Preface xix 1 The Use of Machine Learning for Sustainable and Resilient Buildings 1Kuldeep Singh Kaswan and Jagjit Singh Dhatterwal 1.1 Introduction of ML Sustainable Resilient Building 2 1.2 Related Works 2 1.3 Machine Learning 5 1.4 What is Resilience? 6 1.4.1 Sustainability and Resiliency Conditions 7 1.4.2 Paradigm and Challenges of Sustainability and Resilience 7 1.4.3 Perspectives of Local Community 9 1.5 Sustainability and Resilience of Engineered System 12 1.5.1 Resilience and Sustainable Development Framework for Decision-Making 13 1.5.2 Exposures and Disturbance Events 15 1.5.3 Quantification of Resilience 15 1.5.4 Quantification of Sustainability 16 1.6 Community and Quantification Metrics, Resilience and Sustainability Objectives 17 1.6.1 Definition of Quantification Metric 18 1.6.2 Considering and Community 19 1.7 Structure Engineering Dilemmas and Resilient Epcot 21 1.7.1 Dilation of Resilience Essence 21 1.7.2 Quality of Life 22 1.8 Development of Risk Informed Criteria for Building Design Hurricane Resilient on Building 27 1.9 Resilient Infrastructures Against Earthquake and Tsunami Multi-Hazard 28 1.10 Machine Learning With Smart Building 29 1.10.1 Smart Building Appliances 29 1.10.2 Intelligent Tools, Cameras and Electronic Controls in a Connected House (SRB) 29 1.10.3 Level if Clouds are the IoT Institute Level With SBs 31 1.10.4 Component of Smart Buildings (SB) 33 1.10.5 Machine Learning Tasks in Smart Building Environment 46 1.10.6 ML Tools and Services for Smart Building 47 1.10.7 Big Data Research Applications for SBs in Real-Time 51 1.10.8 Implementation of the ML Concept in the SB Context 51 1.11 Conclusion and Future Research 53 References 58 2 Fire Hazard Detection and Prediction by Machine Learning Techniques in Smart Buildings (SBs) Using Sensors and Unmanned Aerial Vehicles (UAVs) 63Sandhya Tarar and Namisha Bhasin 2.1 Introduction 64 2.1.1 Bluetooth 65 2.1.2 Unmanned Aerial Vehicle 65 2.1.3 Sensors 65 2.1.4 Problem Description 67 2.2 Literature Review 68 2.3 Experimental Methods 71 2.3.1 Univariate Time-Series 73 2.3.1.1 Naïve Bayes 74 2.3.1.2 Simple Average 74 2.3.1.3 Moving Average 75 2.3.1.4 Simple Exponential Smoothing (SES) 76 2.3.1.5 Holt’s Linear Trend 76 2.3.1.6 Holt–Winters Method 76 2.3.1.7 Autoregressive Integrated Moving Average Model (ARIMA) 77 2.3.2 Multivariate Time-Series Prediction 80 2.3.2.1 Vector Autoregressive (VAR) 80 2.3.3 Hidden Markov Model (HMM) 81 2.3.4 Fuzzy Logic 85 2.4 Results 89 2.5 Conclusion and Future Work 89 References 90 3 Sustainable Infrastructure Theories and Models 97Saurabh Jain, Keshav Kaushik, Deepak Kumar Sharma, Rajalakshmi Krishnamurthi and Adarsh Kumar 3.1 Introduction to Data Fusion Approaches in Sustainable Infrastructure 98 3.1.1 The Need for Sustainable Infrastructure 98 3.1.2 Data Fusion 99 3.1.3 Different Types of Data Fusion Architecture 100 3.1.3.1 Centralized Architecture 100 3.1.3.2 Decentralized Architecture 101 3.1.3.3 Distributed Architecture 101 3.1.3.4 Hierarchical Architecture 102 3.1.4 Smart Cities Application With Sustainable Infrastructures Based on Different Data Fusion Techniques 102 3.2 Smart City Infrastructure Approaches 104 3.2.1 Smart City Infrastructure 104 3.2.2 Smart City IoT Deployments 105 3.2.3 Smart City Control and Monitoring Centers 106 3.2.4 Theory of Unified City Modeling for Smart Infrastructure 108 3.2.5 Smart City Operational Modeling 109 3.3 Theories and Models 110 3.3.1 Sustainable Infrastructure Theories 110 3.3.2 Sustainable Infrastructure Models 112 3.4 Case Studies 113 3.4.1 Case Studies-1: Web Browsing History Analysis 113 3.4.1.1 Objective 115 3.4.2 Case Study-2: Data Model for Group Construction in Student’s Industrial Placement 117 3.5 Conclusion and Future Scope 121 References 122 4 Blockchain for Sustainable