Suman Lata Tripathi, PhD, is a professor at Lovely Professional with more than seventeen years of experience in academics. She has published more than 45 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published one edited book and currently has multiple volumes scheduled for publication, including volumes available from Wiley-Scrivener. Souvik Ganguli, PhD, is an assistant professor and received his PhD from Thapar Institute of Engineering and Technology, Patiala. With fourteen years of experience in academics and several years in industry, he has been a session chair, keynote speaker, and conference organizer for scholarly conferences, and he has published over 50 papers in academic journals. He also has coveted grants to his credit and has published a number of book chapters in edited volumes. Abhishek Kumar, PhD, is an associate professor at and obtained his PhD in the area of VLSI Design for Low Power and Secured Architecture from Lovely Professional University, India. With over 11 years of academic experience, he has published more than 30 research papers and proceedings in scholarly journals. He has also published five book chapters and one authored book. He has worked as a reviewer and fprogram committee member and editorial board member for academic and scholarly conferences and journals. Tengiz Magradze, PhD, is an electrical design advisor for WINDTHINK, head of power transmission lines projects with “Georgian State Electrosystem,” and an adjunct professor of Electrical/Power Engineering/Management at Ballsbridge University, Dominica. He has published 14 journal articles and one book and is an editorial board member for a number of academic journals.

• Preface xvList of Contributors xvii1 An Overview of the Intelligent Green Technologies for Sustainable Smart Cities 1Tanya Srivastava, Sahil Virk and Souvik Ganguli1.1 Introduction 21.2 Case Study 1: Oslo—A Smart City 51.3 Case Study 2: Chandigarh—A Smart City 51.4 Features of the Smart Cities 61.5 Well-Planned Public Spaces and Streets 61.5.1 Waste Management 61.5.2 Energy Management 71.5.3 Good Connectivity 71.5.4 Urban Residence 81.5.5 Smart Grids 81.5.6 Smart Governance 81.6 Intelligent Green Technologies 91.7 Global and National Acceptance Scenarios 131.8 Conclusions 15References 152 Artificial Intelligence for Green Energy Technology 19Shanthi Jayaraj and Meena Chinniah2.1 Introduction 192.2 Solar Energy and AI 202.3 AI Transforms Renewable Energy 232.4 IBM Solution Using AI 242.5 Hydrogen Vehicles 242.6 Wind Energy and AI 252.7 Renewable Energy Industry in India 292.8 Conclusion 30References 30Website Reference 31Abbreviations 313 Effective Waste Management System for Smart Cities 33G. Boopathi Raja3.1 Introduction 343.2 Literature Survey 363.3 Waste Management in India 373.4 Existing Methodology 403.4.1 IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System 403.4.2 IoT Enabled Solid Waste Management System 413.4.3 Smart Garbage Management System 413.5 Proposed Framework 423.5.1 System Description 423.6 Functionality of the Proposed System 443.6.1 Sensing Module 443.6.2 Storage Module 463.6.3 User Module 473.7 Workflow of the Proposed Framework 483.8 Conclusion and Future Scope 49References 504 Municipal Solid Waste Energy: An Option for Green Technology for Smart Cities 53Soumitra Mukhopadhyay4.1 Unavoidable Impacts of Nonrenewable Energy 534.2 Municipal Solid Waste Energy as Clean Energy for Smart Cities 554.2.1 Renewable Energy Options 554.2.2 Municipal Solid Waste as Renewable Energy Option for Smart Cities 564.2.3 Why Is MSW Energy Renewable? 584.2.4 Various Waste to Energy Technologies 584.3 Waste to Energy Technologies (WTE-T) 594.3.1 Incineration 594.3.2 Pyrolysis 614.3.3 Gasification 634.3.4 Anaerobic Digestion 654.3.5 Landfill with Gas Capture 664.3.6 Microbial Fuel Cell (MFC) 684.4 Integrated Solid Waste Management Systems (ISWM-S) for Smart Cities 694.5 Conclusion 70References 705 E-Waste Management and Recycling Issues: An Overview 73Simran Srivastava, Sahil Virk, Saumyadip Hazra and Souvik Ganguli5.1 Introduction 735.2 Global Status of E-Waste Management 755.3 Industrial Practices in E-Waste Management 775.4 Recycling of E-Waste 795.5 E-Waste Management Benchmarking 815.6 Future of E-Waste Management 825.7 Conclusions 83References 846 Energy Audit and Management for Green Energy 89Arjyadhara Pradhan and Babita Panda6.1 Introduction 896.2 Types of Renewable Energy 916.2.1 Solar Energy 916.2.2 Wind Energy 916.2.3 Biomass 926.2.4 Geothermal Energy 926.2.5 Ocean Energy 936.3 Energy Management 936.3.1 Types of Energy Management 946.3.1.1 Demand Side Management 946.3.1.2 Implementation of DSM 956.3.1.3 Supply Side Management 966.3.2 Ways to Improve Energy Management 976.4 Energy Audit 976.4.1 Types of Energy Audit 986.4.2 Preliminary Energy Audit 986.4.3 Detailed Energy Audit 986.4.4 Data Analysis 1006.4.5 Detailed Steps in Energy Audit 1006.5 Energy Audit in Solar Plant 1016.5.1 Technical Inspection Steps of Solar Power Plant 1036.6 Energy Conservation 1046.6.1 Energy Conservation Methods 1046.6.2 Case Study 1056.7 Conclusion 108References 1087 A Smart Energy-Efficient