Preface xv 1 Design of Low Power Junction-Less Double-Gate MOSFET 1Namrata Mendiratta and Suman Lata Tripathi 1.1 Introduction 1 1.2 MOSFET Performance Parameters 2 1.3 Comparison of Existing MOSFET Architectures 3 1.4 Proposed Heavily Doped Junction-Less Double Gate MOSFET (AJ-DGMOSFET) 3 1.5 Heavily Doped JL-DG MOSFET for Biomedical Application 8 1.6 Conclusion 9 References 10 2 VLSI Implementation of Vedic Multiplier 13Abhishek Kumar 2.1 Introduction 13 2.2 8x8 Vedic Multiplier 14 2.3 The Architecture of 8x8 Vedic Multiplier (VM) 16 2.3.1 Compressor Architecture 17 2.3.1.1 3:2 Compressor 18 2.3.1.2 4:3 Compressor 18 2.3.1.3 5:3 Compressor 18 2.3.1.4 8:4 Compressor 19 2.3.1.5 10:4 Compressor 19 2.3.1.6 12:5 Compressor 20 2.3.1.7 15:5 Compressor 21 2.3.1.8 20:5 Compressor 21 2.4 Results and Discussion 23 2.4.1 Instance Power 23 2.4.2 Net Power 24 2.4.3 8-Bit Multiplier 25 2.4.4 16-Bit Multiplier 26 2.4.5 Applications of Multiplier 27 2.5 Conclusion 28 References 28 3 Gas Leakage Detection from Drainage to Offer Safety for Sanitary Workers 31Dr. D. Jeyabharathi, Dr. D. Kesavaraja and D. Sasireka 3.1 Introduction 31 3.1.1 IOT-Based Sewer Gas Detection 31 3.1.1.1 IoT Sensors 32 3.1.2 Objective 32 3.1.3 Contribution of this Chapter 33 3.1.4 Outline of the Chapter 33 3.2 Related Works 33 3.2.1 Sewer Gas Leakage Detection 33 3.2.2 Crack Detection 34 3.3 Methodology 34 3.3.1 Sewer Gas Detection 34 3.3.1.1 Proposed Tristate Pattern 35 3.3.2 Crack Detection 36 3.3.3 Experimental Setup 37 3.4 Experimental Results 39 3.5 Conclusion 40 References 40 4 Machine Learning for Smart Healthcare Energy-Efficient System 43S. Porkodi, Dr. D. Kesavaraja and Dr. Sivanthi Aditanar 4.1 Introduction 43 4.1.1 IoT in the Digital Age 43 4.1.2 Using IoT to Enhance Healthcare Services 44 4.1.3 Edge Computing 44 4.1.4 Machine Learning 44 4.1.5 Application in Healthcare 45 4.2 Related Works 45 4.3 Edge Computing 47 4.3.1 Architecture 47 4.3.2 Advantages of Edge Computing over Cloud Computing 47 4.3.3 Applications of Edge Computing in Healthcare 48 4.3.4 Edge Computing Advantages 49 4.3.5 Challenges 50 4.4 Smart Healthcare System 50 4.4.1 Methodology 50 4.4.2 Data Acquisition and IoT End Device 51 4.4.3 IoT End Device and Backend Server 51 4.5 Conclusion and Future Directions 52 References 52 5 Review of Machine Learning Techniques Used for Intrusion and Malware Detection in WSNs and IoT Devices 57Dr. Jeyabharathi, Dr. A. Sherly Alphonse, Ms. E.L. Dhivya Priya and Dr. M. Kowsigan 5.1 Introduction 57 5.2 Types of Attacks 58 5.3 Some Countermeasures for the Attacks 59 5.4 Machine Learning Solutions 59 5.5 Machine Learning Algorithms 59 5.6 Authentication Process Based on Machine Learning 60 5.7 Internet of Things (IoT) 62 5.8 IoT-Based Attacks 62 5.8.1 Botnets 62 5.8.2 Man-in-the-Middle 62 5.9 Information and Identity Theft 62 5.10 Social Engineering 63 5.11 Denial of Service 63 5.12 Concerns 63 5.13 Conclusion 64 References 64 6 Smart Energy-Efficient Techniques for Large-Scale Process Industries 67B Koti Reddy and N V Raghavaiah 6.1 Pumps Operation 67 6.1.1 Parts in a Centrifugal Pump 68 6.1.2 Pump Efficiency 68 6.1.3 VFD 70 6.1.4 VFD and Pump Motor 72 6.1.5 Large HT Motors 73 6.1.6 Smart Pumps 73 6.2 Vapour Absorption Refrigeration System 74 6.2.1 Vapour Compression Refrigeration 74 6.2.2 Vapour Absorption Refrigeration 75 6.3 Heat Recovery Equipment 77 6.3.1 Case Study 77 6.3.2 Advantages of Heat Recovery 78 6.4 Lighting System 78 6.4.1 Technical Terms 78 6.4.2 Introduction 78 6.4.3 LED Lighting 79 6.4.4 Energy-Efficiency Techniques 79 6.4.5 Light Control with IoT 80 6.4.5.1 Wipro Scheme 80 6.4.5.2 Tata Scheme 80 6.4.6 EU Practices 81 6.5 Air Conditioners 82 6.5.1 Technical Terms 82 6.5.2 Types of Air Conditioners 