Smart Cities 127Iftikhar Ahmad, Syeda Warda Ashar, Umamma Khalid, Anmol Irfan and Wajeeha Khalil 4.1 Introduction 128 4.2 Smart City 130 4.2.1 Overview of Smart City 130 4.2.2 Evolution 130 4.2.3 Smart City’s Sub Systems 130 4.2.4 Domains of Smart City 132 4.2.5 Challenges 134 4.3 Blockchain 136 4.3.1 Motivation 137 4.3.2 The Birth of Blockchain 137 4.3.3 System of Blockchain 137 4.4 Use Cases of Smart City Implementing Blockchain 138 4.4.1 Blockchain-Based Smart Economy 138 4.4.1.1 Facilitating Faster and Cheaper International Payment 139 4.4.1.2 Distributed Innovations in Financial Transactions 139 4.4.1.3 Enhancing the Transparency of Supply/Global Commodity Chains 140 4.4.1.4 Equity Crowd Funding 141 4.4.2 Blockchain for Smart People 141 4.4.2.1 Elections through Blockchain Technology 141 4.4.2.2 Smart Contract 143 4.4.2.3 Protecting Personal Data 144 4.4.2.4 E-Health: Storing Health Records on Blockchain 145 4.4.2.5 Intellectual Property Rights 145 4.4.2.6 Digital Payments 146 4.4.2.7 Other Use Cases 146 4.4.3 Blockchain-Based Smart Governance 147 4.4.3.1 Transparent Record Keeping and Tracking of Records 147 4.4.3.2 Fraud Free Voting 148 4.4.3.3 Decision Making 150 4.4.4 Blockchain-Based Smart Transport 150 4.4.4.1 Digitizing Driving License 150 4.4.4.2 Smart Ride Sharing 150 4.4.5 Blockchain-Based Smart Environment 151 4.4.5.1 Social Plastic 151 4.4.5.2 Energy 152 4.4.5.3 Environmental Treaties 152 4.4.5.4 Carbon Tax 153 4.4.6 Blockchain-Based Smart Living 153 4.4.6.1 Fighting Against Frauds and Discriminatory Policies and Practices 154 4.4.6.2 Managing Change in Ownership 154 4.4.6.3 Sustainable Buildings 154 4.4.6.4 Other Use Cases 155 4.5 Conclusion 156 References 156 5 Contextualizing Electronic Governance, Smart City Governance and Sustainable Infrastructure in India: A Study and Framework 163Nitin K. Tyagi and Mukta Goyal 5.1 Introduction 164 5.2 Related Works 166 5.2.1 Research Questions 166 5.3 Related E-Governance Frameworks 178 5.3.1 Smart City Features in India 181 5.4 Proposed Smart Governance Framework 181 5.5 Results Discussion 185 5.5.1 Initial Stage 185 5.5.2 Design, Development and Delivery Stage 186 5.6 Conclusion 186 References 188 6 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 194 6.1.1 Aim, Objectives, and Methodology 196 6.1.2 Organization of Chapter 197 6.2 Background 197 6.2.1 Development of Smart Homes 197 6.2.2 Development of Smart Homes for Elderly 198 6.2.3 Indian Scenario 200 6.3 Need for Smart Homes: An Assessment of Requirements for the Elderly-Activity Mapping 201 6.3.1 Geriatric Smart Home Design: The Indian Context 202 6.3.2 Elderly Activity Mapping 202 6.3.3 Framework for Smart Homes for Elderly People 206 6.3.4 Architectural Interventions: Spatial Requirements for Daily Activities 207 6.3.5 Architectural Interventions to Address Issues Faced by Elderly People 208 6.4 Schematic Design for a Nesting Home: IoT-Enabled Smart Home for Elderly People 208 6.4.1 IoT-Based Real Time Automation for Nesting Homes 208 6.4.2 Technological Components of Elderly Smart Homes 212 6.4.2.1 Sensors for Smart Home 212 6.4.2.2 Health Monitoring System 213 6.4.2.3 Network Devices 213 6.4.2.4 Alerts 214 6.5 Worldwide Elderly Smart Homes 214 6.5.1 Challenges in Smart Elderly Homes 215 6.6 Conclusion and Future Scope 216 References 216 7 Sustainable E-Infrastructure for Blockchain-Based Voting System 221Mukta Goyal and Adarsh Kumar 7.1 Introduction 222 7.1.1 E-Voting Challenge 224 7.2 Related Works 224 7.3 System Design 227 7.4 Experimentation 230 7.4.1 Software Requirements 230 7.4.2 Function Requirements 230 7.4.2.1 Election Organizer 231 7.4.2.2 Candidate Registration 231 7.4.2.3 Voter Registration Process 232 7.4.3 Common Functional Requirement for All Users 233 7.4.3.1 Result Display 233 7.4.4 Non-Function