Arun Solanki PhD is an assistant professor in the Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, India, where he has been working since 2009. His research interests span expert systems, machine learning, and search engines. He has published many research articles in international journals/conferences.Adarsh Kumar PhD is an associate professor at the University of Petroleum & Energy Studies, Dehradun, India. His main research interests are cybersecurity, cryptography, network security, and ad-hoc networks. He has published 60+ research papers in reputed journals, conferences and workshops.Anand Nayyar PhD is currently working in the Graduate School, Duy Tan University, Da Nang, Vietnam. He is a certified professional with more than 75 Professional Certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam, and many more. He published more than 300 research articles in various national and international journals and conferences. He has authored, coauthored or edited about 30 books and has been granted two patents in the areas of Internet of Things and speech processing.

• Preface xix1 The Use of Machine Learning for Sustainable and Resilient Buildings 1Kuldeep Singh Kaswan and Jagjit Singh Dhatterwal1.1 Introduction of ML Sustainable Resilient Building 21.2 Related Works 21.3 Machine Learning 51.4 What is Resilience? 61.4.1 Sustainability and Resiliency Conditions 71.4.2 Paradigm and Challenges of Sustainability and Resilience 71.4.3 Perspectives of Local Community 91.5 Sustainability and Resilience of Engineered System 121.5.1 Resilience and Sustainable Development Framework for Decision-Making 131.5.2 Exposures and Disturbance Events 151.5.3 Quantification of Resilience 151.5.4 Quantification of Sustainability 161.6 Community and Quantification Metrics, Resilience and Sustainability Objectives 171.6.1 Definition of Quantification Metric 181.6.2 Considering and Community 191.7 Structure Engineering Dilemmas and Resilient Epcot 211.7.1 Dilation of Resilience Essence 211.7.2 Quality of Life 221.8 Development of Risk Informed Criteria for Building Design Hurricane Resilient on Building 271.9 Resilient Infrastructures Against Earthquake and Tsunami Multi-Hazard 281.10 Machine Learning With Smart Building 291.10.1 Smart Building Appliances 291.10.2 Intelligent Tools, Cameras and Electronic Controls in a Connected House (SRB) 291.10.3 Level if Clouds are the IoT Institute Level With SBs 311.10.4 Component of Smart Buildings (SB) 331.10.5 Machine Learning Tasks in Smart Building Environment 461.10.6 ML Tools and Services for Smart Building 471.10.7 Big Data Research Applications for SBs in Real-Time 511.10.8 Implementation of the ML Concept in the SB Context 511.11 Conclusion and Future Research 53References 582 Fire Hazard Detection and Prediction by Machine Learning Techniques in Smart Buildings (SBs) Using Sensors and Unmanned Aerial Vehicles (UAVs) 63Sandhya Tarar and Namisha Bhasin2.1 Introduction 642.1.1 Bluetooth 652.1.2 Unmanned Aerial Vehicle 652.1.3 Sensors 652.1.4 Problem Description 672.2 Literature Review 682.3 Experimental Methods 712.3.1 Univariate Time-Series 732.3.1.1 Naïve Bayes 742.3.1.2 Simple Average 742.3.1.3 Moving Average 752.3.1.4 Simple Exponential Smoothing (SES) 762.3.1.5 Holt’s Linear Trend 762.3.1.6 Holt–Winters Method 762.3.1.7 Autoregressive Integrated Moving Average Model (ARIMA) 772.3.2 Multivariate Time-Series Prediction 802.3.2.1 Vector Autoregressive (VAR) 802.3.3 Hidden Markov Model (HMM) 812.3.4 Fuzzy Logic 852.4 Results 892.5 Conclusion and Future Work 89References 903 Sustainable Infrastructure Theories and Models 97Saurabh Jain, Keshav Kaushik, Deepak Kumar Sharma, Rajalakshmi Krishnamurthi and Adarsh Kumar3.1 Introduction to Data Fusion Approaches in Sustainable Infrastructure 983.1.1 The Need for Sustainable Infrastructure 983.1.2 Data Fusion 993.1.3 Different Types of Data Fusion Architecture 1003.1.3.1 Centralized Architecture 1003.1.3.2 Decentralized Architecture 1013.1.3.3 Distributed Architecture 1013.1.3.4 Hierarchical Architecture 1023.1.4 Smart Cities Application With Sustainable Infrastructures Based on Different Data Fusion Techniques 1023.2 Smart City Infrastructure Approaches 1043.2.1 Smart City Infrastructure 1043.2.2 Smart City IoT Deployments 1053.2.3 Smart City Control and Monitoring Centers 1063.2.4 Theory of Unified City Modeling for Smart Infrastructure 1083.2.5 Smart City Operational Modeling 1093.3 Theories and Models 1103.3.1 Sustainable Infrastructure Theories 1103.3.2 Sustainable Infrastructure Models 1123.4 Case Studies 1133.4.1 Case Studies-1: Web Browsing History Analysis 1133.4.1.1 Objective 1153.4.2 Case Study-2: Data Model for Group Construction in Student’s Industrial Placement 1173.5 Conclusion and Future Scope 121References 1224 Blockchain for Sustainable Smart Cities 127Iftikhar Ahmad, Syeda Warda Ashar, Umamma Khalid, Anmol Irfan and Wajeeha Khalil4.1 Introduction 1284.2 Smart City 1304.2.1 Overview of Smart City 1304.2.2 Evolution 1304.2.3 Smart City’s Sub Systems 1304.2.4 Domains of Smart City 1324.2.5 Challenges 1344.3 Blockchain 1364.3.1 Motivation 1374.3.2 The Birth of Blockchain 1374.3.3 System of Blockchain 1374.4 Use Cases of Smart City Implementing Blockchain 1384.4.1 Blockchain-Based Smart Economy 1384.4.1.1 Facilitating Faster and Cheaper International Payment 1394.4.1.2 Distributed Innovations in Financial Transactions 1394.4.1.3 Enhancing the Transparency of Supply/Global Commodity Chains 1404.4.1.4 Equity Crowd Funding 1414.4.2 Blockchain for Smart People 1414.4.2.1 Elections through Blockchain Technology 1414.4.2.2 Smart Contract 1434.4.2.3 Protecting Personal Data 1444.4.2.4 E-Health: Storing Health Records on Blockchain 1454.4.2.5 Intellectual Property Rights 1454.4.2.6 Digital Payments 1464.4.2.7 Other Use Cases 1464.4.3 Blockchain-Based Smart Governance 1474.4.3.1 Transparent Record Keeping and Tracking of Records 1474.4.3.2 Fraud Free Voting 1484.4.3.3 Decision Making 1504.4.4 Blockchain-Based Smart Transport 1504.4.4.1 Digitizing Driving License 1504.4.4.2 Smart Ride Sharing 1504.4.5 Blockchain-Based Smart Environment 1514.4.5.1 Social Plastic 1514.4.5.2 Energy 1524.4.5.3 Environmental Treaties 1524.4.5.4 Carbon Tax 1534.4.6 Blockchain-Based Smart Living 1534.4.6.1 Fighting Against Frauds and Discriminatory Policies and Practices 1544.4.6.2 Managing Change in Ownership 1544.4.6.3 Sustainable Buildings 1544.4.6.4 