{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":24,"n_cols":5,"rows_kept":24,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[5,2,5,3],[7,2,7,3],[8,2,8,3],[9,2,9,3],[10,2,10,3],[11,2,11,3],[12,2,12,3],[14,2,14,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNINA9911020462803321"]
[null,null,"Autore",null,"Chopra Shakti Raj"]
[null,null,"Titolo",null,"Energy-Efficient Communication Networks"]
[null,null,"Pubbl/distr/stampa",null,"Newark : , : John Wiley & Sons, Incorporated, , 2025"]
[{"$nulls":4},"©2025"]
[null,null,"ISBN",null,"1-394-27167-0"]
[{"$nulls":4},"1-394-27168-9"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (205 pages)"]
[null,null,"Disciplina",null,"621.382"]
[null,null,"Soggetti",null,"Telecommunication - Power supply"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Components and Systems -- 3.2.5.2 Applications -- 3.2.5.3 Challenges and Future Trends -- 3.2.6 Internet of Things (IoT) and Connectivity -- 3.2.6.1 Internet of Things (IoT) -- 3.2.6.2 Connectivity in Electrical Engineering."]
[{"$nulls":4},"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."]
[{"$nulls":4},"5.4.2 Spectrum-Sharing Techniques in Cognitive Radio- NOMA Systems -- 5.4.3 Cognitive Radio-NOMA Architecture and Protocol Stack -- 5.4.4 Resource Allocation and Management in Cognitive Radio-NOMA Networks -- 5.4.4.1 Power Allocation and Control Strategies -- 5.4.4.2 Spectrum Sensing and Dynamic Spectrum Access in NOMA-CR Networks -- 5.4.4.3 QoS Provisioning and Optimization Techniques -- 5.5 Performance Evaluation and Analysis -- 5.5.1 Metrics for Assessing Cognitive Radio-NOMA Networks -- 5.5.2 Simulation and Modeling Approaches -- 5.6 Applications and Use Cases -- 5.6.1 Cognitive Radio-NOMA in Next-Generation Wireless Systems -- 5.6.2 Internet of Things (IoT) and Machine-to-Machine (M2M) Communications -- 5.6.3 Vertical Industry Applications -- 5.7 Challenges and Future Directions -- 5.7.1 Interference Management and Coexistence Issues -- 5.7.2 Security and Privacy Concerns in Cognitive Radio- NOMA Systems -- 5.7.3 Emerging Trends and Future Research Directions -- 5.8 Conclusion -- References -- Chapter 6 Cognitive Radio (CR) Based Non-Orthogonal Multiple Access (NOMA) Network -- 6.1 Introduction -- 6.2 Fundamentals of CR -- 6.2.1 Spectrum Hole Approach -- 6.2.2 Physical Layout of CR -- 6.2.3 Characteristics of CR -- 6.2.3.1 Cognitive Capability -- 6.2.3.2 Reconfigurability -- 6.2.4 CR Paradigms -- 6.2.5 Multiple Access Scheme -- 6.3 Spectrum Management System -- 6.3.1 Spectrum Sensing -- 6.3.2 Spectrum Decision -- 6.3.3 Spectrum Sharing -- 6.3.4 Spectrum Mobility -- 6.4 Noma Networks -- 6.4.1 NOMA Classification -- 6.4.1.1 PD-NOMA -- 6.4.1.2 CD-NOMA -- 6.4.2 OMA vs. NOMA -- 6.4.3 Downlink NOMA -- 6.4.4 Uplink "]
[{"$nulls":4},"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)."]
[{"$nulls":4},"6.5.4 Cooperative CR-Based NOMA Systems -- 6.5.5 Satellite Communication (SatCom) CR-Based NOMA Systems -- 6.6 Conclusion -- References -- Chapter 7 Artificial Intelligence and Machine Learning-Based Network Power Optimization Schemes -- 7.1 Introduction -- 7.2 Network -- 7.2.1 Working of Network -- 7.2.1.1 Client-Server Architecture -- 7.2.1.2 Network Protocols -- 7.2.1.3 Network Addresses -- 7.2.2 Network Methods -- 7.2.2.1 Wireless vs. Wired -- 7.2.2.2 Network Range -- 7.3 Decentralized Connection -- 7.4 Communication Network -- 7.4.1 Types of Communication Networks -- 7.4.2 Components of Communication Networks -- 7.4.3 Communication Protocols -- 7.4.4 Communication Medium -- 7.5 Internet of Things (IoT) -- 7.6 5G and Future Technologies -- 7.7 Network Power and Unstable Power Supply of Computer Networks -- 7.8 Adaption of Optimization Schemes to Enhance Network Power -- 7.9 Related Work -- 7.10 Traditional Evaluation AI and ML-Based Network Energy Optimization Techniques -- 7.11 AI- and ML-Based Systems for Network Energy Optimization Techniques -- 7.11.1 Problem Definition and Objectives -- 7.12 Conclusion -- References -- Chapter 8 Integration of PV Solar Rooftop Technology for Enhanced Performance and Sustainability of Electric Vehicles: A Techno-Analytical Approach -- 8.1 Introduction -- 8.1.1 Electric Vehicle -- 8.2 Literature Review -- 8.2.1 Numerous Challenges Faced by Electric Vehicles -- 8.3 Methods and Methodology -- 8.3.1 Structure of an Electric Vehicle Driven by Induction Motor -- 8.3.1.1 Solar Panel -- 8.3.1.2 Battery System -- 8.3.1.3 Motor Controller -- 8.3.1.4 Induction Motor -- 8.3.1.5 Power Electronics -- 8.3.1.6 Charging System -- 8.3.1.7 Energy Management System -- 8.3.1.8 Regenerative Braking System -- 8.3.1.9 Vehicle Control Unit -- 8.3.1.10 Mechanical Design -- 8.3.2 Contribution -- 8.4 Result and Discussion."]
[{"$nulls":4},"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 "]
[{"$nulls":4},"Adaptive Power Management -- 10.5 Scheduling Strategies for MLMD 6G Networks -- 10.5.1 Dimensions Considered in Scheduling -- 10.5.2 Resource Allocation Algorithms -- 10.5.3 Interference Mitigation Techniques -- 10.6 Proposed Framework."]
[{"$nulls":4},"10.6.1 Integration of Power Optimization and Scheduling."]
[null,null,null,"Sommario/riassunto","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. 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. This book 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."]