Design and Development of Efficient Energy Systems

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There is not a single industry which will not be transformed by machine learning and Internet of Things (IoT). IoT and machine learning have altogether changed the technological scenario by letting the user monitor and control things based on the prediction made by machine learning algorithms. There has been substantial progress in the usage of platforms, technologies and applications that are based on these technologies. These breakthrough technologies affect not just the software perspective of the industry, but they cut across areas like smart cities, smart healthcare, smart retail, smart monitoring, control, and others. Because of these “game changers,” governments, along with top companies around the world, are investing heavily in its research and development. Keeping pace with the latest trends, endless research, and new developments is paramount to innovate systems that are not only user-friendly but also speak to the growing needs and demands of society.
This volume is focused on saving energy at different levels of design and automation including the concept of machine learning automation and prediction modeling. It also deals with the design and analysis for IoT-enabled systems including energy saving aspects at different level of operation.
The editors and contributors also cover the fundamental concepts of IoT and machine learning, including the latest research, technological developments, and practical applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of IoT and machine technology, this is a must-have for any library.

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Table of Contents

1 Cover

2 Title Page

3 Copyright

4 Preface

5 1 Design of Low Power Junction-Less Double-Gate MOSFET 1.1 Introduction 1.2 MOSFET Performance Parameters 1.3 Comparison of Existing MOSFET Architectures 1.4 Proposed Heavily Doped Junction-Less Double Gate MOSFET (AJ-DGMOSFET) 1.5 Heavily Doped JL-DG MOSFET for Biomedical Application 1.6 Conclusion References

6 2 VLSI Implementation of Vedic Multiplier 2.1 Introduction 2.2 8x8 Vedic Multiplier 2.3 The Architecture of 8x8 Vedic Multiplier (VM) 2.4 Results and Discussion 2.5 Conclusion References

7 3 Gas Leakage Detection from Drainage to Offer Safety for Sanitary Workers 3.1 Introduction 3.2 Related Works 3.3 Methodology 3.4 Experimental Results 3.5 Conclusion References

8 4 Machine Learning for Smart Healthcare Energy-Efficient System 4.1 Introduction 4.2 Related Works 4.3 Edge Computing 4.4 Smart Healthcare System 4.5 Conclusion and Future Directions References

9 5 Review of Machine Learning Techniques Used for Intrusion and Malware Detection in WSNs and IoT Devices 5.1 Introduction 5.2 Types of Attacks 5.3 Some Countermeasures for the Attacks 5.4 Machine Learning Solutions 5.5 Machine Learning Algorithms 5.6 Authentication Process Based on Machine Learning 5.7 Internet of Things (IoT) 5.8 IoT-Based Attacks 5.9 Information and Identity Theft 5.10 Social Engineering 5.11 Denial of Service 5.12 Concerns 5.13 Conclusion References

10 6 Smart Energy-Efficient Techniques for Large-Scale Process Industries 6.1 Pumps Operation 6.2 Vapour Absorption Refrigeration System 6.3 Heat Recovery Equipment 6.4 Lighting System 6.5 Air Conditioners 6.6 Fans and Other Smart Appliances 6.7 Motors 6.8 Energy-Efficient Transformers References

11 7 Link Restoration and Relay Node Placement in Partitioned Wireless Sensor Network 7.1 Introduction 7.2 Related Work 7.3 Proposed K-Means Clustering Algorithm 7.4 System Model and Assumption 7.5 Results and Discussion 7.6 Conclusions References

12 8 Frequency Modulated PV Powered MLI Fed Induction Motor Drive for Water Pumping Applications 8.1 Introduction 8.2 PV Panel as Energy Source 8.3 Multi-Level Inverter Topologies 8.4 Experimental Results and Discussion 8.5 Conclusion and Future Scope References

13 9 Analysis and Design of Bidirectional Circuits for Energy Storage Application 9.1 Introduction 9.2 Modes of Operation Based on Main Converters 9.3 Proposed Methodology for Three-Phase System 9.4 Conclusion References

14 10 Low-Power IOT-Enabled Energy Systems 10.1 Overview 10.2 Empowering Tools 10.3 Internet of Things within Power Region 10.4 Difficulties - Relating Internet of Things 10.5 Upcoming Developments 10.6 Conclusion References

15 11 Efficient Renewable Energy Systems Introduction 11.1 Renewable-Based Available Technologies 11.2 Adaptability Frameworks 11.3 Conclusion References

16 12 Efficient Renewable Energy Systems 12.1 Introduction 12.2 Sources of Energy: Classification 12.3 Renewable Energy Systems 12.4 Solar Energy 12.5 Wind Energy 12.6 Geothermal Energy 12.7 Biomass 12.8 Ocean Power 12.9 Hydrogen 12.10 Hydro Power 12.11 Conclusion References

17 13 Agriculture-IoT-Based Sprinkler System for Water and Fertilizer Conservation and Management 13.1 Introduction 13.2 Development of the Proposed System 13.3 System Description 13.4 Layers of the System Architecture 13.5 Calibration 13.6 Layout of the Sprinkler System 13.7 Testing 13.8 Results and Discussion 13.9 Conclusion References

18 14 A Behaviour-Based Authentication to Internet of Things Using Machine Learning 14.1 Introduction 14.2 Basics of Internet of Things (IoT) 14.3 Authentication in IoT 14.4 User Authentication Based on Behavioral-Biometric 14.5 Threats and Challenges in the Current Security Solution for IoT 14.6 Proposed Methodology 14.7 Conclusion and Future Work References

