Оглавление
Группа авторов. Digital Cities Roadmap
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
Digital Cities Roadmap. IoT-Based Architecture and Sustainable Buildings
Preface
1. The Use of Machine Learning for Sustainable and Resilient Buildings
1.1 Introduction of ML Sustainable Resilient Building
1.2 Related Works
1.3 Machine Learning
1.4 What is Resilience?
1.4.1 Sustainability and Resiliency Conditions
1.4.2 Paradigm and Challenges of Sustainability and Resilience
1.4.3 Perspectives of Local Community
1.5 Sustainability and Resilience of Engineered System
1.5.1. Resilience and Sustainable Development Framework for Decision-Making
1.5.2. Exposures and Disturbance Events
1.5.3 Quantification of Resilience
1.5.4 Quantification of Sustainability
1.6 Community and Quantification Metrics, Resilience and Sustainability Objectives
1.6.1 Definition of Quantification Metric
1.6.2 Considering and Community
1.7 Structure Engineering Dilemmas and Resilient Epcot
1.7.1 Dilation of Resilience Essence
1.7.2 Quality of Life
First Dilemma
Second Dilemma
Strategies of Resilience
Third Dilemma
Possible Solution to the Core Resilience Problem
1.8 Development of Risk Informed Criteria for Building Design Hurricane Resilient on Building
1.9 Resilient Infrastructures Against Earthquake and Tsunami Multi-Hazard
1.10 Machine Learning With Smart Building
1.10.1 Smart Building Appliances
1.10.2 Intelligent Tools, Cameras and Electronic Controls in a Connected House (SRB)
1.10.3 Level if Clouds are the IoT Institute Level With SBs
1.10.4 Component of Smart Buildings (SB)
Sensors and Actuators for SBs
Smart Control Devices
Networking and Home Gateway
Machine Learning Framework
1.10.5 Machine Learning Tasks in Smart Building Environment
1.10.6 ML Tools and Services for Smart Building
1.10.7 Big Data Research Applications for SBs in Real-Time
1.10.8 Implementation of the ML Concept in the SB Context
Smart Building Services Taxonomy
1.11 Conclusion and Future Research
References
2. Fire Hazard Detection and Prediction by Machine Learning Techniques in Smart Buildings (SBs) Using Sensors and Unmanned Aerial Vehicles (UAVs)
2.1Introduction
2.1.1 Bluetooth
2.1.2 Unmanned Aerial Vehicle
2.1.3 Sensors
2.1.4 Problem Description
2.2 Literature Review
2.3 Experimental Methods
2.3.1 Univariate Time-Series
2.3.1.1 Naïve Bayes
2.3.1.2 Simple Average
2.3.1.3 Moving Average
2.3.1.4 Simple Exponential Smoothing (SES)
2.3.1.5 Holt’s Linear Trend
2.3.1.6 Holt–Winters Method
2.3.1.7Autoregressive Integrated Moving Average Model (ARIMA)
2.3.2 Multivariate Time-Series Prediction
2.3.2.1 Vector Autoregressive (VAR)
2.3.3 Hidden Markov Model (HMM)
2.3.4 Fuzzy Logic
2.4 Results
2.5 Conclusion and Future Work
References
3. Sustainable Infrastructure Theories and Models
3.1 Introduction to Data Fusion Approaches in Sustainable Infrastructure
3.1.1 The Need for Sustainable Infrastructure
3.1.2 Data Fusion
3.1.3 Different Types of Data Fusion Architecture
3.1.3.1 Centralized Architecture
3.1.3.2 Decentralized Architecture
3.1.3.3 Distributed Architecture
3.1.3.4 Hierarchical Architecture
