With the rapid penetration of technology in varied application domains, the existing cities are getting connected more seamlessly. Cities becomes smart by inducing ICT in the classical city infrastructure for its management. According to McKenzie Report, about 68% of the world population will migrate towards urban settlements in near future. This migration is largely because of the improved Quality of Life (QoL) and livelihood in urban settlements. In the light of urbanization, climate change, democratic flaws, and rising urban welfare expenditures, smart cities have emerged as an important approach for society’s future development. Smart cities have achieved enhanced QoL by giving smart information to people regarding healthcare, transportation, smart parking, smart traffic structure, smart home, smart agronomy, community security etc. Typically, in smart cities data is sensed by the sensor devices and provided to end users for further use. The sensitive data is transferred with the help of internet creating higher chances for the adversaries to breach the data. Considering the privacy and security as the area of prime focus, this book covers the most prominent security vulnerabilities associated with varied application areas like healthcare, manufacturing, transportation, education and agriculture etc. Furthermore, the massive amount of data being generated through ubiquitous sensors placed across the smart cities needs to be handled in an effective, efficient, secured and privacy preserved manner. Since a typical smart city ecosystem is data driven, it is imperative to manage this data in an optimal manner. Enabling technologies like Internet of Things (IoT), Natural Language Processing (NLP), Blockchain Technology, Deep Learning, Machine Learning, Computer vision, Big Data Analytics, Next Generation Networks and Software Defined Networks (SDN) provide exemplary benefits if they are integrated in the classical city ecosystem in an effective manner. The application of Artificial Intelligence (AI) is expanding across many domains in the smart city, such as infrastructure, transportation, environmental protection, power and energy, privacy and security, governance, data management, healthcare, and more. AI has the potential to improve human health, prosperity, and happiness by reducing our reliance on manual labor and accelerating our progress in the sciences and technologies. NLP is an extensive domain of AI and is used in collaboration with machine learning and deep learning algorithms for clinical informatics and data processing. In modern smart cities, blockchain provides a complete framework that controls the city operations and ensures that they are managed as effectively as possible. Besides having an impact on our daily lives, it also facilitates many areas of city management.
Author(s): Mohd Abdul Ahad, Gabriella Casalino, Bharat Bhushan
Publisher: Springer
Year: 2023
Language: English
Pages: 408
City: Cham
Preface
Contents
List of Contributors
About the Editors
Chapter 1: Challenges and Opportunities in Secure Smart Cities for Enhancing the Security and Privacy
1.1 Introduction
1.2 Smart City
1.2.1 Basic Definitions
1.2.2 Components of Smart City
1.2.3 History of Smart City
1.2.4 Architecture of Smart City
1.2.5 Pillars of Smart City
1.2.6 Features of Smart City
1.2.7 Components of Smart City
1.2.8 Requirements of Smart City
1.2.9 Applications, Characteristics and Examples of Smart City
1.2.10 Why Smart Cities Are Popular and Its Benefits
1.3 Security Requirements
1.3.1 Authentication
1.3.2 Confidentiality
1.3.3 Integrity
1.3.4 Availability
1.3.5 Intrusion Detection
1.3.6 Privacy Protection
1.3.7 Access Control
1.3.8 Data Security
1.4 Security and Privacy Issues in Smart City
1.4.1 Threats to Smart City
1.4.2 Attacks on Smart City
1.4.3 Motivation for Attacks
1.4.4 Privacy Risks
1.5 Opportunities and Proposed Solutions
1.6 Conclusion
References