Other Use Cases 1554.5 Conclusion 156References 1565 Contextualizing Electronic Governance, Smart City Governance and Sustainable Infrastructure in India: A Study and Framework 163Nitin K. Tyagi and Mukta Goyal5.1 Introduction 1645.2 Related Works 1665.2.1 Research Questions 1665.3 Related E-Governance Frameworks 1785.3.1 Smart City Features in India 1815.4 Proposed Smart Governance Framework 1815.5 Results Discussion 1855.5.1 Initial Stage 1855.5.2 Design, Development and Delivery Stage 1865.6 Conclusion 186References 1886 Revolutionizing Geriatric Design in Developing Countries: IoT-Enabled Smart Home Design for the Elderly 193Shubhi Sonal and Anupadma R.6.1 Introduction to Geriatric Design 1946.1.1 Aim, Objectives, and Methodology 1966.1.2 Organization of Chapter 1976.2 Background 1976.2.1 Development of Smart Homes 1976.2.2 Development of Smart Homes for Elderly 1986.2.3 Indian Scenario 2006.3 Need for Smart Homes: An Assessment of Requirements for the Elderly-Activity Mapping 2016.3.1 Geriatric Smart Home Design: The Indian Context 2026.3.2 Elderly Activity Mapping 2026.3.3 Framework for Smart Homes for Elderly People 2066.3.4 Architectural Interventions: Spatial Requirements for Daily Activities 2076.3.5 Architectural Interventions to Address Issues Faced by Elderly People 2086.4 Schematic Design for a Nesting Home: IoT-Enabled Smart Home for Elderly People 2086.4.1 IoT-Based Real Time Automation for Nesting Homes 2086.4.2 Technological Components of Elderly Smart Homes 2126.4.2.1 Sensors for Smart Home 2126.4.2.2 Health Monitoring System 2136.4.2.3 Network Devices 2136.4.2.4 Alerts 2146.5 Worldwide Elderly Smart Homes 2146.5.1 Challenges in Smart Elderly Homes 2156.6 Conclusion and Future Scope 216References 2167 Sustainable E-Infrastructure for Blockchain-Based Voting System 221Mukta Goyal and Adarsh Kumar7.1 Introduction 2227.1.1 E-Voting Challenge 2247.2 Related Works 2247.3 System Design 2277.4 Experimentation 2307.4.1 Software Requirements 2307.4.2 Function Requirements 2307.4.2.1 Election Organizer 2317.4.2.2 Candidate Registration 2317.4.2.3 Voter Registration Process 2327.4.3 Common Functional Requirement for All Users 2337.4.3.1 Result Display 2337.4.4 Non-Function Requirements 2337.4.4.1 Performance Requirement 2337.4.4.2 Security Requirement 2337.4.4.3 Usability Requirement 2337.4.4.4 Availability Requirement 2347.4.5 Implementation Details 2347.5 Findings & Results 2377.5.1 Smart Contract Deployment 2417.6 Conclusion and Future Scope 242Acknowledgement 246References 2468 Impact of IoT-Enabled Smart Cities: A Systematic Review and Challenges 253K. Rajkumar and U. Hariharan8.1 Introduction 2548.2 Recent Development in IoT Application for Modern City 2568.2.1 IoT Potential Smart City Approach 2578.2.2 Problems and Related Solutions in Modern Smart Cities Application 2598.3 Classification of IoT-Based Smart Cities 2628.3.1 Program Developers 2638.3.2 Network Type 2638.3.3 Activities of Standardization Bodies of Smart City 2638.3.4 Available Services 2698.3.5 Specification 2698.4 Impact of 5G Technology in IT, Big Data Analytics, and Cloud Computing 2708.4.1 IoT Five-Layer Architecture for Smart City Applications 2708.4.1.1 Sensing Layer (Get Information from Sensor) 2728.4.1.2 Network Layer (Access and Also Transmit Information) 2728.4.1.3 Data Storage and Analyzing 2738.4.1.4 Smart Cities Model (Smart Industry Model, Smart Healthcare Model, Smart Cities, Smart Agriculture Model) 2738.4.1.5 Application Layer (Dedicated Apps and Services) 2738.4.2 IoT Computing Paradigm for Smart City Application 2748.5 Research Advancement and Drawback on Smart Cities 2808.5.1 Integration of Cloud Computing in Smart Cities 2808.5.2 Integration of Applications 2818.5.3 System Security 2818.6 Summary of Smart Cities and Future Research Challenges and Their Guidelines 2828.7 Conclusion and Future Direction 287References 2889 Indoor Air Quality (IAQ) in Green Buildings, a Pre-Requisite to Human Health and Well-Being 293Ankita Banerjee, N.P. Melkania and Ayushi Nain9.1 Introduction 2949.2 Pollutants Responsible for Poor IAQ 2969.2.1 Volatile Organic Compounds (VOCs) 2969.2.2 Particulate Matter (PM) 2989.2.3 Asbestos 2999.2.4 Carbon Monoxide (CO) 2999.2.5 Environmental Tobacco Smoke (ETS) 3009.2.6 Biological Pollutants 3019.2.7 Lead (Pb) 3039.2.8 Nitrogen Dioxide (NO2) 3049.2.9 Ozone (O3) 3059.3 Health Impacts of Poor IAQ 3069.3.1 Sick Building Syndrome (SBS) 3069.3.2 Acute Impacts 3079.3.3 Chronic Impacts 3089.4 Strategies to Maintain a Healthy Indoor Environment in Green Buildings 3089.5 Conclusion and Future Scope 313References 31410 An Era of Internet of Things Leads to Smart Cities Initiatives Towards Urbanization 319Pooja Choudhary, Lava Bhargava, Ashok Kumar Suhag, Manju Choudhary and Satendra Singh10.1 Introduction: Emergence of a Smart City Concept 32010.2 Components of Smart City 32110.2.1 Smart Infrastructure 32310.2.2 Smart Building 32310.2.3 Smart Transportation 32510.2.4 Smart Energy 32610.2.5 Smart Health Care 32710.2.6 Smart Technology 32810.2.7 Smart Citizen 32910.2.8 Smart Governance 33010.2.9 Smart Education 33010.3 Role of IoT in Smart Cities 33110.3.1 Intent of IoT Adoption in Smart Cities 33310.3.2 IoT-Supported Communication Technologies 33310.4 Sectors, Services Related and Principal Issues for IoT Technologies 33610.5 Impact of Smart Cities 33610.5.1 Smart City Impact on Science and Technology 33610.5.2 Smart City Impact on Competitiveness 33910.5.3 Smart City Impact on Society 33910.5.4 Smart City Impact on Optimization and Management 33910.5.5 Smart City for Sustainable Development 34010.6 Key Applications of IoT in Smart Cities 34010.7 Challenges 34310.7.1 Smart City Design Challenges 34310.7.2 Challenges Raised by Smart Cities 34410.7.3 Challenges of IoT Technologies in Smart Cities 34410.8 Conclusion 346Acknowledgements 346References 34611 Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure 351Rajalakshmi Krishnamurthi, Dhanalekshmi Gopinathan and Adarsh Kumar11.1 Introduction 35211.2 Smart City and IoT 35411.3 Mobile Computing for Smart City 35711.4 Smart City and its Applications 36011.4.1 Traffic Monitoring 36011.4.2 Smart Lighting 36111.4.3 Air Quality Monitoring 36211.5 Smart Tourism in Smart City 36311.6 Mobile Computing-Based Smart Tourism 36611.7 Case Study: A Mobile Application for Trip Planner Task Scheduling in Smart City’s Sustainable Infrastructure 36811.7.1 System