Support System for PV Power Plants 111Salwa Ammach and Saeed Mian Qaisar7.1 Introduction 1127.2 Literature Review 1187.2.1 Solar Tracking System 1197.2.2 Solar Cleaning Mechanisms 1207.2.3 Hotspots Detection 1237.3 Proposed Solution 1317.3.1 Solar Tracking 1317.3.2 Cleaning System 1367.3.3 Hotspots 1367.3.4 Modeling and Simulation 1367.3.5 Limitations 1377.3.6 Hypothesis 1377.4 Conclusion 138References 1388 A New Hybrid Proposition Based on a Cuckoo Search Algorithm for Parameter Estimation of Solar Cells 143Souvik Ganguli, Shilpy Goyal and Parag Nijhawan8.1 Introduction 1448.2 Modelling of an Amended Double Diode Model (ADDM) and the Objective Function 1458.3 Proposed Work 1498.4 Results and Discussions 1498.5 Conclusions 161References 1629 Supervisory Digital Feedback Control System for An Effective PV Management and Battery Integration 165Amal E. Abdel Gawad, Nehal A. Alyamani and Saeed Mian Qaisar9.1 Introduction 1669.2 Literature Review 1739.2.1 GHI in the Middle East 1739.2.2 Types of PV Systems 1739.2.3 Solar Tracking Systems 1769.2.4 Charger Controller 1799.2.5 Series Regulator 1799.2.6 Shunt Regulator 1809.2.7 Pulse Width Modulation 1809.2.8 Maximum Power Point Tracker Charger Controller 1819.2.9 Reducing the Charging Time 1829.2.10 Dust Remover 1839.3 Proposed Solution 1859.3.1 Single Axis Solar Tracking System 1869.3.2 Supervisory Digital Feedback Solar Tracker Control System 1869.3.3 Database-Based Digital Solar Tracker Control System 1879.3.4 Soiling Treatment Module 1879.3.5 PV-to-Battery Switching Module 1879.4 Discussion 1899.5 Conclusion 191References 19110 Performance Analysis of Tunnel Field Effect Transistor for Low-Power Applications 195Deepak Kumar, Shiromani Balmukund Rahi and Neha Paras10.1 Introduction 19610.1.1 Limitation of Conventional MOSFET 19910.1.2 Subthreshold Slope Devices 19910.2 TFET Structure and Simulation Setup 20110.3 TFET Working Principle 20310.3.1 Transport Mechanism in TFET 20510.3.1.1 Band to Band (BTB) Tunneling Transmission 20510.3.1.2 Kane’s Model 20810.4 Subthreshold Swing (SS) in Tunnel FETs 20910.5 Performance of Hetrojunction Tunnel FET 21410.5.1 Transfer Characteristics Analysis of TFET Devices 21410.5.2 Frequency Analysis of TFET Devices 21910.6 Conclusion 221References 22211 Low-Power Integrated Circuit Smart Device Design 227Shasanka Sekhar Rout, Salony Mahapatro, Gaurav Jayaswal and Manish Hooda11.1 Introduction 22811.2 Need of Low Power 22911.3 Design Techniques of Low Power 23011.3.1 Power Optimization by IC System 23011.3.2 Power Optimization by Algorithm Section 23111.3.3 Power Optimization by Architecture Design 23111.3.4 Power Optimization by Circuit Level 23111.3.5 Power Optimization by Process Technology 23111.4 VLSI Circuit Design for Low Power 23211.4.1 Power Dissipation of CMOS Inverter 23211.4.1.1 Static Power 23211.4.1.2 Dynamic Power 23311.4.1.3 Short Circuit Power Dissipation 23311.4.1.4 Other Power Issue 23311.4.2 Capacitance Estimation of CMOS Logic Gate 23411.5 Circuit Techniques for Low Power 23411.5.1 Static Power Technique 23411.5.1.1 Self-Reverse Biasing 23411.5.1.2 Multithreshold Voltage Technique 23511.5.2 Dynamic Power Technique 23511.6 Random Access Memory (RAM) Circuits for Low Power 23611.6.1 Low-Power Techniques for SRAM 23611.6.2 Low-Power Techniques for DRAM 23711.7 VLSI Design Methodologies for Low Power 23711.7.1 Low-Power Physical Design 23711.7.2 Low-Power Gate Level Design 23711.7.2.1 Technology Mapping and Logic Minimization 23811.7.2.2 Reduction of Spurious Transitions 23811.7.2.3 Power Reduction by Precomputation 23811.7.3 Low-Power Architecture Level Design 23811.8 Power Reduction by Algorithmic Level 23911.8.1 Lowering in Switched Capacitance 23911.8.2 Lowering in Switching Activities 23911.9 Power Estimation Technique 23911.9.1 Circuit Level Tool 23911.9.2 Gate Level 24011.9.3 Architectural Level 24011.9.4 Behavioral Level 24011.10 Low-Power Flood Sensor Design 24011.11 Low-Power VCO Design 24111.12 Low-Power Gilbert Mixer Design 24111.13 Conclusion 243References 24312 GaN Technology Analysis as a Greater Mobile Semiconductor: An Overview 247Biyyapu Sai Vamsi, Tarun Chaudhary, Deepti Kakkar, Amit Tiwari and Manish Sharma12.1 Introduction 24812.2 Research and Collected Data 25012.3 Studies Reviewed and Findings 25512.4 Conclusion 266References 26613 Multilevel Distributed Energy Efficient Clustering Protocol for Relay Node Selection in Three-Tiered Architecture 269Deepti Kakkar, Gurjot Kaur and Aradhana Tirkey13.1 Introduction 27013.1.1 Overview 27013.1.2 Routing Challenges and Design Issues 27113.1.3 Heterogeneous Wireless Sensor Networks (HWSNs) 27213.1.3.1 Clustering in WSN 27313.1.4 Relay Node Selection Scheme 27413.1.5 Genetic Algorithm 27513.1.6 Problem Definition and Motivation 27513.1.7 Proposed Work 27613.2 Implementation of Proposed Relay Node Selection Based on GA 27613.2.1 Network Model 27613.2.2 Heterogenous Network Model 27713.2.3 Radio Energy Dissipation Model 27913.2.4 GA-Based Relay Node Selection 27913.2.5 Steady State Phase or Data Communication Phase 28213.3 Results of Simulation For Energy Consumption, Lifetime and Throughput of Network 