Requirements 233 7.4.4.1 Performance Requirement 233 7.4.4.2 Security Requirement 233 7.4.4.3 Usability Requirement 233 7.4.4.4 Availability Requirement 234 7.4.5 Implementation Details 234 7.5 Findings & Results 237 7.5.1 Smart Contract Deployment 241 7.6 Conclusion and Future Scope 242 Acknowledgement 246 References 246 8 Impact of IoT-Enabled Smart Cities: A Systematic Review and Challenges 253K. Rajkumar and U. Hariharan 8.1 Introduction 254 8.2 Recent Development in IoT Application for Modern City 256 8.2.1 IoT Potential Smart City Approach 257 8.2.2 Problems and Related Solutions in Modern Smart Cities Application 259 8.3 Classification of IoT-Based Smart Cities 262 8.3.1 Program Developers 263 8.3.2 Network Type 263 8.3.3 Activities of Standardization Bodies of Smart City 263 8.3.4 Available Services 269 8.3.5 Specification 269 8.4 Impact of 5G Technology in IT, Big Data Analytics, and Cloud Computing 270 8.4.1 IoT Five-Layer Architecture for Smart City Applications 270 8.4.1.1 Sensing Layer (Get Information from Sensor) 272 8.4.1.2 Network Layer (Access and Also Transmit Information) 272 8.4.1.3 Data Storage and Analyzing 273 8.4.1.4 Smart Cities Model (Smart Industry Model, Smart Healthcare Model, Smart Cities, Smart Agriculture Model) 273 8.4.1.5 Application Layer (Dedicated Apps and Services) 273 8.4.2 IoT Computing Paradigm for Smart City Application 274 8.5 Research Advancement and Drawback on Smart Cities 280 8.5.1 Integration of Cloud Computing in Smart Cities 280 8.5.2 Integration of Applications 281 8.5.3 System Security 281 8.6 Summary of Smart Cities and Future Research Challenges and Their Guidelines 282 8.7 Conclusion and Future Direction 287 References 288 9 Indoor Air Quality (IAQ) in Green Buildings, a Pre-Requisite to Human Health and Well-Being 293Ankita Banerjee, N.P. Melkania and Ayushi Nain 9.1 Introduction 294 9.2 Pollutants Responsible for Poor IAQ 296 9.2.1 Volatile Organic Compounds (VOCs) 296 9.2.2 Particulate Matter (PM) 298 9.2.3 Asbestos 299 9.2.4 Carbon Monoxide (CO) 299 9.2.5 Environmental Tobacco Smoke (ETS) 300 9.2.6 Biological Pollutants 301 9.2.7 Lead (Pb) 303 9.2.8 Nitrogen Dioxide (NO2) 304 9.2.9 Ozone (O3) 305 9.3 Health Impacts of Poor IAQ 306 9.3.1 Sick Building Syndrome (SBS) 306 9.3.2 Acute Impacts 307 9.3.3 Chronic Impacts 308 9.4 Strategies to Maintain a Healthy Indoor Environment in Green Buildings 308 9.5 Conclusion and Future Scope 313 References 314 10 An Era of Internet of Things Leads to Smart Cities Initiatives Towards Urbanization 319Pooja Choudhary, Lava Bhargava, Ashok Kumar Suhag, Manju Choudhary and Satendra Singh 10.1 Introduction: Emergence of a Smart City Concept 320 10.2 Components of Smart City 321 10.2.1 Smart Infrastructure 323 10.2.2 Smart Building 323 10.2.3 Smart Transportation 325 10.2.4 Smart Energy 326 10.2.5 Smart Health Care 327 10.2.6 Smart Technology 328 10.2.7 Smart Citizen 329 10.2.8 Smart Governance 330 10.2.9 Smart Education 330 10.3 Role of IoT in Smart Cities 331 10.3.1 Intent of IoT Adoption in Smart Cities 333 10.3.2 IoT-Supported Communication Technologies 333 10.4 Sectors, Services Related and Principal Issues for IoT Technologies 336 10.5 Impact of Smart Cities 336 10.5.1 Smart City Impact on Science and Technology 336 10.5.2 Smart City Impact on Competitiveness 339 10.5.3 Smart City Impact on Society 339 10.5.4 Smart City Impact on Optimization and Management 339 10.5.5 Smart City for Sustainable Development 340 10.6 Key Applications of IoT in Smart Cities 340 10.7 Challenges 343 10.7.1 Smart City Design Challenges 343 10.7.2 Challenges Raised by Smart Cities 344 10.7.3 Challenges of IoT Technologies in Smart Cities 344 10.8 Conclusion 346 Acknowledgements 346 References 346 11 Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure 