| Autore |
Gatti Rathishchandra R
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Newark : , : John Wiley & Sons, Incorporated, , 2023
|
| Descrizione fisica |
1 online resource (415 pages)
|
| Disciplina |
621.39
|
| Altri autori (Persone) |
SinghChandra
AgrawalRajeev
SerraoFelcy Jyothi
|
| Soggetto topico |
Data centers - Energy consumption
|
| ISBN |
9781119842026
1119842026
9781119842019
1119842018
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Chapter 1 Self-Powered Sensory Transducers: A Way Toward Green Internet of Things -- 1.1 Introduction -- 1.2 Need of the Work -- 1.3 Energy Scavenging Schemes in WSAN -- 1.3.1 Photovoltaic or Solar Cell -- 1.3.2 Temperature Gradient -- 1.3.3 Pressure Variations -- 1.3.4 Plant Microbial Fuel -- 1.3.5 Wind/Liquid Flow -- 1.3.6 Vibrations -- 1.3.7 Friction -- 1.4 Self Powered Systems and Green IoT (G-IoT) -- 1.5 Application Area and Scope of Self-Powered System in G-IoT -- 1.5.1 Terrestrial Applications -- 1.5.1.1 Agriculture -- 1.5.1.2 Smart Home and Cities -- 1.5.1.3 Industry -- 1.5.1.4 Medicines -- 1.5.1.5 Environment Monitoring -- 1.5.1.6 Structural Monitoring -- 1.5.1.7 Indoor Applications -- 1.5.1.8 Arial Vehicles -- 1.5.1.9 Military Applications -- 1.5.1.10 Underwater Applications -- 1.5.1.11 Submarine and Event Localization -- 1.5.1.12 Water Contamination -- 1.5.1.13 Intelligent Water Distribution and Smart Meter -- 1.5.1.14 Underground Applications -- 1.5.1.15 Coal and Petroleum Mining Application -- 1.5.1.16 Underground Structural Monitoring -- 1.6 Challenges and Future Scope of the Self-Powered G-IoT -- 1.6.1 Challenges Pertain to Energy Efficient Design and Protocols -- 1.6.2 Size and Cost of the Harvester -- 1.6.3 Energy-Efficient Routing and Scheduling Protocols -- 1.6.4 Design of Application-Specific Passive Wake-Up Receivers -- 1.6.5 Redefined Protocol with Application-Specific Goals -- 1.6.6 Embedded Operating Systems -- 1.6.7 AI and Cloud-Assisted Lifetime Prediction Techniques -- 1.6.8 Design of Energy-Efficient/Harvested Service-Oriented Architecture -- 1.6.9 Smart Web Interfaces for Monitoring -- 1.6.10 Cross Layer Exploitations with Energy Harvesting -- 1.6.11 Security Aspects and Need of Standardization.
1.6.12 Challenges Related to Energy Harvesting Techniques -- 1.6.13 Generic Energy Generator -- 1.6.14 Hybrid Energy Sources -- 1.6.15 Cooperation Among Different Energy Sources -- 1.6.16 Energy Storage -- 1.6.17 Intelligent Prediction Model for Amount of Harvested Energy -- 1.6.18 Focus on Energy Generator for Underwater and Underground Applications -- 1.7 Conclusion -- References -- Chapter 2 Self-Powered Wireless Sensor Networks in Cyber Physical System -- 2.1 Introduction -- 2.2 Wireless Sensor Networks in CPS -- 2.3 Architecture of WSNs with Energy Harvesting -- 2.4 Energy Harvesting for WSN -- 2.5 Energy Harvesting Due to Mechanical Vibrations -- 2.6 Piezoelectric Generators -- 2.7 Piezoelectric Materials -- 2.8 Types of Piezoelectric Structures -- 2.8.1 Nanogenerators -- 2.8.2 Piezoelectric Nanogenerators -- 2.8.3 Triboelectric Nanogenerators -- 2.8.4 Pyroelectric Nanogenerators -- 2.8.5 Thermoelectric Nanogenerator -- 2.9 Hybridized Nanogenerators for Energy Harvesting -- 2.10 Conclusion -- References -- Chapter 3 The Emergence of Cyber-Physical System in the Context of Self-Powered Soft Robotics -- 3.1 Introduction -- 3.2 Actuators and Its Types -- 3.2.1 Nature of Actuation -- 3.2.1.1 Actuators Based on Thermal Materials -- 3.2.1.2 Actuators Based on Pressure -- 3.2.1.3 Actuators Based on Photo Responsivity -- 3.2.1.4 Actuators Based on Explosive Function -- 3.2.1.5 Electric Actuation Methods -- 3.3 Soft Actuator Electrodes -- 3.4 Sensors -- 3.5 Soft Robotic Structures and Control Methods -- 3.6 Soft Robot Applications -- 3.7 Future Scope -- 3.8 Conclusion -- References -- Chapter 4 Dynamic Butterfly Optimization Algorithm-Based Task Scheduling for Minimizing Energy Consumption in Distributed Green Data Centers -- 4.1 Introduction -- 4.2 Related Work -- 4.2.1 Green Data Centers -- 4.2.2 Energy-Aware Task Scheduling.
