Challenges and Solutions for Sustainable Smart City Development (EAI/Springer Innovations in Communication and Computing)

Preface
Acknowledgement
Contents
About the Editors
Fog and Edge Computing for Automotive Applications
	1 Introduction
	2 Scope of Edge and Fog Computing in Automotive Environment
	3 Internet of Vehicle (IoV)
		3.1 IoV Architecture
		3.2 Challenges Faced in Smart and Secure IoV
	4 Vehicular Edge Computing (VEC)
		4.1 Edge Computing Architecture
		4.2 Impact of VEC on Smart Vehicles
	5 Fog Computing
		5.1 Overview
		5.2 Fog Computing Architecture
		5.3 Application and Data Management in Fog Computing Networks
		5.4 Simulation Tools for Fog Computing
	6 Comparison Between Edge and Fog Computing
	7 Discussion
	8 Conclusion
	References
Intelligent Transportation System in Smart City: A SWOT Analysis
	1 Introduction
	2 Intelligent Transportation System (ITS)
		2.1 ITS Structure
			2.1.1 Physical Layer
			2.1.2 Communication Layer
			2.1.3 Operation Layer
			2.1.4 Service Layer
		2.2 Autonomous Vehicles
			2.2.1 Classification of Autonomous Vehicles
				2.2.1.1 Level 1: Operation Specialized Automation
				2.2.1.2 Level 2: Joint Operation Automation
				2.2.1.3 Level 3: Customized Automation
				2.2.1.4 Level 4: Complete Self-Driving Automation
			2.2.2 Fundamental Objectives of Autonomous Automobiles
		2.3 ITS Algorithm
			2.3.1 Approach and Methodology
			2.3.2 Algorithm
		2.4 Smart Public Transportation
			2.4.1 Explanation
			2.4.2 Methodology
			2.4.3 Algorithm for System Execution
	3 SWOT Analysis
		3.1 Strength
			3.1.1 Improves Traffic Well-Being (Safety)
			3.1.2 Diminishing Infrastructural Harm/Damage
			3.1.3 Traffic and Traffic Light Control
			3.1.4 Vehicle Parking Administration
			3.1.5 Collecting Traffic Data
		3.2 Weakness
			3.2.1 Cost
			3.2.2 Technology Challenges
			3.2.3 Evacuation of Old Vehicles
			3.2.4 Issue of Unemployment
			3.2.5 Security and Protection Concern
			3.2.6 Guidelines and Regulation
		3.3 Opportunities
			3.3.1 Blockage and Climate Alerts
			3.3.2 Path Navigation
			3.3.3 Obstruction Acknowledgment
			3.3.4 Night-Perception Improvement
			3.3.5 Intelligent Cruise Control (ICC) and Lane Keeping Assistance
			3.3.6 Avoidance and Warning of Collision
			3.3.7 Driver Status Monitoring
			3.3.8 Sign in to the Vehicle
			3.3.9 Occupant Safety
		3.4 Threats
			3.4.1 Physical Strikes and Dangers
			3.4.2 Organization Attacks and Threats
			3.4.3 Remote Attacks and Threats
			3.4.4 Suitable Practices
			3.4.5 Challenges
			3.4.6 Further Involvement of IoV (Internet of Vehicle)
			3.4.7 Utilization of Multiple-Source Data in ITS
			3.4.8 Automated Driving
			3.4.9 Model Validation
			3.4.10 Security
			3.4.11 Standard Measurements for Course Assessment
			3.4.12 Dynamic Ideal Path
			3.4.13 Arranging and Programming the Public Capital in Transportation
	4 Results and Discussion
		4.1 ITS Algorithm
	5 Conclusion
	References
Deep Learning in Smart Applications: Approaches and Challenges
	1 Introduction
	2 Waste Segregation and Classification
		2.1 Waste Materials
		2.2 Database Information
		2.3 Methodology in Waste Segregation
			2.3.1 Object Detection
			2.3.2 Feature Extraction and Classification
			2.3.3 Supervised Architectures for Waste Image Classification
				2.3.3.1 AlexNet Architecture for Waste Image Classification
				2.3.3.2 Faster R-CNN
		2.4 Network Training Methods