3.1.4 Smart Cities Application With Sustainable Infrastructures Based on Different Data Fusion Techniques
3.2 Smart City Infrastructure Approaches
3.2.1 Smart City Infrastructure
3.2.2 Smart City IoT Deployments
3.2.3 Smart City Control and Monitoring Centers
3.2.4 Theory of Unified City Modeling for Smart Infrastructure
3.2.5 Smart City Operational Modeling
3.3 Theories and Models
3.3.1 Sustainable Infrastructure Theories
3.3.2 Sustainable Infrastructure Models
3.4 Case Studies
3.4.1 Case Studies-1: Web Browsing History Analysis
3.4.1.1 Objective
3.4.2 Case Study-2: Data Model for Group Construction in Student’s Industrial Placement
3.5 Conclusion and Future Scope
References
4. Blockchain for Sustainable Smart Cities
4.1 Introduction
4.2 Smart City. 4.2.1 Overview of Smart City
4.2.2 Evolution
4.2.3 Smart City’s Sub Systems
4.2.4 Domains of Smart City
4.2.5 Challenges
4.3 Blockchain
4.3.1 Motivation
4.3.2 The Birth of Blockchain
4.3.3 System of Blockchain
4.4 Use Cases of Smart City Implementing Blockchain
4.4.1 Blockchain-Based Smart Economy
4.4.1.1 Facilitating Faster and Cheaper International Payment
4.4.1.2 Distributed Innovations in Financial Transactions
4.4.1.3 Enhancing the Transparency of Supply/Global Commodity Chains
4.4.1.4 Equity Crowd Funding
4.4.2 Blockchain for Smart People
4.4.2.1 Elections through Blockchain Technology
4.4.2.2 Smart Contract
4.4.2.3 Protecting Personal Data
4.4.2.4 E-Health: Storing Health Records on Blockchain
4.4.2.5 Intellectual Property Rights
4.4.2.6 Digital Payments
4.4.2.7 Other Use Cases
4.4.3 Blockchain-Based Smart Governance
4.4.3.1 Transparent Record Keeping and Tracking of Records
4.4.3.2 Fraud Free Voting
4.4.3.3 Decision Making
4.4.4 Blockchain-Based Smart Transport. 4.4.4.1 Digitizing Driving License
4.4.4.2 Smart Ride Sharing
4.4.5 Blockchain-Based Smart Environment
4.4.5.1 Social Plastic
4.4.5.2 Energy
4.4.5.3 Environmental Treaties
4.4.5.4 Carbon Tax
4.4.6 Blockchain-Based Smart Living
4.4.6.1 Fighting Against Frauds and Discriminatory Policies and Practices
4.4.6.2 Managing Change in Ownership
4.4.6.3 Sustainable Buildings
4.4.6.4 Other Use Cases
4.5 Conclusion
References
5. Contextualizing Electronic Governance, Smart City Governance and Sustainable Infrastructure in India: A Study and Framework
5.1 Introduction
5.2 Related Works
5.2.1 Research Questions
5.3 Related E-Governance Frameworks
5.3.1 Smart City Features in India
5.4 Proposed Smart Governance Framework
5.5 Results Discussion
5.5.1 Initial Stage
5.5.2 Design, Development and Delivery Stage
5.6 Conclusion
References
6. Revolutionizing Geriatric Design in Developing Countries: IoT-Enabled Smart Home Design for the Elderly
6.1 Introduction to Geriatric Design
6.1.1 Aim, Objectives, and Methodology
6.1.2 Organization of Chapter
6.2 Background. 6.2.1 Development of Smart Homes
6.2.2 Development of Smart Homes for Elderly
6.2.3 Indian Scenario
6.3 Need for Smart Homes: An Assessment of Requirements for the Elderly-Activity Mapping
6.3.1 Geriatric Smart Home Design: The Indian Context
6.3.2 Elderly Activity Mapping
6.3.3 Framework for Smart Homes for Elderly People
6.3.4 Architectural Interventions: Spatial Requirements for Daily Activities