Interfaces and User Interfaces 37111.8 Experimentation and Results Discussion 37111.9 Conclusion and Future Scope 373References 37412 Smart Health Monitoring for Elderly Care in Indoor Environments 379Sonia and Tushar Semwal12.1 Introduction 38012.2 Sensors 38212.2.1 Human Traits 38312.2.2 Sensors Description 38412.2.2.1 Passive Sensors 38512.2.2.2 Active Sensors 38612.2.3 Sensing Challenges 38712.3 Internet of Things and Connected Systems 38712.4 Applications 38912.5 Case Study 39212.5.1 Case 1 39212.5.2 Case 2 39312.5.3 Challenges Involved 39312.5.4 Possible Solution 39312.6 Conclusion 39512.7 Discussion 395References 39513 A Comprehensive Study of IoT Security Risks in Building a Secure Smart City 401Akansha Bhargava, Gauri Salunkhe, Sushant Bhargava and Prerna Goswami13.1 Introduction 40213.1.1 Organization of the Chapter 40413.2 Related Works 40513.3 Overview of IoT System in Smart Cities 40713.3.1 Physical Devices 40913.3.2 Connectivity 40913.3.3 Middleware 41013.3.4 Human Interaction 41013.4 IoT Security Prerequisite 41113.5 IoT Security Areas 41313.5.1 Anomaly Detection 41313.5.2 Host-Based IDS (HIDS) 41413.5.3 Network-Based IDS (NIDS) 41413.5.4 Malware Detection 41413.5.5 Ransomware Detection 41513.5.6 Intruder Detection 41513.5.7 Botnet Detection 41513.6 IoT Security Threats 41613.6.1 Passive Threats 41613.6.2 Active Threats 41713.7 Review of ML/DL Application in IoT Security 41813.7.1 Machine Learning Methods 42113.7.1.1 Decision Trees (DTs) 42113.7.1.2 K-Nearest Neighbor (KNN) 42313.7.1.3 Random Forest 42413.7.1.4 Principal Component Analysis (PCA) 42513.7.1.5 Naïve Bayes 42513.7.1.6 Support Vector Machines (SVM) 42513.7.2 Deep Learning Methods 42613.7.2.1 Convolutional Neural Networks (CNNs) 42713.7.2.2 Auto Encoder (AE) 42913.7.2.3 Recurrent Neural Networks (RNNs) 42913.7.2.4 Restricted Boltzmann Machines (RBMs) 43213.7.2.5 Deep Belief Networks (DBNs) 43313.7.2.6 Generative Adversarial Networks (GANs) 43313.8 Challenges 43413.8.1 IoT Dataset Unavailability 43413.8.2 Computational Complications 43413.8.3 Forensics Challenges 43513.9 Future Prospects 43613.9.1 Implementation of ML/DL With Edge Computing 43713.9.2 Integration of ML/DL With Blockchain 43813.9.3 Integration of ML/DL With Fog Computing 43913.10 Conclusion 439References 44014 Role of Smart Buildings in Smart City—Components, Technology, Indicators, Challenges, Future Research Opportunities 449Tarana Singh, Arun Solanki and Sanjay Kumar Sharma14.1 Introduction 44914.1.1 Chapter Organization 45314.2 Literature Review 45314.3 Components of Smart Cities 45514.3.1 Smart Infrastructure 45514.3.2 Smart Parking Management 45614.3.3 Connected Charging Stations 45714.3.4 Smart Buildings and Properties 45714.3.5 Smart Garden and Sprinkler Systems 45714.3.6 Smart Heating and Ventilation 45714.3.7 Smart Industrial Environment 45814.3.8 Smart City Services 45814.3.9 Smart Energy Management 45814.3.10 Smart Water Management 45914.3.11 Smart Waste Management 45914.4 Characteristics of Smart Buildings 45914.4.1 Minimal Human Control 45914.4.2 Optimization 46014.4.3 Qualities 46014.4.4 Connected Systems 46014.4.5 Use of Sensors 46014.4.6 Automation 46114.4.7 Data 46114.5 Supporting Technology 46114.5.1 Big Data and IoT in Smart Cities 46114.5.2 Sensors 46214.5.3 5G Connectivity 46214.5.4 Geospatial Technology 46214.5.5 Robotics 46314.6 Key Performance Indicators of Smart City 46314.6.1 Smart Economy 46314.6.2 Smart Governance 46414.6.3 Smart Mobility 46414.6.4 Smart Environment 46414.6.5 Smart People 46414.6.6 Smart Living 46514.7 Challenges While Working for Smart City 46514.7.1 Retrofitting Existing Legacy City Infrastructure to Make it Smart 46514.7.2 Financing Smart Cities 46614.7.3 Availability of Master Plan or City Development Plan 46614.7.4 Financial Sustainability of ULBs 46614.7.5 Technical Constraints ULBs 46614.7.6 Three-Tier Governance 46714.7.7 Providing Clearances in a Timely Manner 46714.7.8 Dealing With a Multivendor Environment 46714.7.9 Capacity Building Program 46714.7.10 Reliability of Utility Services 46814.8 Future Research Opportunities in Smart City 46814.8.1 IoT Management 46814.8.2 Data Management 46914.8.3 Smart City Assessment Framework 46914.8.4 VANET Security 46914.8.5 Improving Photovoltaic Cells 46914.8.6 Smart City Enablers 47014.8.7 Information System Risks 47014.9 Conclusion 470References 47115 Effects of Green Buildings on the Environment 477Ayushi Nain, Ankita Banerjee and N.P. Melkania15.1 Introduction 47815.2 Sustainability and the Building Industry 48015.2.1 Environmental Benefits 48115.2.2 Social Benefits 48315.2.3 Economic Benefits 48315.3 Goals of Green Buildings 48415.3.1 Green Design 48515.3.2 Energy Efficiency 48515.3.3 Water Efficiency 48715.3.4 Material Efficiency 48915.3.5 Improved Internal Environment and Air Quality 49015.3.6 Minimization of Wastes 49215.3.7 Operations and Maintenance Optimization 49215.4 Impacts of Classical Buildings that Green Buildings Seek to Rectify 49315.4.1 Energy Use in Buildings 49415.4.2 Green House Gas (GHG) Emissions 49415.4.3 Indoor Air Quality 49415.4.4 Building Water Use 49615.4.5 Use of Land and Consumption 49615.4.6 Construction Materials 49715.4.7 Construction and Demolition (C&D) Wastes 49815.5 Green Buildings in India 49815.6 Conclusion 503Acknowledgement 504Acronyms 504References 505Index 509

Arun Solanki PhD is an assistant professor in the Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, India, where he has been working since 2009. His research interests span expert systems, machine learning, and search engines. He has published many research articles in international journals/conferences.Adarsh Kumar PhD is an associate professor at the University of Petroleum & Energy Studies, Dehradun, India. His main research interests are cybersecurity, cryptography, network security, and ad-hoc networks. He has published 60+ research papers in reputed journals, conferences and workshops.Anand Nayyar PhD is currently working in the Graduate School, Duy Tan University, Da Nang, Vietnam. He is a certified professional with more than 75 Professional Certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam, and many more. He published more than 300 research articles in various national and international journals and conferences. He has authored, coauthored or edited about 30 books and has been granted two patents in the areas of Internet of Things and speech processing.