82 6.5.3 Star Rating of BEE 83 6.5.4 EU Practices 83 6.5.5 Energy-Efficiency Tips 83 6.5.6 Inverter Air Conditioners 85 6.5.7 IoT-Based Air Conditioners 85 6.6 Fans and Other Smart Appliances 86 6.6.1 BLDC Fan Motors 87 6.6.2 Star Ratings 87 6.6.3 Group Drive of Fans 88 6.6.4 Other Smart Appliances 88 6.7 Motors 92 6.7.1 Motor Efficiency 92 6.7.2 Underrated Operation 93 6.7.3 Energy-Efficient Motors 94 6.7.3.1 Energy-Efficiency Ratings of BEE 94 6.7.3.2 Energy-Efficiency Ratings of IEC 94 6.7.4 Retrofit of Standard Motors with Energy-Efficient Motors 96 6.7.5 Other Salient Points 97 6.7.6 Use of Star-Delta Starter Motor 97 6.8 Energy-Efficient Transformers 98 6.8.1 IEC Recommendation 98 6.8.2 Super Conducting Transformers 99 References 99 7 Link Restoration and Relay Node Placement in Partitioned Wireless Sensor Network 101Manwinder Singh and Anudeep Gandam 7.1 Introduction 101 7.2 Related Work 103 7.2.1 Existing Techniques 105 7.3 Proposed K-Means Clustering Algorithm 105 7.3.1 Homogenous and Heterogeneous Network Clustering Algorithms 105 7.3.2 Dynamic and Static Clustering 105 7.3.2.1 Routing 106 7.3.3 Flow Diagram 106 7.3.4 Objective Function 106 7.4 System Model and Assumption 108 7.4.1 Simulation Parameters 108 7.4.1.1 Residual Energy 108 7.4.1.2 End-to-End Delay 109 7.4.1.3 Number of Hops or Hop Count in the Network 109 7.5 Results and Discussion 109 7.6 Conclusions 114 References 115 8 Frequency Modulated PV Powered MLI Fed Induction Motor Drive for Water Pumping Applications 119Arunkumar S, Mohana Sundaram N and K. Malarvizhi 8.1 Introduction 119 8.2 PV Panel as Energy Source 120 8.2.1 Solar Cell 120 8.3 Multi-Level Inverter Topologies 121 8.3.1 Types of Inverters Used for Drives 121 8.3.2 Multi-Level Inverters 121 8.4 Experimental Results and Discussion 122 8.4.1 PV Powered H Bridge Inverter-Fed Drive 123 8.4.2 PV Powered DCMLI Fed Drive 126 8.5 Conclusion and Future Scope 128 References 129 9 Analysis and Design of Bidirectional Circuits for Energy Storage Application 131Suresh K, Sanjeevikumar Padmanaban and S Vivek 9.1 Introduction 131 9.2 Modes of Operation Based on Main Converters 133 9.2.1 Single-Stage Rectification 134 9.2.2 Single-Stage Inversion 135 9.2.3 Double-Stage Rectification 137 9.2.3.1 Duty Mode - Interval -I 137 9.2.3.2 Freewheeling Mode - Interval -II 138 9.2.4 Double-Stage Inversion 139 9.2.4.1 Charging Mode - Interval -I 140 9.2.4.2 Duty Mode - Interval -II 141 9.3 Proposed Methodology for Three-Phase System 141 9.3.1 Control Block of Overall System 143 9.3.2 Proposed Carrier-Based PWM Strategy 144 9.3.3 Experiment Results 145 9.4 Conclusion 148 References 148 10 Low-Power IOT-Enabled Energy Systems 151Yogini Dilip Borole and Dr. C. G. Dethe 10.1 Overview 151 10.1.1 Conceptions 151 10.1.2 Motivation 152 10.1.3 Methodology 154 10.2 Empowering Tools 156 10.2.1 Sensing Components 156 10.2.2 Movers 159 10.2.3 Telecommunication Technology 160 10.2.4 Internet of Things Information and Evaluation 166 10.2.4.1 Distributed Evaluation 166 10.2.4.2 Fog Computing (Edge Computing) 167 10.3 Internet of Things within Power Region 167 10.3.1 Internet of Things along with Vitality Production 168 10.3.2 Smart Metropolises 168 10.3.3 Intelligent Lattice Network 171 10.3.4 Smart Buildings Structures 172 10.3.5 Powerful Usage of Vitality in Production 173 10.3.6 Insightful Transport 174 10.4 Difficulties - Relating Internet of Things 174 10.4.1 Vitality Ingestion 178 10.4.2 Synchronization via Internet of Things through Sub-Units 178 10.4.3 Client Confidentiality 180 10.4.4 Safety Challenges 180 10.4.5 IoT Standardization and Architectural Concept 181 10.5 Upcoming Developments 182 10.5.1 IoT and Block Chain 182 10.5.2 Artificial Intelligence and IoT 184 10.5.3 Green IoT 185 