28213.3.1 Simulation Setup 28213.3.2 Comparison of Residual Energy Consumption 28413.3.3 Comparison of Lifetime of Network 28413.3.4 Comparison of Throughput at BS 28613.4 Conclusion and Future Scope 287References 28814 Privacy and Security of Smart Systems 291K. Suresh Kumar, D. Prabakaran, R. Senthil Kumaran and I. Yamuna14.1 Smart Systems—An Overview 29114.2 Security and Privacy Challenges in Smart Systems 29214.2.1 Botnet Activities in Smart Systems 29414.2.2 Threats of Nonhuman-Operated Cars 29414.2.3 Privacy Issues of Virtual Reality 29414.3 Case Studies—Security Breaches in Smart Systems 29414.3.1 Breaching Smart Surveillance Cameras 29514.3.2 Hacking Smart Televisions 29514.3.3 Hacked Smart Bulbs 29514.3.4 Vulnerable Smart Homes 29614.3.5 Identity Stealing using Smart Coffee Machines 29614.4 Existing Security and Privacy Protection Technologies 29614.4.1 Cryptography 29714.4.2 Biometric 29914.4.3 Block Chain Technology 30114.5 Machine Learning, Deep Learning, and Artificial Intelligence 30114.5.1 Machine Learning in Smart Systems 30114.5.2 Genetic Algorithm 30214.5.3 Deep Learning in Smart Systems 30314.5.4 Artificial Intelligence in Smart Systems 30314.6 Security Requirement for Smart Systems 30314.6.1 Thwarting of Data Leakage and Falsifications 30414.6.2 Identification and Prevention of Device Tampering 30414.6.3 Light Weight Encryption Algorithm for Authentication 30414.6.4 Access Restrictions to Users 30514.6.5 Incident Response for Entire Systems 30514.7 Instruction to Build Strong Privacy Policy 30514.7.1 Privacy Policy 30514.7.2 Definition 30614.7.3 Key Reasons Why There Is a Need for Privacy Policy 30614.8 Role of Internet in Smart Systems 30614.8.1 Home Automation 30714.8.2 Agriculture 30714.8.3 Industry 30814.8.4 Health & Lifestyle 30914.9 Frameworks, Algorithms, and Protocols for Security Enhancements 31014.9.1 Framework for the Internet of Things by Cryptography 31114.9.2 Protocols for Security Enhancements 31214.10 Design Principles of Privacy Enhancing Methodologies 31214.11 Conclusion 313References 31415 Artificial Intelligence and Blockchain Technologies for Smart City 317Jagendra Singh, Mohammad Sajid, Suneet Kumar Gupta and Raza Abbas Haidri15.1 Introduction 31815.2 Standard for Designing Smart City and Society 32215.2.1 Scalability 32215.2.2 Intelligent Health Care 32215.2.3 Flexible and Interoperable 32215.2.4 Safeguard Infrastructure 32215.2.5 Robust Environment 32315.2.6 Distribution and Sources of Energy 32315.2.7 Intelligent Infrastructure 32315.2.8 Choice-Based Backing System 32315.2.9 Monitoring of Behavior 32315.3 Blockchain and Artificial Intelligence 32315.4 Contributions and Literature Study 32415.5 Conclusion 328References 32916 Android Application for School Bus Tracking System 331S. Sriram16.1 Introduction 33116.2 Application Methods for Access 33216.2.1 Driver Portal Screen 33316.2.2 Parent Portal Screen 33416.2.3 Teachers Portal Screen 33416.3 GPS Data Processing Methodology 33516.4 GPS Working Process 33616.5 System Implementation 33616.6 Result and Discussion 33616.6.1 Reasons to Utilize Android Application for School Bus Tracking System 33716.6.1.1 Perfect Child Security 33716.6.1.2 Elaborate Operational Efficiency 33716.6.1.3 Valid Timely Maintenance 33816.6.1.4 Automating Attendance Management 33816.6.1.5 Better Staff Management 33816.6.1.6 Addressing Environmental Concerns 33816.7 Conclusion 338References 339About the Editors 341Index 343

Suman Lata Tripathi, PhD, is a professor at Lovely Professional with more than seventeen years of experience in academics. She has published more than 45 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published one edited book and currently has multiple volumes scheduled for publication, including volumes available from Wiley-Scrivener. Souvik Ganguli, PhD, is an assistant professor and received his PhD from Thapar Institute of Engineering and Technology, Patiala. With fourteen years of experience in academics and several years in industry, he has been a session chair, keynote speaker, and conference organizer for scholarly conferences, and he has published over 50 papers in academic journals. He also has coveted grants to his credit and has published a number of book chapters in edited volumes. Abhishek Kumar, PhD, is an associate professor at and obtained his PhD in the area of VLSI Design for Low Power and Secured Architecture from Lovely Professional University, India. With over 11 years of academic experience, he has published more than 30 research papers and proceedings in scholarly journals. He has also published five book chapters and one authored book. He has worked as a reviewer and fprogram committee member and editorial board member for academic and scholarly conferences and journals. Tengiz Magradze, PhD, is an electrical design advisor for WINDTHINK, head of power transmission lines projects with “Georgian State Electrosystem,” and an adjunct professor of Electrical/Power Engineering/Management at Ballsbridge University, Dominica. He has published 14 journal articles and one book and is an editorial board member for a number of academic journals.