• Preface xix1 The Use of Machine Learning for Sustainable and Resilient Buildings 1Kuldeep Singh Kaswan and Jagjit Singh Dhatterwal1.1 Introduction of ML Sustainable Resilient Building 21.2 Related Works 21.3 Machine Learning 51.4 What is Resilience? 61.4.1 Sustainability and Resiliency Conditions 71.4.2 Paradigm and Challenges of Sustainability and Resilience 71.4.3 Perspectives of Local Community 91.5 Sustainability and Resilience of Engineered System 121.5.1 Resilience and Sustainable Development Framework for Decision-Making 131.5.2 Exposures and Disturbance Events 151.5.3 Quantification of Resilience 151.5.4 Quantification of Sustainability 161.6 Community and Quantification Metrics, Resilience and Sustainability Objectives 171.6.1 Definition of Quantification Metric 181.6.2 Considering and Community 191.7 Structure Engineering Dilemmas and Resilient Epcot 211.7.1 Dilation of Resilience Essence 211.7.2 Quality of Life 221.8 Development of Risk Informed Criteria for Building Design Hurricane Resilient on Building 271.9 Resilient Infrastructures Against Earthquake and Tsunami Multi-Hazard 281.10 Machine Learning With Smart Building 291.10.1 Smart Building Appliances 291.10.2 Intelligent Tools, Cameras and Electronic Controls in a Connected House (SRB) 291.10.3 Level if Clouds are the IoT Institute Level With SBs 311.10.4 Component of Smart Buildings (SB) 331.10.5 Machine Learning Tasks in Smart Building Environment 461.10.6 ML Tools and Services for Smart Building 471.10.7 Big Data Research Applications for SBs in Real-Time 511.10.8 Implementation of the ML Concept in the SB Context 511.11 Conclusion and Future Research 53References 582 Fire Hazard Detection and Prediction by Machine Learning Techniques in Smart Buildings (SBs) Using Sensors and Unmanned Aerial Vehicles (UAVs) 63Sandhya Tarar and Namisha Bhasin2.1 Introduction 642.1.1 Bluetooth 652.1.2 Unmanned Aerial Vehicle 652.1.3 Sensors 652.1.4 Problem Description 672.2 Literature Review 682.3 Experimental Methods 712.3.1 Univariate Time-Series 732.3.1.1 Naïve Bayes 742.3.1.2 Simple Average 742.3.1.3 Moving Average 752.3.1.4 Simple Exponential Smoothing (SES) 762.3.1.5 Holt’s Linear Trend 762.3.1.6 Holt–Winters Method 762.3.1.7 Autoregressive Integrated Moving Average Model (ARIMA) 772.3.2 Multivariate Time-Series Prediction 802.3.2.1 Vector Autoregressive (VAR) 802.3.3 Hidden Markov Model (HMM) 812.3.4 Fuzzy Logic 852.4 Results 892.5 Conclusion and Future Work 89References 903 Sustainable Infrastructure Theories and Models 97Saurabh Jain, Keshav Kaushik, Deepak Kumar Sharma, Rajalakshmi Krishnamurthi and Adarsh Kumar3.1 Introduction to Data Fusion Approaches in Sustainable Infrastructure 983.1.1 The Need for Sustainable Infrastructure 983.1.2 Data Fusion 993.1.3 Different Types of Data Fusion Architecture 1003.1.3.1 Centralized Architecture 1003.1.3.2 Decentralized Architecture 1013.1.3.3 Distributed Architecture 1013.1.3.4 Hierarchical Architecture 1023.1.4 Smart Cities Application With Sustainable Infrastructures Based on Different Data Fusion Techniques 1023.2 Smart City Infrastructure Approaches 1043.2.1 Smart City Infrastructure 1043.2.2 Smart City IoT Deployments 1053.2.3 Smart City Control and Monitoring Centers 1063.2.4 Theory of Unified City Modeling for Smart Infrastructure 1083.2.5 Smart City Operational Modeling 1093.3 Theories and Models 1103.3.1 Sustainable Infrastructure Theories 1103.3.2 Sustainable Infrastructure Models 1123.4 Case Studies 1133.4.1 Case Studies-1: Web Browsing History Analysis 1133.4.1.1 Objective 1153.4.2 Case Study-2: Data Model for Group Construction in Student’s Industrial Placement 1173.5 Conclusion and Future Scope 121References 1224 Blockchain for Sustainable Smart Cities 127Iftikhar Ahmad, Syeda Warda Ashar, Umamma Khalid, Anmol Irfan and Wajeeha Khalil4.1 Introduction 1284.2 Smart City 1304.2.1 Overview of Smart City 1304.2.2 Evolution 1304.2.3 Smart City’s Sub Systems 1304.2.4 Domains of Smart City 1324.2.5 Challenges 1344.3 Blockchain 1364.3.1 Motivation 1374.3.2 The Birth of Blockchain 1374.3.3 System of Blockchain 1374.4 Use Cases of Smart City Implementing Blockchain 1384.4.1 Blockchain-Based Smart Economy 1384.4.1.1 Facilitating Faster and Cheaper International Payment 1394.4.1.2 Distributed Innovations in Financial Transactions 1394.4.1.3 Enhancing the Transparency of Supply/Global Commodity Chains 1404.4.1.4 Equity Crowd Funding 1414.4.2 Blockchain for Smart People 1414.4.2.1 Elections through Blockchain Technology 1414.4.2.2 Smart Contract 1434.4.2.3 Protecting Personal Data 1444.4.2.4 E-Health: Storing Health Records on Blockchain 1454.4.2.5 Intellectual Property Rights 1454.4.2.6 Digital Payments 1464.4.2.7 Other Use Cases 1464.4.3 Blockchain-Based Smart Governance 1474.4.3.1 Transparent Record Keeping and Tracking of Records 1474.4.3.2 Fraud Free Voting 1484.4.3.3 Decision Making 1504.4.4 Blockchain-Based Smart Transport 1504.4.4.1 Digitizing Driving License 1504.4.4.2 Smart Ride Sharing 1504.4.5 Blockchain-Based Smart Environment 1514.4.5.1 Social Plastic 1514.4.5.2 Energy 1524.4.5.3 Environmental Treaties 1524.4.5.4 Carbon Tax 1534.4.6 Blockchain-Based Smart Living 1534.4.6.1 Fighting Against Frauds and Discriminatory Policies and Practices 1544.4.6.2 Managing Change in Ownership 1544.4.6.3 Sustainable Buildings 1544.4.6.4 Other Use Cases 1554.5 Conclusion 156References 1565 Contextualizing Electronic Governance, Smart City Governance and Sustainable Infrastructure in India: A Study and Framework 163Nitin K. Tyagi and Mukta Goyal5.1 Introduction 1645.2 Related Works 1665.2.1 Research Questions 1665.3 Related E-Governance Frameworks 1785.3.1 Smart City Features in India 1815.4 Proposed Smart Governance Framework 1815.5 Results Discussion 1855.5.1 Initial Stage 1855.5.2 Design, Development and Delivery Stage 1865.6 Conclusion 186References 1886 Revolutionizing Geriatric Design in Developing Countries: IoT-Enabled Smart Home Design for the Elderly 193Shubhi Sonal and Anupadma R.6.1 Introduction to Geriatric Design 1946.1.1 Aim, Objectives, and Methodology 1966.1.2 Organization of Chapter 1976.2 Background 1976.2.1 Development of Smart Homes 1976.2.2 Development of Smart Homes for Elderly 1986.2.3 Indian Scenario 2006.3 Need for Smart Homes: An Assessment of Requirements for the Elderly-Activity Mapping 2016.3.1 Geriatric Smart Home Design: The Indian Context 2026.3.2 Elderly Activity Mapping 2026.3.3 Framework for Smart Homes for Elderly People 2066.3.4 Architectural Interventions: Spatial Requirements for Daily Activities 2076.3.5 Architectural Interventions to Address Issues Faced by Elderly People 2086.4 Schematic Design for a Nesting Home: IoT-Enabled Smart Home for Elderly People 2086.4.1 IoT-Based Real Time Automation for Nesting Homes 2086.4.2 Technological Components of Elderly Smart Homes 2126.4.2.1 Sensors for Smart Home 2126.4.2.2 Health Monitoring System 2136.4.2.3 Network Devices 2136.4.2.4 Alerts 2146.5 Worldwide Elderly Smart Homes 2146.5.1 Challenges in Smart Elderly Homes 2156.6 Conclusion and Future Scope 