351Rajalakshmi Krishnamurthi, Dhanalekshmi Gopinathan and Adarsh Kumar 11.1 Introduction 352 11.2 Smart City and IoT 354 11.3 Mobile Computing for Smart City 357 11.4 Smart City and its Applications 360 11.4.1 Traffic Monitoring 360 11.4.2 Smart Lighting 361 11.4.3 Air Quality Monitoring 362 11.5 Smart Tourism in Smart City 363 11.6 Mobile Computing-Based Smart Tourism 366 11.7 Case Study: A Mobile Application for Trip Planner Task Scheduling in Smart City’s Sustainable Infrastructure 368 11.7.1 System Interfaces and User Interfaces 371 11.8 Experimentation and Results Discussion 371 11.9 Conclusion and Future Scope 373 References 374 12 Smart Health Monitoring for Elderly Care in Indoor Environments 379Sonia and Tushar Semwal 12.1 Introduction 380 12.2 Sensors 382 12.2.1 Human Traits 383 12.2.2 Sensors Description 384 12.2.2.1 Passive Sensors 385 12.2.2.2 Active Sensors 386 12.2.3 Sensing Challenges 387 12.3 Internet of Things and Connected Systems 387 12.4 Applications 389 12.5 Case Study 392 12.5.1 Case 1 392 12.5.2 Case 2 393 12.5.3 Challenges Involved 393 12.5.4 Possible Solution 393 12.6 Conclusion 395 12.7 Discussion 395 References 395 13 A Comprehensive Study of IoT Security Risks in Building a Secure Smart City 401Akansha Bhargava, Gauri Salunkhe, Sushant Bhargava and Prerna Goswami 13.1 Introduction 402 13.1.1 Organization of the Chapter 404 13.2 Related Works 405 13.3 Overview of IoT System in Smart Cities 407 13.3.1 Physical Devices 409 13.3.2 Connectivity 409 13.3.3 Middleware 410 13.3.4 Human Interaction 410 13.4 IoT Security Prerequisite 411 13.5 IoT Security Areas 413 13.5.1 Anomaly Detection 413 13.5.2 Host-Based IDS (HIDS) 414 13.5.3 Network-Based IDS (NIDS) 414 13.5.4 Malware Detection 414 13.5.5 Ransomware Detection 415 13.5.6 Intruder Detection 415 13.5.7 Botnet Detection 415 13.6 IoT Security Threats 416 13.6.1 Passive Threats 416 13.6.2 Active Threats 417 13.7 Review of ML/DL Application in IoT Security 418 13.7.1 Machine Learning Methods 421 13.7.1.1 Decision Trees (DTs) 421 13.7.1.2 K-Nearest Neighbor (KNN) 423 13.7.1.3 Random Forest 424 13.7.1.4 Principal Component Analysis (PCA) 425 13.7.1.5 Naïve Bayes 425 13.7.1.6 Support Vector Machines (SVM) 425 13.7.2 Deep Learning Methods 426 13.7.2.1 Convolutional Neural Networks (CNNs) 427 13.7.2.2 Auto Encoder (AE) 429 13.7.2.3 Recurrent Neural Networks (RNNs) 429 13.7.2.4 Restricted Boltzmann Machines (RBMs) 432 13.7.2.5 Deep Belief Networks (DBNs) 433 13.7.2.6 Generative Adversarial Networks (GANs) 433 13.8 Challenges 434 13.8.1 IoT Dataset Unavailability 434 13.8.2 Computational Complications 434 13.8.3 Forensics Challenges 435 13.9 Future Prospects 436 13.9.1 Implementation of ML/DL With Edge Computing 437 13.9.2 Integration of ML/DL With Blockchain 438 13.9.3 Integration of ML/DL With Fog Computing 439 13.10 Conclusion 439 References 440 14 Role of Smart Buildings in Smart City—Components, Technology, Indicators, Challenges, Future Research Opportunities 449Tarana Singh, Arun Solanki and Sanjay Kumar Sharma 14.1 Introduction 449 14.1.1 Chapter Organization 453 14.2 Literature Review 453 14.3 Components of Smart Cities 455 14.3.1 Smart Infrastructure 455 14.3.2 Smart Parking Management 456 14.3.3 Connected Charging Stations 457 14.3.4 Smart Buildings and Properties 457 14.3.5 Smart Garden and Sprinkler Systems 457 14.3.6 Smart Heating and Ventilation 457 14.3.7 Smart Industrial Environment 458 14.3.8 Smart City Services 458 14.3.9 Smart Energy Management 458 14.3.10 Smart Water Management 459 14.3.11 Smart Waste Management 459 14.4 Characteristics of Smart Buildings 459 14.4.1 Minimal Human Control 459 14.4.2 Optimization 460 14.4.3 Qualities 460 14.4.4 Connected Systems 460 14.4.5 Use of Sensors 460 14.4.6 Automation 461 14.4.7 Data 461 14.5 Supporting Technology 