• Preface xvList of Contributors xvii1 An Overview of the Intelligent Green Technologies for Sustainable Smart Cities 1Tanya Srivastava, Sahil Virk and Souvik Ganguli1.1 Introduction 21.2 Case Study 1: Oslo—A Smart City 51.3 Case Study 2: Chandigarh—A Smart City 51.4 Features of the Smart Cities 61.5 Well-Planned Public Spaces and Streets 61.5.1 Waste Management 61.5.2 Energy Management 71.5.3 Good Connectivity 71.5.4 Urban Residence 81.5.5 Smart Grids 81.5.6 Smart Governance 81.6 Intelligent Green Technologies 91.7 Global and National Acceptance Scenarios 131.8 Conclusions 15References 152 Artificial Intelligence for Green Energy Technology 19Shanthi Jayaraj and Meena Chinniah2.1 Introduction 192.2 Solar Energy and AI 202.3 AI Transforms Renewable Energy 232.4 IBM Solution Using AI 242.5 Hydrogen Vehicles 242.6 Wind Energy and AI 252.7 Renewable Energy Industry in India 292.8 Conclusion 30References 30Website Reference 31Abbreviations 313 Effective Waste Management System for Smart Cities 33G. Boopathi Raja3.1 Introduction 343.2 Literature Survey 363.3 Waste Management in India 373.4 Existing Methodology 403.4.1 IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System 403.4.2 IoT Enabled Solid Waste Management System 413.4.3 Smart Garbage Management System 413.5 Proposed Framework 423.5.1 System Description 423.6 Functionality of the Proposed System 443.6.1 Sensing Module 443.6.2 Storage Module 463.6.3 User Module 473.7 Workflow of the Proposed Framework 483.8 Conclusion and Future Scope 49References 504 Municipal Solid Waste Energy: An Option for Green Technology for Smart Cities 53Soumitra Mukhopadhyay4.1 Unavoidable Impacts of Nonrenewable Energy 534.2 Municipal Solid Waste Energy as Clean Energy for Smart Cities 554.2.1 Renewable Energy Options 554.2.2 Municipal Solid Waste as Renewable Energy Option for Smart Cities 564.2.3 Why Is MSW Energy Renewable? 584.2.4 Various Waste to Energy Technologies 584.3 Waste to Energy Technologies (WTE-T) 594.3.1 Incineration 594.3.2 Pyrolysis 614.3.3 Gasification 634.3.4 Anaerobic Digestion 654.3.5 Landfill with Gas Capture 664.3.6 Microbial Fuel Cell (MFC) 684.4 Integrated Solid Waste Management Systems (ISWM-S) for Smart Cities 694.5 Conclusion 70References 705 E-Waste Management and Recycling Issues: An Overview 73Simran Srivastava, Sahil Virk, Saumyadip Hazra and Souvik Ganguli5.1 Introduction 735.2 Global Status of E-Waste Management 755.3 Industrial Practices in E-Waste Management 775.4 Recycling of E-Waste 795.5 E-Waste Management Benchmarking 815.6 Future of E-Waste Management 825.7 Conclusions 83References 846 Energy Audit and Management for Green Energy 89Arjyadhara Pradhan and Babita Panda6.1 Introduction 896.2 Types of Renewable Energy 916.2.1 Solar Energy 916.2.2 Wind Energy 916.2.3 Biomass 926.2.4 Geothermal Energy 926.2.5 Ocean Energy 936.3 Energy Management 936.3.1 Types of Energy Management 946.3.1.1 Demand Side Management 946.3.1.2 Implementation of DSM 956.3.1.3 Supply Side Management 966.3.2 Ways to Improve Energy Management 976.4 Energy Audit 976.4.1 Types of Energy Audit 986.4.2 Preliminary Energy Audit 986.4.3 Detailed Energy Audit 986.4.4 Data Analysis 1006.4.5 Detailed Steps in Energy Audit 1006.5 Energy Audit in Solar Plant 1016.5.1 Technical Inspection Steps of Solar Power Plant 1036.6 Energy Conservation 1046.6.1 Energy Conservation Methods 1046.6.2 Case Study 1056.7 Conclusion 108References 1087 A Smart Energy-Efficient Support System for PV Power Plants 111Salwa Ammach and Saeed Mian Qaisar7.1 Introduction 1127.2 Literature Review 1187.2.1 Solar Tracking System 1197.2.2 Solar Cleaning Mechanisms 1207.2.3 Hotspots Detection 1237.3 Proposed Solution 1317.3.1 Solar Tracking 1317.3.2 Cleaning System 1367.3.3 Hotspots 1367.3.4 Modeling and Simulation 1367.3.5 Limitations 1377.3.6 Hypothesis 1377.4 Conclusion 138References 1388 A New Hybrid Proposition Based on a Cuckoo Search Algorithm for Parameter Estimation of Solar Cells 143Souvik Ganguli, Shilpy Goyal and Parag Nijhawan8.1 Introduction 1448.2 Modelling of an Amended Double Diode Model (ADDM) and the Objective Function 1458.3 Proposed Work 1498.4 Results and Discussions 1498.5 Conclusions 161References 1629 Supervisory Digital Feedback Control System for An Effective PV Management and Battery Integration 165Amal E. Abdel Gawad, Nehal A. Alyamani and Saeed Mian Qaisar9.1 Introduction 1669.2 Literature Review 1739.2.1 GHI in the Middle East 1739.2.2 Types of PV Systems 1739.2.3 Solar Tracking Systems 1769.2.4 Charger Controller 1799.2.5 Series Regulator 1799.2.6 Shunt Regulator 1809.2.7 Pulse Width Modulation 1809.2.8 Maximum Power Point Tracker Charger Controller 1819.2.9 Reducing the Charging Time 1829.2.10 Dust Remover 1839.3 Proposed Solution 1859.3.1 Single Axis Solar Tracking System 1869.3.2 Supervisory Digital Feedback Solar Tracker Control System 1869.3.3 Database-Based Digital Solar Tracker Control System 1879.3.4 Soiling Treatment Module 1879.3.5 PV-to-Battery Switching Module 1879.4 Discussion 1899.5 Conclusion 191References 19110 Performance Analysis of Tunnel Field Effect Transistor for Low-Power Applications 195Deepak Kumar, Shiromani Balmukund Rahi and Neha Paras10.1 Introduction 