216References 2167 Sustainable E-Infrastructure for Blockchain-Based Voting System 221Mukta Goyal and Adarsh Kumar7.1 Introduction 2227.1.1 E-Voting Challenge 2247.2 Related Works 2247.3 System Design 2277.4 Experimentation 2307.4.1 Software Requirements 2307.4.2 Function Requirements 2307.4.2.1 Election Organizer 2317.4.2.2 Candidate Registration 2317.4.2.3 Voter Registration Process 2327.4.3 Common Functional Requirement for All Users 2337.4.3.1 Result Display 2337.4.4 Non-Function Requirements 2337.4.4.1 Performance Requirement 2337.4.4.2 Security Requirement 2337.4.4.3 Usability Requirement 2337.4.4.4 Availability Requirement 2347.4.5 Implementation Details 2347.5 Findings & Results 2377.5.1 Smart Contract Deployment 2417.6 Conclusion and Future Scope 242Acknowledgement 246References 2468 Impact of IoT-Enabled Smart Cities: A Systematic Review and Challenges 253K. Rajkumar and U. Hariharan8.1 Introduction 2548.2 Recent Development in IoT Application for Modern City 2568.2.1 IoT Potential Smart City Approach 2578.2.2 Problems and Related Solutions in Modern Smart Cities Application 2598.3 Classification of IoT-Based Smart Cities 2628.3.1 Program Developers 2638.3.2 Network Type 2638.3.3 Activities of Standardization Bodies of Smart City 2638.3.4 Available Services 2698.3.5 Specification 2698.4 Impact of 5G Technology in IT, Big Data Analytics, and Cloud Computing 2708.4.1 IoT Five-Layer Architecture for Smart City Applications 2708.4.1.1 Sensing Layer (Get Information from Sensor) 2728.4.1.2 Network Layer (Access and Also Transmit Information) 2728.4.1.3 Data Storage and Analyzing 2738.4.1.4 Smart Cities Model (Smart Industry Model, Smart Healthcare Model, Smart Cities, Smart Agriculture Model) 2738.4.1.5 Application Layer (Dedicated Apps and Services) 2738.4.2 IoT Computing Paradigm for Smart City Application 2748.5 Research Advancement and Drawback on Smart Cities 2808.5.1 Integration of Cloud Computing in Smart Cities 2808.5.2 Integration of Applications 2818.5.3 System Security 2818.6 Summary of Smart Cities and Future Research Challenges and Their Guidelines 2828.7 Conclusion and Future Direction 287References 2889 Indoor Air Quality (IAQ) in Green Buildings, a Pre-Requisite to Human Health and Well-Being 293Ankita Banerjee, N.P. Melkania and Ayushi Nain9.1 Introduction 2949.2 Pollutants Responsible for Poor IAQ 2969.2.1 Volatile Organic Compounds (VOCs) 2969.2.2 Particulate Matter (PM) 2989.2.3 Asbestos 2999.2.4 Carbon Monoxide (CO) 2999.2.5 Environmental Tobacco Smoke (ETS) 3009.2.6 Biological Pollutants 3019.2.7 Lead (Pb) 3039.2.8 Nitrogen Dioxide (NO2) 3049.2.9 Ozone (O3) 3059.3 Health Impacts of Poor IAQ 3069.3.1 Sick Building Syndrome (SBS) 3069.3.2 Acute Impacts 3079.3.3 Chronic Impacts 3089.4 Strategies to Maintain a Healthy Indoor Environment in Green Buildings 3089.5 Conclusion and Future Scope 313References 31410 An Era of Internet of Things Leads to Smart Cities Initiatives Towards Urbanization 319Pooja Choudhary, Lava Bhargava, Ashok Kumar Suhag, Manju Choudhary and Satendra Singh10.1 Introduction: Emergence of a Smart City Concept 32010.2 Components of Smart City 32110.2.1 Smart Infrastructure 32310.2.2 Smart Building 32310.2.3 Smart Transportation 32510.2.4 Smart Energy 32610.2.5 Smart Health Care 32710.2.6 Smart Technology 32810.2.7 Smart Citizen 32910.2.8 Smart Governance 33010.2.9 Smart Education 33010.3 Role of IoT in Smart Cities 33110.3.1 Intent of IoT Adoption in Smart Cities 33310.3.2 IoT-Supported Communication Technologies 33310.4 Sectors, Services Related and Principal Issues for IoT Technologies 33610.5 Impact of Smart Cities 33610.5.1 Smart City Impact on Science and Technology 33610.5.2 Smart City Impact on Competitiveness 33910.5.3 Smart City Impact on Society 33910.5.4 Smart City Impact on Optimization and Management 33910.5.5 Smart City for Sustainable Development 34010.6 Key Applications of IoT in Smart Cities 34010.7 Challenges 34310.7.1 Smart City Design Challenges 34310.7.2 Challenges Raised by Smart Cities 34410.7.3 Challenges of IoT Technologies in Smart Cities 34410.8 Conclusion 346Acknowledgements 346References 34611 Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure 351Rajalakshmi Krishnamurthi, Dhanalekshmi Gopinathan and Adarsh Kumar11.1 Introduction 35211.2 Smart City and IoT 35411.3 Mobile Computing for Smart City 35711.4 Smart City and its Applications 36011.4.1 Traffic Monitoring 36011.4.2 Smart Lighting 36111.4.3 Air Quality Monitoring 36211.5 Smart Tourism in Smart City 36311.6 Mobile Computing-Based Smart Tourism 36611.7 Case Study: A Mobile Application for Trip Planner Task Scheduling in Smart City’s Sustainable Infrastructure 36811.7.1 System Interfaces and User Interfaces 37111.8 Experimentation and Results Discussion 37111.9 Conclusion and Future Scope 373References 37412 Smart Health Monitoring for Elderly Care in Indoor Environments 379Sonia and Tushar Semwal12.1 Introduction 38012.2 Sensors 38212.2.1 Human Traits 38312.2.2 Sensors Description 38412.2.2.1 Passive Sensors 38512.2.2.2 Active Sensors 38612.2.3 Sensing Challenges 38712.3 Internet of Things and Connected Systems 38712.4 Applications 38912.5 Case Study 39212.5.1 Case 1 39212.5.2 Case 2 39312.5.3 Challenges Involved 39312.5.4 Possible Solution 39312.6 Conclusion 39512.7 Discussion 395References 39513 A Comprehensive Study of IoT Security Risks in Building a Secure Smart City 401Akansha Bhargava, Gauri Salunkhe, Sushant Bhargava and Prerna Goswami13.1 Introduction 40213.1.1 Organization of the Chapter 40413.2 Related Works 40513.3 Overview of IoT System in Smart Cities 40713.3.1 Physical Devices 40913.3.2 Connectivity 40913.3.3 Middleware 41013.3.4 Human Interaction 41013.4 IoT Security Prerequisite 41113.5 IoT Security Areas 41313.5.1 Anomaly Detection 41313.5.2 Host-Based IDS (HIDS) 41413.5.3 Network-Based IDS (NIDS) 41413.5.4 Malware Detection 41413.5.5 Ransomware Detection 41513.5.6 Intruder Detection 41513.5.7 Botnet Detection 41513.6 IoT Security Threats 41613.6.1 Passive Threats 41613.6.2 Active Threats 41713.7 Review of ML/DL Application in IoT Security 41813.7.1 Machine Learning Methods 42113.7.1.1 Decision Trees (DTs) 42113.7.1.2 K-Nearest Neighbor (KNN) 42313.7.1.3 Random Forest 42413.7.1.4 Principal Component Analysis (PCA) 42513.7.1.5 Naïve Bayes 42513.7.1.6 Support Vector Machines (SVM) 42513.7.2 Deep Learning Methods 42613.7.2.1 Convolutional Neural Networks (CNNs) 42713.7.2.2 Auto Encoder (AE) 42913.7.2.3 Recurrent Neural Networks (RNNs) 42913.7.2.4 Restricted Boltzmann Machines (RBMs) 43213.7.2.5 Deep Belief Networks (DBNs) 43313.7.2.6 Generative Adversarial Networks (GANs) 43313.8 Challenges 43413.8.1 IoT Dataset Unavailability 43413.8.2 Computational Complications 43413.8.3 Forensics Challenges 43513.9 Future Prospects 43613.9.1 Implementation of ML/DL With Edge Computing 43713.9.2 Integration of ML/DL With Blockchain 43813.9.3 Integration of ML/DL With Fog Computing 43913.10 Conclusion 439References 44014 Role of Smart Buildings in Smart City—Components, Technology, Indicators, Challenges, Future