10.6 Conclusion 187 References 188 11 Efficient Renewable Energy Systems 199Prabhansu and Nayan Kumar Introduction 199 11.1 Renewable-Based Available Technologies 200 11.1.1 Wind Power 201 11.1.1.1 Modeling of the Wind Turbine Generator (WTG) 201 11.1.1.2 Categorization of Wind Turbine 202 11.1.2 Solar Power 202 11.1.2.1 PV System 202 11.1.2.2 Network-Linked Photovoltaic Grid-Connected PV Set-Up 203 11.1.3 Tidal Energy 203 11.1.4 Battery Storage System 204 11.1.5 Solid Oxide Energy Units for Enhancing Power Life 204 11.1.5.1 Common Utility of SOFC 204 11.1.5.2 Integrated Solid Oxide Energy Components and Sustainable Power Life 205 11.2 Adaptability Frameworks 206 11.2.1 Distributed Energy Resources (DER) 206 11.2.2 New Age Grid Connection 209 11.3 Conclusion 210 References 211 12 Efficient Renewable Energy Systems 215Dr. Arvind Dhingra 12.1 Introduction 215 12.1.1 World Energy Scenario 215 12.2 Sources of Energy: Classification 217 12.3 Renewable Energy Systems 217 12.3.1 Solar Energy 218 12.3.2 Wind 218 12.3.3 Geothermal 218 12.3.4 Biomass 218 12.3.5 Ocean 218 12.3.6 Hydrogen 218 12.4 Solar Energy 218 12.5 Wind Energy 223 12.6 Geothermal Energy 225 12.7 Biomass 226 12.7.1 Forms of Biomass 226 12.8 Ocean Power 227 12.9 Hydrogen 227 12.10 Hydro Power 227 12.11 Conclusion 227 References 227 13 Agriculture-IoT-Based Sprinkler System for Water and Fertilizer Conservation and Management 229Dilip Kumar and Ujala Choudhury 13.1 Introduction 229 13.1.1 Novelty of the Work 232 13.1.2 Benefit to Society 232 13.2 Development of the Proposed System 233 13.3 System Description 233 13.3.1 Study of the Crop Under Experiment 233 13.3.2 Hardware of the System 235 13.3.3 Software of the System 235 13.4 Layers of the System Architecture 236 13.4.1 Application Layer 236 13.4.2 Cloud Layer 237 13.4.3 Network Layer 237 13.4.4 Physical Layer 237 13.5 Calibration 237 13.6 Layout of the Sprinkler System 239 13.7 Testing 239 13.8 Results and Discussion 241 13.9 Conclusion 242 References 242 14 A Behaviour-Based Authentication to Internet of Things Using Machine Learning 245Mohit Goyal and Durgesh Srivastava 14.1 Introduction 246 14.2 Basics of Internet of Things (IoT) 246 14.2.1 The IoT Reference Model 248 14.2.2 Working of IoT 249 14.2.2.1 Device 249 14.2.2.2 Connectivity to Cloud 250 14.2.2.3 Data Analysis 250 14.2.2.4 User Interface 250 14.2.3 Utilization of Internet of Things (IoT) 250 14.3 Authentication in IoT 251 14.3.1 Methods of Authentication 251 14.3.1.1 Authentication Based on Knowledge 252 14.3.1.2 Authentication Based on Possession 252 14.3.1.3 Authentication Based on Biometric 253 14.4 User Authentication Based on Behavioral-Biometric 255 14.4.1 Machine Learning 256 14.4.1.1 Supervised Machine Learning 256 14.4.1.2 Unsupervised Machine Learning 256 14.4.2 Machine Learning Algorithms 257 14.4.2.1 RIPPER 257 14.4.2.2 Multilayer Perceptron 257 14.4.2.3 Decision Tree 257 14.4.2.4 Random Forest 258 14.4.2.5 Instance-Based Learning 258 14.4.2.6 Bootstrap Aggregating 258 14.4.2.7 Naïve Bayes 258 14.5 Threats and Challenges in the Current Security Solution for IoT 258 14.6 Proposed Methodology 259 14.6.1 Collection of Gait Dataset 259 14.6.2 Gait Data Preprocessing 259 14.6.3 Reduction in Data Size 260 14.6.4 Gaits Feature 260 14.6.5 Classification 260 14.7 Conclusion and Future Work 261 References 261 15 A Fuzzy Goal Programming Model for Quality Monitoring of Fruits during Shipment Overseas 265Pushan Kr. Dutta, Somsubhra Gupta, Simran Kumari and Akshay Vinayak 15.1 Introduction 265 15.2 Proposed System 266 15.2.1 Problem Statement 266 15.2.2 Overview 266 15.2.3 System Components 268 15.3 Work Process 271 15.3.1 System Hardware 271 15.3.2 Connections and Circuitry 271 15.4 Optimization Framework 271 15.4.1 