4.3 Improved Dynamic Butterfly Optimization Algorithm (IDBOA)-Based Task Scheduling (IDBOATS) -- 4.3.1 Problem Definition -- 4.3.2 Delay Constraint -- 4.3.3 Green Energy Model -- 4.3.4 Energy Consumption Model -- 4.3.5 Constraint-Imposed Optimization Problem -- 4.3.6 Primitives of Dynamic Butterfly Optimization Algorithm (DBOA) -- 4.3.7 Classical Butterfly Optimization Algorithm -- 4.3.8 Transformation of BOA into DBOA using Mutation-Based Local Searching Strategy (MLSS) -- 4.4 Results and Discussion -- 4.5 Conclusion -- References -- Chapter 5 Wireless Power Transfer for IoT Applications-A Review -- 5.1 Introduction -- 5.2 Sensors -- 5.3 Actuators -- 5.4 Energy Requirement in Wireless Sensor Networks (WSNs) -- 5.5 Wireless Sensor Network and Green IoT (G-IoT) -- 5.6 Purpose of G-IoT -- 5.7 Motivation -- 5.8 Contribution -- 5.9 Need of the Work -- 5.10 Energy Transferring Schemes in WSAN -- 5.11 Electromagnetic Induction -- 5.11.1 Electrodynamic and Electrostatic -- 5.11.2 Electrostatic Field -- 5.11.3 Electrostatic Force -- 5.11.4 Electromagnetic -- 5.11.5 Electromagnetic Field -- 5.12 Inductive Coupling -- 5.13 Resonance Inductive Coupling -- 5.14 Wireless Power Transmission Using Microwaves -- 5.15 Electromagnetic Radiations -- 5.16 Conclusion -- References -- Chapter 6 Adaptive Energy Intelligence Using AI/ML Techniques -- 6.1 Introduction -- 6.2 Evolution of Cyber Physical System -- 6.3 Relationship With Internet of Things -- 6.4 Challenges in Design and Integration of Cyber Physical Systems -- 6.5 Future Challenges and Promises -- 6.6 Machine Learning Models -- 6.7 Estimation of Building Energy Consumption -- 6.8 Development of Artificial Intelligence -- 6.9 Usage of AI/ML in Adaptive Energy Management -- 6.10 Use of Hybrid/Ensemble Machine Learning Algorithm for Better Prediction -- 6.11 Conclusion -- References.