Research Opportunities 449Tarana Singh, Arun Solanki and Sanjay Kumar Sharma14.1 Introduction 44914.1.1 Chapter Organization 45314.2 Literature Review 45314.3 Components of Smart Cities 45514.3.1 Smart Infrastructure 45514.3.2 Smart Parking Management 45614.3.3 Connected Charging Stations 45714.3.4 Smart Buildings and Properties 45714.3.5 Smart Garden and Sprinkler Systems 45714.3.6 Smart Heating and Ventilation 45714.3.7 Smart Industrial Environment 45814.3.8 Smart City Services 45814.3.9 Smart Energy Management 45814.3.10 Smart Water Management 45914.3.11 Smart Waste Management 45914.4 Characteristics of Smart Buildings 45914.4.1 Minimal Human Control 45914.4.2 Optimization 46014.4.3 Qualities 46014.4.4 Connected Systems 46014.4.5 Use of Sensors 46014.4.6 Automation 46114.4.7 Data 46114.5 Supporting Technology 46114.5.1 Big Data and IoT in Smart Cities 46114.5.2 Sensors 46214.5.3 5G Connectivity 46214.5.4 Geospatial Technology 46214.5.5 Robotics 46314.6 Key Performance Indicators of Smart City 46314.6.1 Smart Economy 46314.6.2 Smart Governance 46414.6.3 Smart Mobility 46414.6.4 Smart Environment 46414.6.5 Smart People 46414.6.6 Smart Living 46514.7 Challenges While Working for Smart City 46514.7.1 Retrofitting Existing Legacy City Infrastructure to Make it Smart 46514.7.2 Financing Smart Cities 46614.7.3 Availability of Master Plan or City Development Plan 46614.7.4 Financial Sustainability of ULBs 46614.7.5 Technical Constraints ULBs 46614.7.6 Three-Tier Governance 46714.7.7 Providing Clearances in a Timely Manner 46714.7.8 Dealing With a Multivendor Environment 46714.7.9 Capacity Building Program 46714.7.10 Reliability of Utility Services 46814.8 Future Research Opportunities in Smart City 46814.8.1 IoT Management 46814.8.2 Data Management 46914.8.3 Smart City Assessment Framework 46914.8.4 VANET Security 46914.8.5 Improving Photovoltaic Cells 46914.8.6 Smart City Enablers 47014.8.7 Information System Risks 47014.9 Conclusion 470References 47115 Effects of Green Buildings on the Environment 477Ayushi Nain, Ankita Banerjee and N.P. Melkania15.1 Introduction 47815.2 Sustainability and the Building Industry 48015.2.1 Environmental Benefits 48115.2.2 Social Benefits 48315.2.3 Economic Benefits 48315.3 Goals of Green Buildings 48415.3.1 Green Design 48515.3.2 Energy Efficiency 48515.3.3 Water Efficiency 48715.3.4 Material Efficiency 48915.3.5 Improved Internal Environment and Air Quality 49015.3.6 Minimization of Wastes 49215.3.7 Operations and Maintenance Optimization 49215.4 Impacts of Classical Buildings that Green Buildings Seek to Rectify 49315.4.1 Energy Use in Buildings 49415.4.2 Green House Gas (GHG) Emissions 49415.4.3 Indoor Air Quality 49415.4.4 Building Water Use 49615.4.5 Use of Land and Consumption 49615.4.6 Construction Materials 49715.4.7 Construction and Demolition (C&D) Wastes 49815.5 Green Buildings in India 49815.6 Conclusion 503Acknowledgement 504Acronyms 504References 505Index 509

Arun Solanki PhD is an assistant professor in the Department of Computer Science and Engineering, Gautam Buddha University, Greater Noida, India, where he has been working since 2009. His research interests span expert systems, machine learning, and search engines. He has published many research articles in international journals/conferences.Adarsh Kumar PhD is an associate professor at the University of Petroleum & Energy Studies, Dehradun, India. His main research interests are cybersecurity, cryptography, network security, and ad-hoc networks. He has published 60+ research papers in reputed journals, conferences and workshops.Anand Nayyar PhD is currently working in the Graduate School, Duy Tan University, Da Nang, Vietnam. He is a certified professional with more than 75 Professional Certificates from CISCO, Microsoft, Oracle, Google, Beingcert, EXIN, GAQM, Cyberoam, and many more. He published more than 300 research articles in various national and international journals and conferences. He has authored, coauthored or edited about 30 books and has been granted two patents in the areas of Internet of Things and speech processing.

• Preface xix1 The Use of Machine Learning for Sustainable and Resilient Buildings 1Kuldeep Singh Kaswan and Jagjit Singh Dhatterwal1.1 Introduction of ML Sustainable Resilient Building 21.2 Related Works 21.3 Machine Learning 51.4 What is Resilience? 61.4.1 Sustainability and Resiliency Conditions 71.4.2 Paradigm and Challenges of Sustainability and Resilience 71.4.3 Perspectives of Local Community 91.5 Sustainability and Resilience of Engineered System 121.5.1 Resilience and Sustainable Development Framework for Decision-Making 131.5.2 Exposures and Disturbance Events 151.5.3 Quantification of Resilience 151.5.4 Quantification of Sustainability 161.6 Community and Quantification Metrics, Resilience and Sustainability Objectives 171.6.1 Definition of Quantification Metric 181.6.2 Considering and Community 191.7 Structure Engineering Dilemmas and Resilient Epcot 211.7.1 Dilation of Resilience Essence 211.7.2 Quality of Life 221.8 Development of Risk Informed Criteria for Building Design Hurricane Resilient on Building 271.9 Resilient Infrastructures Against Earthquake and Tsunami Multi-Hazard 281.10 Machine Learning With Smart Building 291.10.1 Smart Building Appliances 291.10.2 Intelligent Tools, Cameras and Electronic Controls in a Connected House (SRB) 291.10.3 Level if Clouds are the IoT Institute Level With SBs 311.10.4 Component of Smart Buildings (SB) 331.10.5 Machine Learning Tasks in Smart Building Environment 461.10.6 ML Tools and Services for Smart Building 471.10.7 Big Data Research Applications for SBs in Real-Time 511.10.8 Implementation of the ML Concept in the SB Context 511.11 Conclusion and Future Research 53References 582 Fire Hazard Detection and Prediction by Machine Learning Techniques in Smart Buildings (SBs) Using Sensors and Unmanned Aerial Vehicles (UAVs) 63Sandhya Tarar and Namisha Bhasin2.1 Introduction 642.1.1 Bluetooth 652.1.2 Unmanned Aerial Vehicle 652.1.3 Sensors 652.1.4 Problem Description 672.2 Literature Review 682.3 Experimental Methods 712.3.1 Univariate Time-Series 732.3.1.1 Naïve Bayes 742.3.1.2 Simple Average 742.3.1.3 Moving Average 752.3.1.4 Simple Exponential Smoothing (SES) 762.3.1.5 Holt’s Linear Trend 762.3.1.6 Holt–Winters Method 762.3.1.7 Autoregressive Integrated Moving Average Model (ARIMA) 772.3.2 Multivariate Time-Series Prediction 802.3.2.1 Vector Autoregressive (VAR) 802.3.3 Hidden Markov Model (HMM) 812.3.4 Fuzzy Logic 852.4 Results 892.5 Conclusion and Future Work 89References 903 Sustainable Infrastructure Theories and Models 97Saurabh Jain, Keshav Kaushik, Deepak Kumar Sharma, Rajalakshmi Krishnamurthi and Adarsh Kumar3.1 Introduction to Data Fusion Approaches in Sustainable Infrastructure 983.1.1 The Need for Sustainable Infrastructure 983.1.2 Data Fusion 993.1.3 Different Types of Data Fusion Architecture 1003.1.3.1 Centralized Architecture 1003.1.3.2 Decentralized Architecture 1013.1.3.3 Distributed Architecture 1013.1.3.4 Hierarchical Architecture 