			2.4.1 Transfer Learning
			2.4.2 Data Augmentation
		2.5 Challenges and Future Research Ideas
	3 Diabetic Retinopathy
		3.1 Retinal Imaging Modalities
		3.2 Retinal Lesions
		3.3 Stages of DR
		3.4 Datasets
		3.5 Early Prognosis of DR Using Retinal Images
			3.5.1 Pre-processing of Retinal Images
				3.5.1.1 Extraction of Green Channel
				3.5.1.2 Contrast Enhancement
			3.5.2 Segmentation of Retinal Pathologies
			3.5.3 Deep Learning Architectures to Diagnose DR
			3.5.4 Research Ideas
	4 Conclusion
	References
Smart Metering Using IoT and ICT for Sustainable Seller Consumer in Smart City
	1 Introduction
	2 Sustainable Seller Consumer and Requirement Side Maintenance
	3 Unstable Smart Home Load and Smart Metering
	4 Sustainable Seller Consumers
	5 Conclusion
	References
Deep Learning-Based Activity Monitoring for Smart Environment Using Radar
	1 Introduction
	2 Common Types of Radar
		2.1 Continuous-Wave Radar
		2.2 Pulse Radar
		2.3 Moving Target Indicator Radar
		2.4 Pulse Doppler Radar
		2.5 Frequency-Modulated Continuous-Wave Radar
		2.6 Ultra-Wideband Radar
	3 Micro-Doppler Phenomena and Joint Time-Frequency Signal Processing
		3.1 Micro-Doppler Signatures
		3.2 Time-Frequency Signal Processing
		3.3 Spectrogram
	4 Machine Learning Algorithms for Target Detection and Classification
		4.1 Classification of Machine Learning Techniques
		4.2 Classification of Machine Learning Techniques
	5 Future Research Directions
	6 Summary
	References
GIS-Based Air Quality Index Spatial Model for Indian Cities
	1 Introduction
	2 Air Pollution (Figs. 1, 2, 3, 4, and 5)
		2.1 Definition
	3 Types of Pollutants
	4 Indian Government Programmes
		4.1 National Air Monitoring Programme (NAMP)
		4.2 The System of Air Quality and Weather Forecasting and Research (SAFAR)
		4.3 National Clean Air Action Plan (NCAP)
	5 Air Quality Index
		5.1 Air Quality Index (AQI), India
		5.2 Sub-index Formula
	6 Spatial Modelling
	7 Software Used
		7.1 ESRI ArcMap 10.7.1
			7.1.1 ModelBuilder
	8 Concept of Spatial and Gridded Air Quality Index Model
	9 Study Area
		9.1 About Ahmedabad
	10 Meteorological Parameters of Ahmedabad
		10.1 Ambient Air Temperature (Degrees C)
		10.2 Relative Humidity (%)
		10.3 Average Annual Rainfall
		10.4 Wind Patterns [19] (Fig. 11)
	11 Ground Monitoring Stations in Ahmedabad
		11.1 List of Monitoring Stations
			11.1.1 Gujarat Pollution Control Board (GPCB) Monitoring Stations
			11.1.2 Central Pollution Control Board (CPCB) Monitoring Stations
			11.1.3 SAFAR Monitoring Stations
	12 Data Collection
	13 Data Processing
		13.1 Project
		13.2 Table Join
		13.3 Buffer
	14 Interpolation
	15 Sub-index Calculation Models
		15.1 PM2.5 (Fig. 26)
			15.1.1 Input Parameters
			15.1.2 Tools Used
			15.1.3 Output
		15.2 PM10 (Fig. 29)
			15.2.1 Input Parameters
			15.2.2 Tools Used
			15.2.3 Output
			15.2.4 SO2
			15.2.5 Output (Figs. 32 and 33)
		15.3 NO2
			15.3.1 Output (Figs. 34 and 35)
	16 Overall Air Quality Index (AQI) Model (Fig. 36)
		16.1 Input Parameters
		16.2 Tools Used
		16.3 Output
	17 Conclusion
	Annexure 1: Scripts
		Script for Calculating PM2.5 Sub-index
		Script for Calculating PM2.5 Sub-index Class
		Script for Calculating PM10 Sub-index
		Script for Calculating PM10 Sub-index Class
		Script for Calculating SO2 Sub-index Values
		Script for Calculating NO2 Sub-index Values
	References
