Self-Powered Cyber Physical Systems

Self-Powered Cyber Physical Systems

This book is an attempt to aim at a very futuristic vision of achieving self-powered cyber-physical systems by applying a multitude of current technologies such as ULP electronics, thin film electronics, ULP transducers, autonomous wireless sensor networks using energy harvesters at the component le...

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Detalles Bibliográficos
Autor principal: Gatti, Rathishchandra R. (-)
Otros Autores: Singh, Chandra, Agrawal, Rajeev, Serrao, Felcy Jyothi
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2023.
Edición:1st ed
Materias:
Data centers > Energy consumption.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811326206719
Tabla de Contenidos:
  • 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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