1023.1.4 Smart Cities Application With Sustainable Infrastructures Based on Different Data Fusion Techniques 1023.2 Smart City Infrastructure Approaches 1043.2.1 Smart City Infrastructure 1043.2.2 Smart City IoT Deployments 1053.2.3 Smart City Control and Monitoring Centers 1063.2.4 Theory of Unified City Modeling for Smart Infrastructure 1083.2.5 Smart City Operational Modeling 1093.3 Theories and Models 1103.3.1 Sustainable Infrastructure Theories 1103.3.2 Sustainable Infrastructure Models 1123.4 Case Studies 1133.4.1 Case Studies-1: Web Browsing History Analysis 1133.4.1.1 Objective 1153.4.2 Case Study-2: Data Model for Group Construction in Student’s Industrial Placement 1173.5 Conclusion and Future Scope 121References 1224 Blockchain for Sustainable Smart Cities 127Iftikhar Ahmad, Syeda Warda Ashar, Umamma Khalid, Anmol Irfan and Wajeeha Khalil4.1 Introduction 1284.2 Smart City 1304.2.1 Overview of Smart City 1304.2.2 Evolution 1304.2.3 Smart City’s Sub Systems 1304.2.4 Domains of Smart City 1324.2.5 Challenges 1344.3 Blockchain 1364.3.1 Motivation 1374.3.2 The Birth of Blockchain 1374.3.3 System of Blockchain 1374.4 Use Cases of Smart City Implementing Blockchain 1384.4.1 Blockchain-Based Smart Economy 1384.4.1.1 Facilitating Faster and Cheaper International Payment 1394.4.1.2 Distributed Innovations in Financial Transactions 1394.4.1.3 Enhancing the Transparency of Supply/Global Commodity Chains 1404.4.1.4 Equity Crowd Funding 1414.4.2 Blockchain for Smart People 1414.4.2.1 Elections through Blockchain Technology 1414.4.2.2 Smart Contract 1434.4.2.3 Protecting Personal Data 1444.4.2.4 E-Health: Storing Health Records on Blockchain 1454.4.2.5 Intellectual Property Rights 1454.4.2.6 Digital Payments 1464.4.2.7 Other Use Cases 1464.4.3 Blockchain-Based Smart Governance 1474.4.3.1 Transparent Record Keeping and Tracking of Records 1474.4.3.2 Fraud Free Voting 1484.4.3.3 Decision Making 1504.4.4 Blockchain-Based Smart Transport 1504.4.4.1 Digitizing Driving License 1504.4.4.2 Smart Ride Sharing 1504.4.5 Blockchain-Based Smart Environment 1514.4.5.1 Social Plastic 1514.4.5.2 Energy 1524.4.5.3 Environmental Treaties 1524.4.5.4 Carbon Tax 1534.4.6 Blockchain-Based Smart Living 1534.4.6.1 Fighting Against Frauds and Discriminatory Policies and Practices 1544.4.6.2 Managing Change in Ownership 1544.4.6.3 Sustainable Buildings 1544.4.6.4 Other Use Cases 1554.5 Conclusion 156References 1565 Contextualizing Electronic Governance, Smart City Governance and Sustainable Infrastructure in India: A Study and Framework 163Nitin K. Tyagi and Mukta Goyal5.1 Introduction 1645.2 Related Works 1665.2.1 Research Questions 1665.3 Related E-Governance Frameworks 1785.3.1 Smart City Features in India 1815.4 Proposed Smart Governance Framework 1815.5 Results Discussion 1855.5.1 Initial Stage 1855.5.2 Design, Development and Delivery Stage 1865.6 Conclusion 186References 1886 Revolutionizing Geriatric Design in Developing Countries: IoT-Enabled Smart Home Design for the Elderly 193Shubhi Sonal and Anupadma R.6.1 Introduction to Geriatric Design 1946.1.1 Aim, Objectives, and Methodology 1966.1.2 Organization of Chapter 1976.2 Background 1976.2.1 Development of Smart Homes 1976.2.2 Development of Smart Homes for Elderly 1986.2.3 Indian Scenario 2006.3 Need for Smart Homes: An Assessment of Requirements for the Elderly-Activity Mapping 2016.3.1 Geriatric Smart Home Design: The Indian Context 2026.3.2 Elderly Activity Mapping 2026.3.3 Framework for Smart Homes for Elderly People 2066.3.4 Architectural Interventions: Spatial Requirements for Daily Activities 2076.3.5 Architectural Interventions to Address Issues Faced by Elderly People 2086.4 Schematic Design for a Nesting Home: IoT-Enabled Smart Home for Elderly People 2086.4.1 IoT-Based Real Time Automation for Nesting Homes 2086.4.2 Technological Components of Elderly Smart Homes 2126.4.2.1 Sensors for Smart Home 2126.4.2.2 Health Monitoring System 2136.4.2.3 Network Devices 2136.4.2.4 Alerts 2146.5 Worldwide Elderly Smart Homes 2146.5.1 Challenges in Smart Elderly Homes 2156.6 Conclusion and Future Scope 216References 2167 Sustainable E-Infrastructure for Blockchain-Based Voting System 221Mukta Goyal and Adarsh Kumar7.1 Introduction 2227.1.1 E-Voting Challenge 2247.2 Related Works 2247.3 System Design 2277.4 Experimentation 2307.4.1 Software Requirements 2307.4.2 Function Requirements 2307.4.2.1 Election Organizer 2317.4.2.2 Candidate Registration 2317.4.2.3 Voter Registration Process 2327.4.3 Common Functional Requirement for All Users 2337.4.3.1 Result Display 2337.4.4 Non-Function Requirements 2337.4.4.1 Performance Requirement 2337.4.4.2 Security Requirement 2337.4.4.3 Usability Requirement 2337.4.4.4 Availability Requirement 2347.4.5 Implementation Details 2347.5 Findings & Results 2377.5.1 Smart Contract Deployment 2417.6 Conclusion and Future Scope 242Acknowledgement 246References 2468 Impact of IoT-Enabled Smart Cities: A Systematic Review and Challenges 253K. Rajkumar and U. Hariharan8.1 Introduction 2548.2 Recent Development in IoT Application for Modern City 2568.2.1 IoT Potential Smart City Approach 2578.2.2 Problems and Related Solutions in Modern Smart Cities Application 2598.3 Classification of IoT-Based Smart Cities 2628.3.1 Program Developers 2638.3.2 Network Type 2638.3.3 Activities of Standardization Bodies of Smart City 2638.3.4 Available Services 2698.3.5 Specification 2698.4 Impact of 5G Technology in IT, Big Data Analytics, and Cloud Computing 2708.4.1 IoT Five-Layer Architecture for Smart City Applications 2708.4.1.1 Sensing Layer (Get Information from Sensor) 2728.4.1.2 Network Layer (Access and Also Transmit Information) 2728.4.1.3 Data Storage and Analyzing 2738.4.1.4 Smart Cities Model (Smart Industry Model, Smart Healthcare Model, Smart Cities, Smart Agriculture Model) 2738.4.1.5 Application Layer (Dedicated Apps and Services) 2738.4.2 IoT Computing Paradigm for Smart City Application 2748.5 Research Advancement and Drawback on Smart Cities 2808.5.1 Integration of Cloud Computing in Smart Cities 2808.5.2 Integration of Applications 2818.5.3 System Security 2818.6 Summary of Smart Cities and Future Research Challenges and Their Guidelines 2828.7 Conclusion and Future Direction 287References 2889 Indoor Air Quality (IAQ) in Green Buildings, a Pre-Requisite to Human Health and Well-Being 293Ankita Banerjee, N.P. Melkania and Ayushi Nain9.1 Introduction 2949.2 Pollutants Responsible for Poor IAQ 2969.2.1 Volatile Organic Compounds (VOCs) 2969.2.2 Particulate Matter (PM) 2989.2.3 Asbestos 2999.2.4 Carbon Monoxide (CO) 2999.2.5 Environmental Tobacco Smoke (ETS) 3009.2.6 Biological Pollutants 3019.2.7 Lead (Pb) 3039.2.8 Nitrogen Dioxide (NO2) 3049.2.9 Ozone (O3) 3059.3 Health Impacts of Poor IAQ 3069.3.1 Sick Building Syndrome (SBS) 3069.3.2 Acute Impacts 3079.3.3 Chronic Impacts 3089.4 Strategies to Maintain a Healthy Indoor Environment in Green Buildings 3089.5 Conclusion and Future Scope 313References 31410 An Era of Internet of Things Leads to Smart Cities Initiatives Towards