19610.1.1 Limitation of Conventional MOSFET 19910.1.2 Subthreshold Slope Devices 19910.2 TFET Structure and Simulation Setup 20110.3 TFET Working Principle 20310.3.1 Transport Mechanism in TFET 20510.3.1.1 Band to Band (BTB) Tunneling Transmission 20510.3.1.2 Kane’s Model 20810.4 Subthreshold Swing (SS) in Tunnel FETs 20910.5 Performance of Hetrojunction Tunnel FET 21410.5.1 Transfer Characteristics Analysis of TFET Devices 21410.5.2 Frequency Analysis of TFET Devices 21910.6 Conclusion 221References 22211 Low-Power Integrated Circuit Smart Device Design 227Shasanka Sekhar Rout, Salony Mahapatro, Gaurav Jayaswal and Manish Hooda11.1 Introduction 22811.2 Need of Low Power 22911.3 Design Techniques of Low Power 23011.3.1 Power Optimization by IC System 23011.3.2 Power Optimization by Algorithm Section 23111.3.3 Power Optimization by Architecture Design 23111.3.4 Power Optimization by Circuit Level 23111.3.5 Power Optimization by Process Technology 23111.4 VLSI Circuit Design for Low Power 23211.4.1 Power Dissipation of CMOS Inverter 23211.4.1.1 Static Power 23211.4.1.2 Dynamic Power 23311.4.1.3 Short Circuit Power Dissipation 23311.4.1.4 Other Power Issue 23311.4.2 Capacitance Estimation of CMOS Logic Gate 23411.5 Circuit Techniques for Low Power 23411.5.1 Static Power Technique 23411.5.1.1 Self-Reverse Biasing 23411.5.1.2 Multithreshold Voltage Technique 23511.5.2 Dynamic Power Technique 23511.6 Random Access Memory (RAM) Circuits for Low Power 23611.6.1 Low-Power Techniques for SRAM 23611.6.2 Low-Power Techniques for DRAM 23711.7 VLSI Design Methodologies for Low Power 23711.7.1 Low-Power Physical Design 23711.7.2 Low-Power Gate Level Design 23711.7.2.1 Technology Mapping and Logic Minimization 23811.7.2.2 Reduction of Spurious Transitions 23811.7.2.3 Power Reduction by Precomputation 23811.7.3 Low-Power Architecture Level Design 23811.8 Power Reduction by Algorithmic Level 23911.8.1 Lowering in Switched Capacitance 23911.8.2 Lowering in Switching Activities 23911.9 Power Estimation Technique 23911.9.1 Circuit Level Tool 23911.9.2 Gate Level 24011.9.3 Architectural Level 24011.9.4 Behavioral Level 24011.10 Low-Power Flood Sensor Design 24011.11 Low-Power VCO Design 24111.12 Low-Power Gilbert Mixer Design 24111.13 Conclusion 243References 24312 GaN Technology Analysis as a Greater Mobile Semiconductor: An Overview 247Biyyapu Sai Vamsi, Tarun Chaudhary, Deepti Kakkar, Amit Tiwari and Manish Sharma12.1 Introduction 24812.2 Research and Collected Data 25012.3 Studies Reviewed and Findings 25512.4 Conclusion 266References 26613 Multilevel Distributed Energy Efficient Clustering Protocol for Relay Node Selection in Three-Tiered Architecture 269Deepti Kakkar, Gurjot Kaur and Aradhana Tirkey13.1 Introduction 27013.1.1 Overview 27013.1.2 Routing Challenges and Design Issues 27113.1.3 Heterogeneous Wireless Sensor Networks (HWSNs) 27213.1.3.1 Clustering in WSN 27313.1.4 Relay Node Selection Scheme 27413.1.5 Genetic Algorithm 27513.1.6 Problem Definition and Motivation 27513.1.7 Proposed Work 27613.2 Implementation of Proposed Relay Node Selection Based on GA 27613.2.1 Network Model 27613.2.2 Heterogenous Network Model 27713.2.3 Radio Energy Dissipation Model 27913.2.4 GA-Based Relay Node Selection 27913.2.5 Steady State Phase or Data Communication Phase 28213.3 Results of Simulation For Energy Consumption, Lifetime and Throughput of Network 28213.3.1 Simulation Setup 28213.3.2 Comparison of Residual Energy Consumption 28413.3.3 Comparison of Lifetime of Network 28413.3.4 Comparison of Throughput at BS 28613.4 Conclusion and Future Scope 287References 28814 Privacy and Security of Smart Systems 291K. Suresh Kumar, D. Prabakaran, R. Senthil Kumaran and I. Yamuna14.1 Smart Systems—An Overview 29114.2 Security and Privacy Challenges in Smart Systems 29214.2.1 Botnet Activities in Smart Systems 29414.2.2 Threats of Nonhuman-Operated Cars 29414.2.3 Privacy Issues of Virtual Reality 29414.3 Case Studies—Security Breaches in Smart Systems 29414.3.1 Breaching Smart Surveillance Cameras 29514.3.2 Hacking Smart Televisions 29514.3.3 Hacked Smart Bulbs 29514.3.4 Vulnerable Smart Homes 29614.3.5 Identity Stealing using Smart Coffee Machines 29614.4 Existing Security and Privacy Protection Technologies 29614.4.1 Cryptography 29714.4.2 Biometric 29914.4.3 Block Chain Technology 30114.5 Machine Learning, Deep Learning, and Artificial Intelligence 30114.5.1 Machine Learning in Smart Systems 30114.5.2 Genetic Algorithm 30214.5.3 Deep Learning in Smart Systems 30314.5.4 Artificial Intelligence in Smart Systems 30314.6 Security Requirement for Smart Systems 30314.6.1 Thwarting of Data Leakage and Falsifications 30414.6.2 Identification and Prevention of Device Tampering 30414.6.3 Light Weight Encryption Algorithm for Authentication 30414.6.4 Access Restrictions to Users 30514.6.5 Incident Response for Entire Systems 30514.7 Instruction to Build Strong Privacy Policy 30514.7.1 Privacy Policy 30514.7.2 Definition 30614.7.3 Key Reasons Why There Is a Need for Privacy Policy 30614.8 Role of Internet in Smart Systems 30614.8.1 Home Automation 30714.8.2 Agriculture 30714.8.3 Industry 30814.8.4 Health & Lifestyle 30914.9 Frameworks, Algorithms, and Protocols