6.3.5 Architectural Interventions to Address Issues Faced by Elderly People
6.4 Schematic Design for a Nesting Home: IoT-Enabled Smart Home for Elderly People. 6.4.1 IoT-Based Real Time Automation for Nesting Homes
6.4.2 Technological Components of Elderly Smart Homes. 6.4.2.1 Sensors for Smart Home
6.4.2.2 Health Monitoring System
6.4.2.3 Network Devices
6.4.2.4 Alerts
6.5 Worldwide Elderly Smart Homes
6.5.1 Challenges in Smart Elderly Homes
6.6 Conclusion and Future Scope
References
7. Sustainable E-Infrastructure for Blockchain-Based Voting System
7.1 Introduction
7.1.1 E-Voting Challenge
7.2 Related Works
7.3 System Design
7.4 Experimentation
7.4.1 Software Requirements
7.4.2 Function Requirements
7.4.2.1 Election Organizer
7.4.2.2 Candidate Registration
7.4.2.3 Voter Registration Process
7.4.3 Common Functional Requirement for All Users. 7.4.3.1 Result Display
7.4.4 Non-Function Requirements
7.4.4.1 Performance Requirement
7.4.4.2 Security Requirement
7.4.4.3 Usability Requirement
7.4.4.4 Availability Requirement
7.4.5 Implementation Details
7.5 Findings & Results
7.5.1 Smart Contract Deployment
7.6 Conclusion and Future Scope
Acknowledgement
References
8. Impact of IoT-Enabled Smart Cities: A Systematic Review and Challenges
8.1 Introduction
8.2 Recent Development in IoT Application for Modern City
8.2.1 IoT Potential Smart City Approach
8.2.2 Problems and Related Solutions in Modern Smart Cities Application
8.3 Classification of IoT-Based Smart Cities
8.3.1 Program Developers
8.3.2 Network Type
8.3.3 Activities of Standardization Bodies of Smart City
8.3.4 Available Services
8.3.5 Specification
8.4 Impact of 5G Technology in IT, Big Data Analytics, and Cloud Computing
8.4.1 IoT Five-Layer Architecture for Smart City Applications
8.4.1.1 Sensing Layer (Get Information from Sensor)
8.4.1.2 Network Layer (Access and Also Transmit Information)
8.4.1.3 Data Storage and Analyzing
8.4.1.4 Smart Cities Model (Smart Industry Model, Smart Healthcare Model, Smart Cities, Smart Agriculture Model)
8.4.1.5 Application Layer (Dedicated Apps and Services)
8.4.2 IoT Computing Paradigm for Smart City Application
8.5 Research Advancement and Drawback on Smart Cities. 8.5.1 Integration of Cloud Computing in Smart Cities
8.5.2 Integration of Applications
8.5.3 System Security
8.6 Summary of Smart Cities and Future Research Challenges and ThTheir Guidelines
8.7 Conclusion and Future Direction
References
9. Indoor Air Quality (IAQ) in Green Buildings, a Pre-Requisite to Human Health and Well-Being
9.1 Introduction
9.2 Pollutants Responsible for Poor IAQ
9.2.1 Volatile Organic Compounds (VOCs)
9.2.2 Particulate Matter (PM)
9.2.3 Asbestos
9.2.4 Carbon Monoxide (CO)
9.2.5 Environmental Tobacco Smoke (ETS)
9.2.6 Biological Pollutants
9.2.7 Lead (Pb)
9.2.8 Nitrogen Dioxide (NO2)
9.2.9 Ozone (O3)
9.3 Health Impacts of Poor IAQ
9.3.1 Sick Building Syndrome (SBS)
9.3.2 Acute Impacts
9.3.3 Chronic Impacts
9.4 Strategies to Maintain a Healthy Indoor Environment in Green Buildings
9.5 Conclusion and Future Scope
References
10. An Era of Internet of Things Leads to Smart Cities Initiatives Towards Urbanization