Fuzzy Goal Description 271 15.4.2 Characterizing Fuzzy Membership Function 272 15.4.3 Construction of FGP Model 272 15.4.4 Definition of Variables and Parameters 273 15.4.5 Fuzzy Goal Description 274 15.5 Creation of Database and Website 275 15.5.1 Hosting PHP Application and Creation of MySQL Database 275 15.5.2 Creation of API (Application Programming Interfaces) Key 275 15.5.2.1 $api_key_value = “3mM44UaC2DjFcV_63GZ14aWJcRDNmYBMsxceu”; 275 15.5.2.2 Preparing Mysql Database 275 15.5.2.3 Structured Query Language (SQL) 275 15.5.2.4 Use of HTTP (Hypertext Transfer Protocol) in Posting Request 276 15.5.2.5 Adding a Dynamic Map to the Website 277 15.5.2.6 Adding Dynamic Graph to the Website 277 15.5.2.7 Adding the Download Option of the Data Set 278 15.6 Libraries Used and Code Snipped 278 15.7 Mode of Communication 280 15.8 Conclusion 280 Abbreviations 282 References 282 16 Internet of Things – Definition, Architecture, Applications, Requirements and Key Research Challenges 285Dushyant Kumar Singh, Himani Jerath and P. Raja 16.1 Introduction 285 16.2 Defining the Term Internet of Things (IoT) 286 16.3 IoT Architecture 287 16.4 Applications of Internet of Things (IoT) 289 16.5 Requirement for Internet of Things (IoT) Implementation 290 16.6 Key Research Challenges in Internet of Things (IoT) 291 16.6.1 Computing, Communication and Identification 291 16.6.2 Network Technology 292 16.6.3 Greening of Internet of Things (IoT) 292 16.6.4 Security 293 16.6.5 Diversity 293 16.6.6 Object Safety and Security 293 16.6.7 Data Confidentiality and Unauthorized Access 293 16.6.8 Architecture 293 16.6.9 Network and Routing Information Security 293 References 294 17 FinFET Technology for Low-Power Applications 297Bindu Madhavi, Suman Lata Tripathi and Bhagwan Shree Ram 17.1 Introduction 297 17.2 Exiting Multiple-Gate MOSFET Architectures 299 17.3 FinFET Design and Analysis 301 17.4 Low-Power Applications 304 17.4.1 FinFET-Based Digital Circuit Design 304 17.4.2 FinFET-Based Memory Design 304 17.4.3 FinFET-Based Biosensors 304 17.5 Conclusion 305 References 305 18 An Enhanced Power Quality Single-Source Large Step-Up Switched-Capacitor Based Multi-Level Inverter Configuration with Natural Voltage Balancing of Capacitors 307Mahdi Karimi, Paria Kargar, Kazem Varesi and Sanjeevikumar Padmanaban 18.1 Introduction 307 18.2 Suggested Topology 309 18.2.1 Circuit Configuration 309 18.2.2 Generation of Output Voltage Steps 310 18.2.3 Voltage Stress of Switches 320 18.3 Cascaded Configuration of Suggested Topology 320 18.4 Modulation Technique 321 18.5 Power Loss Analysis 324 18.5.1 Conduction Losses 324 18.5.2 Switching Losses 326 18.5.3 Capacitor Losses 327 18.6 Design of Capacitors 328 18.7 Comparative Analysis 330 18.8 Simulation Results 333 18.9 Conclusions 336 References 336 Index 339

Covering the concepts and fundamentals of efficient energy systems, this volume, written and edited by a global team of experts, also goes into the practical applications that can be utilized across multiple industries, for both the engineer and the student.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.This outstanding new volume:Handles the fundamentals of system design including the concept of energy saving aspects at different levelsIs useful for all engineering students for learning the fundamentals of system design and automation with machine learning and IoTWill be helpful for researchers and designers to find out key parameters for future projects and current applicationsAudience:Engineers and scientists across many fields, including petroleum and process engineers, chemical engineers, electrical engineers working with power systems, and students at the university and post-graduate level studying energy topics