for Security Enhancements 31014.9.1 Framework for the Internet of Things by Cryptography 31114.9.2 Protocols for Security Enhancements 31214.10 Design Principles of Privacy Enhancing Methodologies 31214.11 Conclusion 313References 31415 Artificial Intelligence and Blockchain Technologies for Smart City 317Jagendra Singh, Mohammad Sajid, Suneet Kumar Gupta and Raza Abbas Haidri15.1 Introduction 31815.2 Standard for Designing Smart City and Society 32215.2.1 Scalability 32215.2.2 Intelligent Health Care 32215.2.3 Flexible and Interoperable 32215.2.4 Safeguard Infrastructure 32215.2.5 Robust Environment 32315.2.6 Distribution and Sources of Energy 32315.2.7 Intelligent Infrastructure 32315.2.8 Choice-Based Backing System 32315.2.9 Monitoring of Behavior 32315.3 Blockchain and Artificial Intelligence 32315.4 Contributions and Literature Study 32415.5 Conclusion 328References 32916 Android Application for School Bus Tracking System 331S. Sriram16.1 Introduction 33116.2 Application Methods for Access 33216.2.1 Driver Portal Screen 33316.2.2 Parent Portal Screen 33416.2.3 Teachers Portal Screen 33416.3 GPS Data Processing Methodology 33516.4 GPS Working Process 33616.5 System Implementation 33616.6 Result and Discussion 33616.6.1 Reasons to Utilize Android Application for School Bus Tracking System 33716.6.1.1 Perfect Child Security 33716.6.1.2 Elaborate Operational Efficiency 33716.6.1.3 Valid Timely Maintenance 33816.6.1.4 Automating Attendance Management 33816.6.1.5 Better Staff Management 33816.6.1.6 Addressing Environmental Concerns 33816.7 Conclusion 338References 339About the Editors 341Index 343

Suman Lata Tripathi, PhD, is a professor at Lovely Professional with more than seventeen years of experience in academics. She has published more than 45 research papers in refereed journals and conferences. She has organized several workshops, summer internships, and expert lectures for students, and she has worked as a session chair, conference steering committee member, editorial board member, and reviewer for IEEE journals and conferences. She has published one edited book and currently has multiple volumes scheduled for publication, including volumes available from Wiley-Scrivener. Souvik Ganguli, PhD, is an assistant professor and received his PhD from Thapar Institute of Engineering and Technology, Patiala. With fourteen years of experience in academics and several years in industry, he has been a session chair, keynote speaker, and conference organizer for scholarly conferences, and he has published over 50 papers in academic journals. He also has coveted grants to his credit and has published a number of book chapters in edited volumes. Abhishek Kumar, PhD, is an associate professor at and obtained his PhD in the area of VLSI Design for Low Power and Secured Architecture from Lovely Professional University, India. With over 11 years of academic experience, he has published more than 30 research papers and proceedings in scholarly journals. He has also published five book chapters and one authored book. He has worked as a reviewer and fprogram committee member and editorial board member for academic and scholarly conferences and journals. Tengiz Magradze, PhD, is an electrical design advisor for WINDTHINK, head of power transmission lines projects with “Georgian State Electrosystem,” and an adjunct professor of Electrical/Power Engineering/Management at Ballsbridge University, Dominica. He has published 14 journal articles and one book and is an editorial board member for a number of academic journals.

• Preface xvList of Contributors xvii1 An Overview of the Intelligent Green Technologies for Sustainable Smart Cities 1Tanya Srivastava, Sahil Virk and Souvik Ganguli1.1 Introduction 21.2 Case Study 1: Oslo—A Smart City 51.3 Case Study 2: Chandigarh—A Smart City 51.4 Features of the Smart Cities 61.5 Well-Planned Public Spaces and Streets 61.5.1 Waste Management 61.5.2 Energy Management 71.5.3 Good Connectivity 71.5.4 Urban Residence 81.5.5 Smart Grids 81.5.6 Smart Governance 81.6 Intelligent Green Technologies 91.7 Global and National Acceptance Scenarios 131.8 Conclusions 15References 152 Artificial Intelligence for Green Energy Technology 19Shanthi Jayaraj and Meena Chinniah2.1 Introduction 192.2 Solar Energy and AI 202.3 AI Transforms Renewable Energy 232.4 IBM Solution Using AI 242.5 Hydrogen Vehicles 242.6 Wind Energy and AI 252.7 Renewable Energy Industry in India 292.8 Conclusion 30References 30Website Reference 31Abbreviations 313 Effective Waste Management System for Smart Cities 33G. Boopathi Raja3.1 Introduction 343.2 Literature Survey 363.3 Waste Management in India 373.4 Existing Methodology 403.4.1 IoT-Based Smart Waste Bin Monitoring and Municipal Solid Waste Management System 403.4.2 IoT Enabled Solid Waste Management System 413.4.3 Smart Garbage Management System 413.5 Proposed Framework 423.5.1 System Description 423.6 Functionality of the Proposed System 443.6.1 Sensing Module 443.6.2 Storage Module 463.6.3 User Module 473.7 Workflow of the Proposed Framework 483.8 Conclusion and Future Scope 49References 504 Municipal Solid Waste Energy: An Option for Green Technology for Smart Cities 53Soumitra Mukhopadhyay4.1 Unavoidable Impacts of Nonrenewable Energy 534.2 Municipal Solid Waste Energy as Clean Energy for Smart Cities 554.2.1 Renewable Energy Options 554.2.2 Municipal Solid Waste as Renewable Energy Option for Smart Cities 564.2.3 Why Is MSW Energy Renewable? 