10.1 Introduction: Emergence of a Smart City Concept
10.2 Components of Smart City
10.2.1 Smart Infrastructure
10.2.2 Smart Building
10.2.3 Smart Transportation
10.2.4 Smart Energy
10.2.5 Smart Health Care
10.2.6 Smart Technology
10.2.7 Smart Citizen
10.2.8 Smart Governance
10.2.9 Smart Education
10.3 Role of IoT in Smart Cities
10.3.1 Intent of IoT Adoption in Smart Cities
10.3.2 IoT-Supported Communication Technologies
10.4 Sectors, Services Related and Principal Issues for IoT Technologies
10.5 Impact of Smart Cities
10.5.1 Smart City Impact on Science and Technology
10.5.2 Smart City Impact on Competitiveness
10.5.3 Smart City Impact on Society
10.5.4 Smart City Impact on Optimization and Management
10.5.5 Smart City for Sustainable Development
10.6 Key Applications of IoT in Smart Cities
10.7 Challenges. 10.7.1 Smart City Design Challenges
10.7.2 Challenges Raised by Smart Cities
10.7.3 Challenges of IoT Technologies in Smart Cities
10.8 Conclusion
Acknowledgements
References
11. Trip-I-Plan: A Mobile Application for Task Scheduling in Smart City’s Sustainable Infrastructure
11.1 Introduction
11.2 Smart City and IoT
11.3 Mobile Computing for Smart City
11.4 Smart City and its Applications
11.4.1 Traffic Monitoring
11.4.2 Smart Lighting
11.4.3 Air Quality Monitoring
11.5 Smart Tourism in Smart City
11.6 Mobile Computing-Based Smart Tourism
11.7 Case Study: A Mobile Application for Trip Planner Task Scheduling in Smart City’s Sustainable Infrastructure
11.7.1 System Interfaces and User Interfaces
11.8 Experimentation and Results Discussion
11.9 Conclusion and Future Scope
References
12. Smart Health Monitoring for Elderly Care in Indoor Environments
12.1 Introduction
12.2 Sensors
12.2.1 Human Traits
12.2.2 Sensors Description
12.2.2.1 Passive Sensors
12.2.2.2 Active Sensors
12.2.3 Sensing Challenges
12.3 Internet of Things and Connected Systems
12.4 Applications
12.5 Case Study
12.5.1 Case 1
12.5.2 Case 2
12.5.3 Challenges Involved
12.5.4 Possible Solution
12.6 Conclusion
12.7 Discussion
References
13. A Comprehensive Study of IoT Security Risks in Building a Secure Smart City
13.1 Introduction
13.1.1 Organization of the Chapter
13.2 Related Works
13.3 Overview of IoT System in Smart Cities
13.3.1 Physical Devices
13.3.2 Connectivity
13.3.3 Middleware
13.3.4 Human Interaction
13.4 IoT Security Prerequisite
13.5 IoT Security Areas
13.5.1 Anomaly Detection
13.5.2 Host-Based IDS (HIDS)
13.5.3 Network-Based IDS (NIDS)
13.5.4 Malware Detection
13.5.5 Ransomware Detection
13.5.6 Intruder Detection
13.5.7 Botnet Detection
13.6 IoT Security Threats
13.6.1 Passive Threats
13.6.2 Active Threats
13.7 Review of ML/DL Application in IoT Security
13.7.1 Machine Learning Methods
13.7.1.1 Decision Trees (DTs)
13.7.1.2 K-Nearest Neighbor (KNN)
13.7.1.3 Random Forest
13.7.1.4 Principal Component Analysis (PCA)
13.7.1.5 Naïve Bayes
13.7.1.6 Support Vector Machines (SVM)
13.7.2 Deep Learning Methods
13.7.2.1 Convolutional Neural Networks (CNNs)
13.7.2.2 Auto Encoder (AE)
13.7.2.3 Recurrent Neural Networks (RNNs)
13.7.2.4 Restricted Boltzmann Machines (RBMs)