19 15 A Fuzzy Goal Programming Model for Quality Monitoring of Fruits during Shipment Overseas 15.1 Introduction 15.2 Proposed System 15.3 Work Process 15.4 Optimization Framework 15.5 Creation of Database and Website 15.6 Libraries Used and Code Snipped 15.7 Mode of Communication 15.8 Conclusion Abbreviations References

20 16 Internet of Things – Definition, Architecture, Applications, Requirements and Key Research Challenges 16.1 Introduction 16.2 Defining the Term Internet of Things (IoT) 16.3 IoT Architecture 16.4 Applications of Internet of Things (IoT) 16.5 Requirement for Internet of Things (IoT) Implementation 16.6 Key Research Challenges in Internet of Things (IoT) References

21 17 FinFET Technology for Low-Power Applications 17.1 Introduction 17.2 Exiting Multiple-Gate MOSFET Architectures 17.3 FinFET Design and Analysis 17.4 Low-Power Applications 17.5 Conclusion References

22 18 An Enhanced Power Quality Single-Source Large Step-Up Switched-Capacitor Based Multi-Level Inverter Configuration with Natural Voltage Balancing of Capacitors 18.1 Introduction 18.2 Suggested Topology 18.3 Cascaded Configuration of Suggested Topology 18.4 Modulation Technique 18.5 Power Loss Analysis 18.6 Design of Capacitors 18.7 Comparative Analysis 18.8 Simulation Results 18.9 Conclusions References

23 Index

24 End User License Agreement

List of Illustrations

1 Chapter 1 Figure 1.1 2D view of AJ-DGMOSFET. Figure 1.2 I dversus V gsplot of AJ-DGMOSFET. Figure 1.3 Id Versus Vgs plot with different oxide region material. Figure 1.4 I dversus V gsPlot of different gate contact material. Figure 1.5 JL-DG MOSFET with cavity region. Figure 1.6 I dVersus V gsof AJ-DG MOSFET with varying dielectric constant (Lcavi...

2 Chapter 2 Figure 2.1 Multiplication of two 8-bit number with Urdhwa-Tiryakbhyam Sutra [28]... Figure 2.2 Block diagram of 8*8 multiplier. Figure 2.3 Compressor 3:2. Figure 2.4 Compressor 4:3. Figure 2.5 Compressor 5:3. Figure 2.6 Compressor 8 to 4. Figure 2.7 Compressor 10 to 4. Figure 2.8 Compressor 12 to 5. Figure 2.9 Compressor 15 to 5. Figure 2.10 Compressor 20 to 5. Figure 2.11 Behavioral simulation of 8x8 VM. Figure 2.12 Instance power usage of 8X8 vedic multiplier. Figure 2.13 Instance power usage of 16X16 VM. Figure 2.14 Net power usage of 8X8 VM. Figure 2.15 Net power usage of 16X16 VM.

3 Chapter 3 Figure 3.1 Architecture of the proposed system. Figure 3.2 Flow diagram of the automated system. Figure 3.3 Gas sensor. Figure 3.4 Raspberry pi. Figure 3.5 Flow diagram of the leakage detection. Figure 3.6 Experimental design. Figure 3.7 Experimental design.

4 Chapter 4 Figure 4.1 Edge computing architecture. Figure 4.2 Some of the applications of edge computing. Figure 4.3 Advantages of edge computing. Figure 4.4 Methodology of Smart Healthcare System.

5 Chapter 5Figure 5.1 Security components.

6 Chapter 6Figure 6.1 Pump cross section (Source: Horizontal centrifugal pump, DMPF) 1. Cas...Figure 6.2 Pump impeller diameter characteristic curves (Source: www.enggcyclope...Figure 6.3 Motor with VFD.Figure 6.4 Power and speed relationship.Figure 6.5 Pump curve.Figure 6.6 Retrofitted HT motor with LT motor and VFD.Figure 6.7 Smart Industrial pump concept.Figure 6.8 Basic componets of VCR.Figure 6.9 VAR system.Figure 6.10 WHR scheme.Figure 6.11 Street light control scheme by Wipro.Figure 6.12 Street light control scheme by Tata Communications.Figure 6.13 Sample star rating label.Figure 6.14 Inverter operation.Figure 6.15 IoT-enabled air conditioner.Figure 6.16 Smart air conditioner.Figure 6.17 Fan models.Figure 6.18 USA Star rating label for fan.Figure 6.19 Group control of fans.Figure 6.20 Power flow stages in a motor.Figure 6.21 Energy-efficiency ratings by EU.Figure 6.22 Efficiency graph of transformer.Figure 6.23 Super conducting transformers.

7 Chapter 7Figure 7.1 Clustered wireless sensor network [32].Figure 7.2 Flow diagram.Figure 7.3 System model [34].Figure 7.4 Network design of WSN in proposed system.Figure 7.5 Network design with cluster head in the proposed system.Figure 7.6 Results comparison of Reference paper [5, 13, 14].Figure 7.7 Results comparison of various performance parameters.Figure 7.8 Shows the residual energy of the system w.r.t. simulation time.Figure 7.9 End-to-end delay vs. simulation time.Figure 7.10 Shows the number of hops is less and requires less number of relay n...

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