584.2.4 Various Waste to Energy Technologies 584.3 Waste to Energy Technologies (WTE-T) 594.3.1 Incineration 594.3.2 Pyrolysis 614.3.3 Gasification 634.3.4 Anaerobic Digestion 654.3.5 Landfill with Gas Capture 664.3.6 Microbial Fuel Cell (MFC) 684.4 Integrated Solid Waste Management Systems (ISWM-S) for Smart Cities 694.5 Conclusion 70References 705 E-Waste Management and Recycling Issues: An Overview 73Simran Srivastava, Sahil Virk, Saumyadip Hazra and Souvik Ganguli5.1 Introduction 735.2 Global Status of E-Waste Management 755.3 Industrial Practices in E-Waste Management 775.4 Recycling of E-Waste 795.5 E-Waste Management Benchmarking 815.6 Future of E-Waste Management 825.7 Conclusions 83References 846 Energy Audit and Management for Green Energy 89Arjyadhara Pradhan and Babita Panda6.1 Introduction 896.2 Types of Renewable Energy 916.2.1 Solar Energy 916.2.2 Wind Energy 916.2.3 Biomass 926.2.4 Geothermal Energy 926.2.5 Ocean Energy 936.3 Energy Management 936.3.1 Types of Energy Management 946.3.1.1 Demand Side Management 946.3.1.2 Implementation of DSM 956.3.1.3 Supply Side Management 966.3.2 Ways to Improve Energy Management 976.4 Energy Audit 976.4.1 Types of Energy Audit 986.4.2 Preliminary Energy Audit 986.4.3 Detailed Energy Audit 986.4.4 Data Analysis 1006.4.5 Detailed Steps in Energy Audit 1006.5 Energy Audit in Solar Plant 1016.5.1 Technical Inspection Steps of Solar Power Plant 1036.6 Energy Conservation 1046.6.1 Energy Conservation Methods 1046.6.2 Case Study 1056.7 Conclusion 108References 1087 A Smart Energy-Efficient Support System for PV Power Plants 111Salwa Ammach and Saeed Mian Qaisar7.1 Introduction 1127.2 Literature Review 1187.2.1 Solar Tracking System 1197.2.2 Solar Cleaning Mechanisms 1207.2.3 Hotspots Detection 1237.3 Proposed Solution 1317.3.1 Solar Tracking 1317.3.2 Cleaning System 1367.3.3 Hotspots 1367.3.4 Modeling and Simulation 1367.3.5 Limitations 1377.3.6 Hypothesis 1377.4 Conclusion 138References 1388 A New Hybrid Proposition Based on a Cuckoo Search Algorithm for Parameter Estimation of Solar Cells 143Souvik Ganguli, Shilpy Goyal and Parag Nijhawan8.1 Introduction 1448.2 Modelling of an Amended Double Diode Model (ADDM) and the Objective Function 1458.3 Proposed Work 1498.4 Results and Discussions 1498.5 Conclusions 161References 1629 Supervisory Digital Feedback Control System for An Effective PV Management and Battery Integration 165Amal E. Abdel Gawad, Nehal A. Alyamani and Saeed Mian Qaisar9.1 Introduction 1669.2 Literature Review 1739.2.1 GHI in the Middle East 1739.2.2 Types of PV Systems 1739.2.3 Solar Tracking Systems 1769.2.4 Charger Controller 1799.2.5 Series Regulator 1799.2.6 Shunt Regulator 1809.2.7 Pulse Width Modulation 1809.2.8 Maximum Power Point Tracker Charger Controller 1819.2.9 Reducing the Charging Time 1829.2.10 Dust Remover 1839.3 Proposed Solution 1859.3.1 Single Axis Solar Tracking System 1869.3.2 Supervisory Digital Feedback Solar Tracker Control System 1869.3.3 Database-Based Digital Solar Tracker Control System 1879.3.4 Soiling Treatment Module 1879.3.5 PV-to-Battery Switching Module 1879.4 Discussion 1899.5 Conclusion 191References 19110 Performance Analysis of Tunnel Field Effect Transistor for Low-Power Applications 195Deepak Kumar, Shiromani Balmukund Rahi and Neha Paras10.1 Introduction 19610.1.1 Limitation of Conventional MOSFET 19910.1.2 Subthreshold Slope Devices 19910.2 TFET Structure and Simulation Setup 20110.3 TFET Working Principle 20310.3.1 Transport Mechanism in TFET 20510.3.1.1 Band to Band (BTB) Tunneling Transmission 20510.3.1.2 Kane’s Model 20810.4 Subthreshold Swing (SS) in Tunnel FETs 20910.5 Performance of Hetrojunction Tunnel FET 21410.5.1 Transfer Characteristics Analysis of TFET Devices 21410.5.2 Frequency Analysis of TFET Devices 21910.6 Conclusion 221References 22211 Low-Power Integrated Circuit Smart Device Design 227Shasanka Sekhar Rout, Salony Mahapatro, Gaurav Jayaswal and Manish Hooda11.1 Introduction 22811.2 Need of Low Power 22911.3 Design Techniques of Low Power 23011.3.1 Power Optimization by IC System 23011.3.2 Power Optimization by Algorithm Section 23111.3.3 Power Optimization by Architecture Design 23111.3.4 Power Optimization by Circuit Level 23111.3.5 Power Optimization by Process Technology 23111.4 VLSI Circuit Design for Low Power 23211.4.1 Power Dissipation of CMOS Inverter 23211.4.1.1 Static Power 23211.4.1.2 Dynamic Power 23311.4.1.3 Short Circuit Power Dissipation 23311.4.1.4 Other Power Issue 23311.4.2 Capacitance Estimation of CMOS Logic Gate 23411.5 Circuit Techniques for Low Power 23411.5.1 Static Power Technique 23411.5.1.1 Self-Reverse Biasing 23411.5.1.2 Multithreshold Voltage Technique 23511.5.2 Dynamic Power Technique 23511.6 Random Access Memory (RAM) Circuits for Low Power 23611.6.1 Low-Power Techniques for SRAM 23611.6.2 Low-Power Techniques for DRAM 23711.7 VLSI Design Methodologies for Low Power 23711.7.1 Low-Power Physical