461 14.5.1 Big Data and IoT in Smart Cities 461 14.5.2 Sensors 462 14.5.3 5G Connectivity 462 14.5.4 Geospatial Technology 462 14.5.5 Robotics 463 14.6 Key Performance Indicators of Smart City 463 14.6.1 Smart Economy 463 14.6.2 Smart Governance 464 14.6.3 Smart Mobility 464 14.6.4 Smart Environment 464 14.6.5 Smart People 464 14.6.6 Smart Living 465 14.7 Challenges While Working for Smart City 465 14.7.1 Retrofitting Existing Legacy City Infrastructure to Make it Smart 465 14.7.2 Financing Smart Cities 466 14.7.3 Availability of Master Plan or City Development Plan 466 14.7.4 Financial Sustainability of ULBs 466 14.7.5 Technical Constraints ULBs 466 14.7.6 Three-Tier Governance 467 14.7.7 Providing Clearances in a Timely Manner 467 14.7.8 Dealing With a Multivendor Environment 467 14.7.9 Capacity Building Program 467 14.7.10 Reliability of Utility Services 468 14.8 Future Research Opportunities in Smart City 468 14.8.1 IoT Management 468 14.8.2 Data Management 469 14.8.3 Smart City Assessment Framework 469 14.8.4 VANET Security 469 14.8.5 Improving Photovoltaic Cells 469 14.8.6 Smart City Enablers 470 14.8.7 Information System Risks 470 14.9 Conclusion 470 References 471 15 Effects of Green Buildings on the Environment 477Ayushi Nain, Ankita Banerjee and N.P. Melkania 15.1 Introduction 478 15.2 Sustainability and the Building Industry 480 15.2.1 Environmental Benefits 481 15.2.2 Social Benefits 483 15.2.3 Economic Benefits 483 15.3 Goals of Green Buildings 484 15.3.1 Green Design 485 15.3.2 Energy Efficiency 485 15.3.3 Water Efficiency 487 15.3.4 Material Efficiency 489 15.3.5 Improved Internal Environment and Air Quality 490 15.3.6 Minimization of Wastes 492 15.3.7 Operations and Maintenance Optimization 492 15.4 Impacts of Classical Buildings that Green Buildings Seek to Rectify 493 15.4.1 Energy Use in Buildings 494 15.4.2 Green House Gas (GHG) Emissions 494 15.4.3 Indoor Air Quality 494 15.4.4 Building Water Use 496 15.4.5 Use of Land and Consumption 496 15.4.6 Construction Materials 497 15.4.7 Construction and Demolition (C&D) Wastes 498 15.5 Green Buildings in India 498 15.6 Conclusion 503 Acknowledgement 504 Acronyms 504 References 505 Index 509

This book details applications of technology to efficient digital city infrastructure and its planning, including smart buildings.Rapid urbanization, demographic changes, environmental changes, and new technologies are changing the views of urban leaders on sustainability, as well as creating and providing public services to tackle these new dynamics. Sustainable development is an objective by which the processes of planning, implementing projects, and development is aimed at meeting the needs of modern communities without compromising the potential of future generations. The advent of Smart Cities is the answer to these problems.Digital Cities Roadmap provides an in-depth analysis of design technologies that lay a solid foundation for sustainable buildings. The book also highlights smart automation technologies that help save energy, as well as various performance indicators needed to make construction easier. The book aims to create a strong research community, to have a deep understanding and the latest knowledge in the field of energy and comfort, to offer solid ideas in the nearby future for sustainable and resilient buildings. These buildings will help the city grow as a smart city. The smart city has also a focus on low energy consumption, renewable energy, and a small carbon footprint.AudienceThe information provided in this book will be of value to researchers, academicians and industry professionals interested in IoT-based architecture and sustainable buildings, energy efficiency and various tools and methods used to develop green technologies for construction in smart cities.