Chapter 7 Renewable Energy Smart Grids for Electric Vehicles -- 7.1 Introduction -- 7.2 Integration of Electric Vehicles (EVs) into the Power Grid -- 7.3 EV Charging and Electric Grid Interaction -- 7.4 EVs with V2G System Architecture -- 7.5 EVs and Smart Grid Infrastructure -- 7.6 Renewable Energy Sources Integration With EVs -- 7.6.1 PV Solar Energy With EVs -- 7.6.2 Wind Energy With EVs -- 7.7 Application in Transport Sector -- 7.8 Application in Micro-Grid -- 7.9 State-of-the-Art Review -- 7.10 Future Trends -- References -- Chapter 8 Recent Advances in Integrating Renewable Energy Micro-Grid Systems With Electric Vehicles -- 8.1 Introduction -- 8.2 Electric Vehicles and Renewable Energy Sources: A General Overview -- 8.2.1 Electric Vehicles -- 8.2.2 Battery Electric Vehicles -- 8.2.3 Parallel Hybrid Electric Vehicles -- 8.2.4 Battery Chargers for EVs -- 8.2.5 Renewable Energy Sources -- 8.2.5.1 Wind Energy -- 8.2.5.2 Solar Energy -- 8.3 Microgrid -- 8.3.1 Domestic Use -- 8.3.2 Industrial Use -- 8.3.3 Benefits of Microgrids -- 8.3.4 Locations of Microgrid -- 8.4 Interactions Between Cost-Conscious EVs and RESs -- 8.4.1 Operational Cost Reduction -- 8.4.2 Lowering the Electricity Generation Cost -- 8.4.3 Growth in Profit or Benefit -- 8.4.4 Reduction in Charging Cost for EVs Owners -- 8.4.5 Other Cost-Conscious Efforts -- 8.5 Interaction Between Efficiency-Conscious EVs and RESs -- 8.5.1 Microgrid Implementation -- 8.5.2 Increasing the Use of RESs -- 8.5.3 Other Works With a Focus on Efficiency -- 8.6 Open Problems -- 8.6.1 Grid Integration of RESs on a Large Scale -- 8.6.2 The Use of EV Batteries in Conjunction With RESs -- 8.6.3 V2G's Ability to Allow the Interaction of RESs -- 8.7 Conclusion -- References -- Chapter 9 Overview of Fast Charging Technologies of Electric Vehicles -- 9.1 Introduction.
9.2 Different Levels of Charging Electric Vehicles -- 9.2.1 Level I -- 9.2.2 Level II -- 9.2.3 Level III -- 9.2.4 DC vs AC -- 9.2.5 Fast Charging -- 9.3 State-of-the-Art Fast-Charging Implementation -- 9.4 DC Fast-Charging Structure -- 9.5 Fast Chargers -- 9.5.1 Fast Chargers Working -- 9.5.2 DC Plug Connectors -- 9.5.3 EV Fast-Charging Infrastructure -- 9.6 Today's Situation and Future Needs -- 9.7 Fast-Charging Point Power Requirements -- 9.8 Recent Technologies in Fast Charging, Machine Learning, and Artificial Intelligence -- 9.8.1 Machine Learning -- 9.8.2 Artificial Intelligence -- 9.8.3 Energy Storage Materials -- 9.9 Effect of Fast Charging on EV Powertrain Systems -- 9.9.1 Battery Technology Gap and Lithium Plating -- 9.9.2 Thermal Management Systems -- 9.9.3 Battery Cycle Life -- 9.10 Grid Impacts Caused by EV Charging -- 9.10.1 Impact on Load Profile -- 9.10.2 Impact on Grid Components -- 9.10.3 Impact on Power Losses -- 9.10.4 Impact on Voltage Profile -- 9.10.5 Harmonic Impact -- 9.11 Fast-Charging Technologies on the Self-Powered Automotive Cyber-Physical Systems -- 9.12 Conclusions -- References -- Chapter 10 A Survey of VANET Routing Attacks and Defense Mechanisms in Intelligent Transportation System -- 10.1 Introduction -- 10.2 Attacks in VANET -- 10.2.1 Attack on V2V Communication -- 10.2.2 Various Attacks on Safety Applications -- 10.2.3 Attack on Infotainment Applications -- 10.3 Impacts of Attacks on VANET Routing -- 10.4 Nonintentional Misbehavior -- 10.5 Intentional Misbehavior -- 10.6 Defence Mechanism of Routing Attacks in VANET Routing -- 10.7 Intrusion Detection Techniques in VANETs -- 10.8 Anonymous Routing in VANETs -- 10.9 Challenges and Future Directions -- 10.10 Conclusion -- References -- Chapter 11 ANN-Based Cracking Model for Flexible Pavement in the Urban Roads -- 11.1 Introduction -- 11.2 Literature Review.
11.3 Methodology.
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| Record Nr. | UNINA-9911018833803321 |
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Data center optimization strategies / / Otto Van Geet