Design 23711.7.2 Low-Power Gate Level Design 23711.7.2.1 Technology Mapping and Logic Minimization 23811.7.2.2 Reduction of Spurious Transitions 23811.7.2.3 Power Reduction by Precomputation 23811.7.3 Low-Power Architecture Level Design 23811.8 Power Reduction by Algorithmic Level 23911.8.1 Lowering in Switched Capacitance 23911.8.2 Lowering in Switching Activities 23911.9 Power Estimation Technique 23911.9.1 Circuit Level Tool 23911.9.2 Gate Level 24011.9.3 Architectural Level 24011.9.4 Behavioral Level 24011.10 Low-Power Flood Sensor Design 24011.11 Low-Power VCO Design 24111.12 Low-Power Gilbert Mixer Design 24111.13 Conclusion 243References 24312 GaN Technology Analysis as a Greater Mobile Semiconductor: An Overview 247Biyyapu Sai Vamsi, Tarun Chaudhary, Deepti Kakkar, Amit Tiwari and Manish Sharma12.1 Introduction 24812.2 Research and Collected Data 25012.3 Studies Reviewed and Findings 25512.4 Conclusion 266References 26613 Multilevel Distributed Energy Efficient Clustering Protocol for Relay Node Selection in Three-Tiered Architecture 269Deepti Kakkar, Gurjot Kaur and Aradhana Tirkey13.1 Introduction 27013.1.1 Overview 27013.1.2 Routing Challenges and Design Issues 27113.1.3 Heterogeneous Wireless Sensor Networks (HWSNs) 27213.1.3.1 Clustering in WSN 27313.1.4 Relay Node Selection Scheme 27413.1.5 Genetic Algorithm 27513.1.6 Problem Definition and Motivation 27513.1.7 Proposed Work 27613.2 Implementation of Proposed Relay Node Selection Based on GA 27613.2.1 Network Model 27613.2.2 Heterogenous Network Model 27713.2.3 Radio Energy Dissipation Model 27913.2.4 GA-Based Relay Node Selection 27913.2.5 Steady State Phase or Data Communication Phase 28213.3 Results of Simulation For Energy Consumption, Lifetime and Throughput of Network 28213.3.1 Simulation Setup 28213.3.2 Comparison of Residual Energy Consumption 28413.3.3 Comparison of Lifetime of Network 28413.3.4 Comparison of Throughput at BS 28613.4 Conclusion and Future Scope 287References 28814 Privacy and Security of Smart Systems 291K. Suresh Kumar, D. Prabakaran, R. Senthil Kumaran and I. Yamuna14.1 Smart Systems—An Overview 29114.2 Security and Privacy Challenges in Smart Systems 29214.2.1 Botnet Activities in Smart Systems 29414.2.2 Threats of Nonhuman-Operated Cars 29414.2.3 Privacy Issues of Virtual Reality 29414.3 Case Studies—Security Breaches in Smart Systems 29414.3.1 Breaching Smart Surveillance Cameras 29514.3.2 Hacking Smart Televisions 29514.3.3 Hacked Smart Bulbs 29514.3.4 Vulnerable Smart Homes 29614.3.5 Identity Stealing using Smart Coffee Machines 29614.4 Existing Security and Privacy Protection Technologies 29614.4.1 Cryptography 29714.4.2 Biometric 29914.4.3 Block Chain Technology 30114.5 Machine Learning, Deep Learning, and Artificial Intelligence 30114.5.1 Machine Learning in Smart Systems 30114.5.2 Genetic Algorithm 30214.5.3 Deep Learning in Smart Systems 30314.5.4 Artificial Intelligence in Smart Systems 30314.6 Security Requirement for Smart Systems 30314.6.1 Thwarting of Data Leakage and Falsifications 30414.6.2 Identification and Prevention of Device Tampering 30414.6.3 Light Weight Encryption Algorithm for Authentication 30414.6.4 Access Restrictions to Users 30514.6.5 Incident Response for Entire Systems 30514.7 Instruction to Build Strong Privacy Policy 30514.7.1 Privacy Policy 30514.7.2 Definition 30614.7.3 Key Reasons Why There Is a Need for Privacy Policy 30614.8 Role of Internet in Smart Systems 30614.8.1 Home Automation 30714.8.2 Agriculture 30714.8.3 Industry 30814.8.4 Health & Lifestyle 30914.9 Frameworks, Algorithms, and Protocols for Security Enhancements 31014.9.1 Framework for the Internet of Things by Cryptography 31114.9.2 Protocols for Security Enhancements 31214.10 Design Principles of Privacy Enhancing Methodologies 31214.11 Conclusion 313References 31415 Artificial Intelligence and Blockchain Technologies for Smart City 317Jagendra Singh, Mohammad Sajid, Suneet Kumar Gupta and Raza Abbas Haidri15.1 Introduction 31815.2 Standard for Designing Smart City and Society 32215.2.1 Scalability 32215.2.2 Intelligent Health Care 32215.2.3 Flexible and Interoperable 32215.2.4 Safeguard Infrastructure 32215.2.5 Robust Environment 32315.2.6 Distribution and Sources of Energy 32315.2.7 Intelligent Infrastructure 32315.2.8 Choice-Based Backing System 32315.2.9 Monitoring of Behavior 32315.3 Blockchain and Artificial Intelligence 32315.4 Contributions and Literature Study 32415.5 Conclusion 328References 32916 Android Application for School Bus Tracking System 331S. Sriram16.1 Introduction 33116.2 Application Methods for Access 33216.2.1 Driver Portal Screen 33316.2.2 Parent Portal Screen 33416.2.3 Teachers Portal Screen 33416.3 GPS Data Processing Methodology 33516.4 GPS Working Process 33616.5 System Implementation 33616.6 Result and Discussion 33616.6.1 Reasons to Utilize Android Application for School Bus Tracking System 33716.6.1.1 Perfect Child Security 33716.6.1.2 Elaborate Operational Efficiency 33716.6.1.3 Valid Timely Maintenance 33816.6.1.4 Automating Attendance Management 33816.6.1.5 Better Staff Management 33816.6.1.6 Addressing Environmental Concerns 33816.7 Conclusion 338References 339About the Editors 341Index 343