Urbanization 319Pooja Choudhary, Lava Bhargava, Ashok Kumar Suhag, Manju Choudhary and Satendra Singh10.1 Introduction: Emergence of a Smart City Concept 32010.2 Components of Smart City 32110.2.1 Smart Infrastructure 32310.2.2 Smart Building 32310.2.3 Smart Transportation 32510.2.4 Smart Energy 32610.2.5 Smart Health Care 32710.2.6 Smart Technology 32810.2.7 Smart Citizen 32910.2.8 Smart Governance 33010.2.9 Smart Education 33010.3 Role of IoT in Smart Cities 33110.3.1 Intent of IoT Adoption in Smart Cities 33310.3.2 IoT-Supported Communication Technologies 33310.4 Sectors, Services Related and Principal Issues for IoT Technologies 33610.5 Impact of Smart Cities 33610.5.1 Smart City Impact on Science and Technology 33610.5.2 Smart City Impact on Competitiveness 33910.5.3 Smart City Impact on Society 33910.5.4 Smart City Impact on Optimization and Management 33910.5.5 Smart City for Sustainable Development 34010.6 Key Applications of IoT in Smart Cities 34010.7 Challenges 34310.7.1 Smart City Design Challenges 34310.7.2 Challenges Raised by Smart Cities 34410.7.3 Challenges of IoT Technologies in Smart Cities 34410.8 Conclusion 346Acknowledgements 346References 34611 Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure 351Rajalakshmi Krishnamurthi, Dhanalekshmi Gopinathan and Adarsh Kumar11.1 Introduction 35211.2 Smart City and IoT 35411.3 Mobile Computing for Smart City 35711.4 Smart City and its Applications 36011.4.1 Traffic Monitoring 36011.4.2 Smart Lighting 36111.4.3 Air Quality Monitoring 36211.5 Smart Tourism in Smart City 36311.6 Mobile Computing-Based Smart Tourism 36611.7 Case Study: A Mobile Application for Trip Planner Task Scheduling in Smart City’s Sustainable Infrastructure 36811.7.1 System Interfaces and User Interfaces 37111.8 Experimentation and Results Discussion 37111.9 Conclusion and Future Scope 373References 37412 Smart Health Monitoring for Elderly Care in Indoor Environments 379Sonia and Tushar Semwal12.1 Introduction 38012.2 Sensors 38212.2.1 Human Traits 38312.2.2 Sensors Description 38412.2.2.1 Passive Sensors 38512.2.2.2 Active Sensors 38612.2.3 Sensing Challenges 38712.3 Internet of Things and Connected Systems 38712.4 Applications 38912.5 Case Study 39212.5.1 Case 1 39212.5.2 Case 2 39312.5.3 Challenges Involved 39312.5.4 Possible Solution 39312.6 Conclusion 39512.7 Discussion 395References 39513 A Comprehensive Study of IoT Security Risks in Building a Secure Smart City 401Akansha Bhargava, Gauri Salunkhe, Sushant Bhargava and Prerna Goswami13.1 Introduction 40213.1.1 Organization of the Chapter 40413.2 Related Works 40513.3 Overview of IoT System in Smart Cities 40713.3.1 Physical Devices 40913.3.2 Connectivity 40913.3.3 Middleware 41013.3.4 Human Interaction 41013.4 IoT Security Prerequisite 41113.5 IoT Security Areas 41313.5.1 Anomaly Detection 41313.5.2 Host-Based IDS (HIDS) 41413.5.3 Network-Based IDS (NIDS) 41413.5.4 Malware Detection 41413.5.5 Ransomware Detection 41513.5.6 Intruder Detection 41513.5.7 Botnet Detection 41513.6 IoT Security Threats 41613.6.1 Passive Threats 41613.6.2 Active Threats 41713.7 Review of ML/DL Application in IoT Security 41813.7.1 Machine Learning Methods 42113.7.1.1 Decision Trees (DTs) 42113.7.1.2 K-Nearest Neighbor (KNN) 42313.7.1.3 Random Forest 42413.7.1.4 Principal Component Analysis (PCA) 42513.7.1.5 Naïve Bayes 42513.7.1.6 Support Vector Machines (SVM) 42513.7.2 Deep Learning Methods 42613.7.2.1 Convolutional Neural Networks (CNNs) 42713.7.2.2 Auto Encoder (AE) 42913.7.2.3 Recurrent Neural Networks (RNNs) 42913.7.2.4 Restricted Boltzmann Machines (RBMs) 43213.7.2.5 Deep Belief Networks (DBNs) 43313.7.2.6 Generative Adversarial Networks (GANs) 43313.8 Challenges 43413.8.1 IoT Dataset Unavailability 43413.8.2 Computational Complications 43413.8.3 Forensics Challenges 43513.9 Future Prospects 43613.9.1 Implementation of ML/DL With Edge Computing 43713.9.2 Integration of ML/DL With Blockchain 43813.9.3 Integration of ML/DL With Fog Computing 43913.10 Conclusion 439References 44014 Role of Smart Buildings in Smart City—Components, Technology, Indicators, Challenges, Future Research Opportunities 449Tarana Singh, Arun Solanki and Sanjay Kumar Sharma14.1 Introduction 44914.1.1 Chapter Organization 45314.2 Literature Review 45314.3 Components of Smart Cities 45514.3.1 Smart Infrastructure 45514.3.2 Smart Parking Management 45614.3.3 Connected Charging Stations 45714.3.4 Smart Buildings and Properties 45714.3.5 Smart Garden and Sprinkler Systems 45714.3.6 Smart Heating and Ventilation 45714.3.7 Smart Industrial Environment 45814.3.8 Smart City Services 45814.3.9 Smart Energy Management 45814.3.10 Smart Water Management 45914.3.11 Smart Waste Management 45914.4 Characteristics of Smart Buildings 45914.4.1 Minimal Human Control 45914.4.2 Optimization 46014.4.3 Qualities 46014.4.4 Connected Systems 46014.4.5 Use of Sensors 46014.4.6 Automation 46114.4.7 Data 46114.5 Supporting Technology 46114.5.1 Big Data and IoT in Smart Cities 46114.5.2 Sensors 46214.5.3 5G Connectivity 46214.5.4 Geospatial Technology 46214.5.5 Robotics 46314.6 Key Performance Indicators of Smart City 46314.6.1 Smart Economy 46314.6.2 Smart Governance 46414.6.3 Smart Mobility 46414.6.4 Smart Environment 46414.6.5 Smart People 46414.6.6 Smart Living 46514.7 Challenges While Working for Smart City 46514.7.1 Retrofitting Existing Legacy City Infrastructure to Make it Smart 46514.7.2 Financing Smart Cities 46614.7.3 Availability of Master Plan or City Development Plan 46614.7.4 Financial Sustainability of ULBs 46614.7.5 Technical Constraints ULBs 46614.7.6 Three-Tier Governance 46714.7.7 Providing Clearances in a Timely Manner 46714.7.8 Dealing With a Multivendor Environment 46714.7.9 Capacity Building Program 46714.7.10 Reliability of Utility Services 46814.8 Future Research Opportunities in Smart City 46814.8.1 IoT Management 46814.8.2 Data Management 46914.8.3 Smart City Assessment Framework 46914.8.4 VANET Security 46914.8.5 Improving Photovoltaic Cells 46914.8.6 Smart City Enablers 47014.8.7 Information System Risks 47014.9 Conclusion 470References 47115 Effects of Green Buildings on the Environment 477Ayushi Nain, Ankita Banerjee and N.P. Melkania15.1 Introduction 47815.2 Sustainability and the Building Industry 48015.2.1 Environmental Benefits 48115.2.2 Social Benefits 48315.2.3 Economic Benefits 48315.3 Goals of Green Buildings 48415.3.1 Green Design 48515.3.2 Energy Efficiency 48515.3.3 Water Efficiency 48715.3.4 Material Efficiency 48915.3.5 Improved Internal Environment and Air Quality 49015.3.6 Minimization of Wastes 49215.3.7 Operations and Maintenance Optimization 49215.4 Impacts of Classical Buildings that Green Buildings Seek to Rectify 49315.4.1 Energy Use in Buildings 49415.4.2 Green House Gas (GHG) Emissions 49415.4.3 Indoor Air Quality 49415.4.4 Building Water Use 49615.4.5 Use of Land and Consumption 49615.4.6 Construction Materials 49715.4.7 Construction and Demolition (C&D) Wastes 49815.5 Green Buildings in India 49815.6 Conclusion 503Acknowledgement 504Acronyms 504References 505Index 509