIMO
12.6 Results
References
Index
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