Intelligent Wearable Electronics: A New Paradigm in Smart Electronics
	1 Introduction
	2 A Brief History
	3 Market Size for Wearable Electronics
	4 WE Architecture and Operation
		4.1 Epidermis as the Information/Data Site
		4.2 Sensor Modules
			4.2.1 Mechanical Wearable Sensors
				4.2.1.1 Piezoresistive Mechanical Wearable Sensors
				4.2.1.2 Capacitive Mechanical Wearable Sensors
				4.2.1.3 Piezoelectric Mechanical Wearable Sensors
			4.2.2 Electrical Wearable Sensor
		4.3 WE Operation Principle
	5 Popular WE
		5.1 WE in Healthcare Domain
			5.1.1 Sensors
				5.1.1.1 Pressure/Force Sensors
				5.1.1.2 Temperature Sensors
				5.1.1.3 Biochemical Sensors
		5.2 WE as Smart Textile
		5.3 WE for Education
	6 Power/Energy Unit for WE
	7 Cloud Computing for WE
	8 Challenges
	9 Future Trends
	References
Road Traffic Congestion Monitoring in Urban Areas: A Review
	1 Introduction
	2 Characterizing Road Traffic Congestion
		2.1 Roadway Users and Congestion
		2.2 Networks and Flows
		2.3 Time
	3 Review of Congestion Monitoring and Assessment Systems
	4 Conclusions and Scope for Future Research
	References
Smart Waste Management Model for Effective Disposal of Waste Management Through Technology
	1 Introduction
		1.1 Waste Management
	2 Solid Waste Disposal and Management
		2.1 Methods of Solid Waste Disposal
			2.1.1 Landfill
			2.1.2 Incineration
			2.1.3 Biogas Generation
			2.1.4 Composting
			2.1.5 Vermicomposting
	3 Medical Waste Treatment
		3.1 Steam Sterilization
		3.2 Advanced Autoclaves
		3.3 Microwaves
		3.4 Chemical Processes
		3.5 Plasma Gasification
	4 Challenges Faced in SWM and Solutions
	5 Recent Methodologies for Solid Waste Management
		5.1 Automated Waste Collection and Transportation
			5.1.1 IoT in Solid Waste Management
		5.2 Route Optimization
	6 Segregation and Sorting
	7 Energy Recovery
		7.1 New Ways to Recycle Precious Materials
	8 Zero Waste Concept
	9 Conclusion
	References
Edge Analytics and Deep Learning for Sustainable Development
	1 Introduction
	2 Edge Computing and Edge Analytics
		2.1 Edge Computing
		2.2 Role of Edge Computing
		2.3 Edge Architecture
		2.4 Strategic Advantages of Using Edge Computing
	3 Deep Learning
		3.1 What Is Deep Learning?
		3.2 How Deep Learning Works?
		3.3 Difference Between ML and DL
		3.4 DL Methods
		3.5 Practical Implementations of DEEP LEARNING
	4 Sustainable Implementation of EI and DEEP LEARNING
		4.1 Real-Time Video Analytic
		4.2 Autonomous Internet of Vehicles (IoVs)
		4.3 Intelligent Manufacturing
		4.4 Smart Home and City
	5 Challenges and Future Prediction
	References
Markov Model-Based Smart Home Assistance for Geriatric Care
	1 Introduction
	2 Related Works
	3 Proposed Model
		3.1 Hidden Markov Model
	4 Results and Discussion
	5 Conclusion
	References
State-of-the-Art and Emerging Trends in Internet of Things for Smart Cities
	1 Introduction
	2 The Creation of the Idea of Smart Cities
	3 A Smart City’s Features and Structures
	4 Architecture for Smart Cities
	5 The IoT and Problems of Smart City Design
		5.1 Huge Spatial-Temporal Urban Data Management, Convergence, and Release
		5.2 Design of Heterogeneous Sensor Information and IoT Emergence
		5.3 Large-Scale Control of Space-Time Information
		5.4 Mechanisms of Sound Intelligence Sharing and Legal Defense
	6 A Revolutionary Waste Management Scenario in a Smart City
	7 Conclusion
	References
Index