13.7.2.5 Deep Belief Networks (DBNs)
13.7.2.6 Generative Adversarial Networks (GANs)
13.8 Challenges
13.8.1 IoT Dataset Unavailability
13.8.2 Computational Complications
13.8.3 Forensics Challenges
13.9 Future Prospects
13.9.1 Implementation of ML/DL With Edge Computing
13.9.2 Integration of ML/DL With Blockchain
13.9.3 Integration of ML/DL With Fog Computing
13.10 Conclusion
References
14. Role of Smart Buildings in Smart City—Components, Technology, Indicators, Challenges, Future Research Opportunities
14.1 Introduction
14.1.1 Chapter Organization
14.2 Literature Review
14.3 Components of Smart Cities
14.3.1 Smart Infrastructure
14.3.2 Smart Parking Management
14.3.3 Connected Charging Stations
14.3.4 Smart Buildings and Properties
14.3.5 Smart Garden and Sprinkler Systems
14.3.6 Smart Heating and Ventilation
14.3.7 Smart Industrial Environment
14.3.8 Smart City Services
14.3.9 Smart Energy Management
14.3.10 Smart Water Management
14.3.11 Smart Waste Management
14.4 Characteristics of Smart Buildings
14.4.1 Minimal Human Control
14.4.2 Optimization
14.4.3 Qualities
14.4.4 Connected Systems
14.4.5 Use of Sensors
14.4.6 Automation
14.4.7 Data
14.5 Supporting Technology
14.5.1 Big Data and IoT in Smart Cities
14.5.2 Sensors
14.5.3 5G Connectivity
14.5.4 Geospatial Technology
14.5.5 Robotics
14.6 Key Performance Indicators of Smart City
14.6.1 Smart Economy
14.6.2 Smart Governance
14.6.3 Smart Mobility
14.6.4 Smart Environment
14.6.5 Smart People
14.6.6 Smart Living
14.7 Challenges While Working for Smart City
14.7.1 Retrofitting Existing Legacy City Infrastructure to Make it Smart
14.7.2 Financing Smart Cities
14.7.3 Availability of Master Plan or City Development Plan
14.7.4 Financial Sustainability of ULBs
14.7.5 Technical Constraints ULBs
14.7.6 Three-Tier Governance
14.7.7 Providing Clearances in a Timely Manner
14.7.8 Dealing With a Multivendor Environment
14.7.9 Capacity Building Program
14.7.10 Reliability of Utility Services
14.8 Future Research Opportunities in Smart City
14.8.1 IoT Management
14.8.2 Data Management
14.8.3 Smart City Assessment Framework
14.8.4 VANET Security
14.8.5 Improving Photovoltaic Cells
14.8.6 Smart City Enablers
14.8.7 Information System Risks
14.9 Conclusion
References
15. Effects of Green Buildings on the Environment
15.1 Introduction
15.2 Sustainability and the Building Industry
15.2.1 Environmental Benefits
15.2.2 Social Benefits
15.2.3 Economic Benefits
15.3 Goals of Green Buildings
15.3.1 Green Design
15.3.2 Energy Efficiency
15.3.3 Water Efficiency
15.3.4 Material Efficiency
15.3.5 Improved Internal Environment and Air Quality
15.3.6 Minimization of Wastes
15.3.7 Operations and Maintenance Optimization
15.4 Impacts of Classical Buildings that Green Buildings Seek to Rectify
15.4.1 Energy Use in Buildings
15.4.2 Green House Gas (GHG) Emissions
15.4.3 Indoor Air Quality
15.4.4 Building Water Use
15.4.5 Use of Land and Consumption
15.4.6 Construction Materials
15.4.7 Construction and Demolition (C&D) Wastes
15.5 Green Buildings in India
15.6 Conclusion
Acknowledgement
Acronyms
References
Index
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