| Autore |
Chopra Shakti Raj
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Newark : , : John Wiley & Sons, Incorporated, , 2025
|
| Descrizione fisica |
1 online resource (205 pages)
|
| Disciplina |
621.382
|
| Altri autori (Persone) |
AroraKrishan
TripathiSuman Lata
KumarVikram
|
| Soggetto topico |
Telecommunication - Power supply
|
| ISBN |
1-394-27167-0
1-394-27168-9
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- List of Contributors -- Chapter 1 Efficient Energy Management in Hyperledger Fabric Blockchain Networks: A Proposed Optimized Solution -- 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 -- Chapter 2 Framework for UAV-Based Wireless Power Harvesting -- 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 -- Chapter 3 Future Generation Technology and Feasibility Assessment -- 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 -- Chapter 4 IoT-Enabled Weather Forecasting Systems in Future Networks: Constraints and Solutions -- 4.1 Introduction -- 4.2 Need of IoT-Based Weather Forecasting System -- 4.3 Methodology and Results -- 4.4 Conclusion -- References -- Chapter 5 Cognitive Radio-Based NOMA Communication Networks -- 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 -- Chapter 6 Cognitive Radio (CR) Based Non-Orthogonal Multiple Access (NOMA) Network -- 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 -- Chapter 7 Artificial Intelligence and Machine Learning-Based Network Power Optimization Schemes -- 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 -- Chapter 8 Integration of PV Solar Rooftop Technology for Enhanced Performance and Sustainability of Electric Vehicles: A Techno-Analytical Approach -- 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 -- Chapter 9 The Viability of Advanced Technology for Future Generations -- 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 -- Chapter 10 Power Optimization and Scheduling for Multi-Layer, Multi-Dimensional 6G Communication Networks -- 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.
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| Record Nr. | UNINA-9911020462803321 |
Info
1998 20th International Telecommunications Energy Conference (INTELEC)