Chapter 2: Reliability and Security of Edge Computing Devices for Smart Cities
2.1 Introduction
2.2 IoT-Based Attacks
2.2.1 IoT-Security Challenges
2.2.2 IoT-Security Countermeasures
2.3 Analysis of Cyberattacks in Smart Cities
2.3.1 Security Vulnerabilities and Threats in Smart Cities
2.4 Security Threats to Edge Computing
2.5 Performance and Reliability Analysis
2.6 Edge Device Implementation in Autonomous Driving System
2.6.1 Security of Autonomous Driving System
2.6.2 Level of Robustness in Adversarial Attacks
2.6.3 Experimental Results
2.7 Conclusion
References
Chapter 3: Artificial Intelligence in Smart City-Systematic Literature Review of Current Knowledge and Future Research Avenues
3.1 Introduction
3.2 Background
3.2.1 Smart City Infrastructure
3.2.1.1 Physical Infrastructure
3.2.1.2 Institutional Infrastructure
3.2.1.3 Social Infrastructure
3.2.1.4 Economic Infrastructure
3.2.2 Smart City Layered Architecture
3.2.2.1 Sensing Components
3.2.2.2 Heterogeneous Networks
3.2.2.3 The Processing Unit
3.2.2.4 Control and Operating Components
3.2.2.5 Sustainability
3.2.2.6 Security
3.2.2.7 Connectivity
3.2.3 Decentralization
3.3 Research Methodology
3.4 Review Designing/Planning
3.4.1 Characterizing Study Selection
3.5 Data Extraction and Analysis
3.6 Data Execution
3.7 Thematic Analysis
3.7.1 Environmental Protection
3.7.1.1 Air Quality
3.7.1.2 Water Leakage
3.7.1.3 Environmental Intelligence
3.7.2 Healthcare
3.7.2.1 IoT Healthcare System
3.7.2.2 Corona Virus Outbreak and Smart City
3.7.2.3 Hospitals Equipped with Smart Systems
3.7.3 Transportation
3.7.3.1 Smart Mobility
3.7.3.2 Smart Parking
3.7.3.3 Autonomous/Electric Vehicle
3.7.3.4 License Plate Recognition
3.7.3.5 Traffic Management
3.7.4 Safety
3.7.4.1 Privacy
3.7.4.2 Security
3.7.5 Power and Energy
3.7.5.1 Intelligent Energy Optimization
3.7.5.2 Smart Monitoring
3.7.6 Data Management
3.7.6.1 Big Data Analytics
3.8 Conclusion
References
Chapter 4: Predictive Farmland Optimization and Crop Monitoring Using Artificial Intelligence Techniques
4.1 Introduction
4.2 Literature Review
4.3 Related Work
4.4 Proposed Model
4.4.1 Machine Learning Approach for Crop Prediction
4.4.1.1 Data Collection
4.4.1.2 Data Preparing and Pre-processing
4.4.1.3 Learning Algorithm
4.4.1.4 Training the Model
4.4.1.5 Evaluating the Model and Prediction
4.4.2 Disease Detection Prediction
4.4.2.1 Image Acquisition
4.4.2.2 Image Pre-processing
4.4.2.3 Feature Extraction
4.4.2.3.1 Convolutional Layer
4.4.2.3.2 Pooling Layer
4.4.2.4 Disease Classification
4.4.2.4.1 ReLU
4.4.2.4.2 Softmax
4.4.2.5 Image Validation
4.4.3 Artificial Neural Networks for Fertilizer Prediction
4.4.3.1 Data Collection
4.4.3.2 Data Pre-processing
4.4.3.3 Input Layer
4.4.3.4 Hidden Layer and Feature Extraction
4.4.3.5 Output Layer and Prediction
4.5 Result Analysis
4.5.1 Crop Prediction
4.5.2 Disease Classification
4.5.3 Fertilizer Prediction
4.6 Conclusions
References
Chapter 5: Natural Language Processing (NLP) Based Innovations for Smart Healthcare Applications in Healthcare 4.0
5.1 Introduction
5.2 Natural Language Processing: An Overview
5.2.1 What Is NLP?
5.2.2 Language Tasks in NLP
5.2.2.1 Information Extraction
5.2.2.2 Named Entity Recognition
5.2.2.3 Sentence Classification
5.2.2.4 Document Classification
5.2.2.5 Text Summarization
5.2.2.6 Question Answering
5.2.2.7 Machine Translation
5.2.3 NLP Pipeline
5.3 Healthcare 4.0
5.3.1 Smart Data Analytics Using Machine Learning
5.3.2 Medical Data Sources
5.4 Motivation
5.5 Smart Healthcare Applications of NLP
5.5.1 Healthcare Management and Administration
5.5.2 Assistive Care and Clinical DSS
5.5.3 Disease Diagnosis, Prediction, and Treatment
5.5.3.1 Mental Healthcare
5.5.3.2 Cancer Research
5.5.3.3 Circulatory System Disease
5.5.3.4 Miscellaneous
5.6 Conclusion
References
Chapter 6: Evolving of Smart Banking with NLP and Deep Learning
6.1 Introduction
6.1.1 Motivation
6.1.2 Objective
6.1.3 Contribution
6.2 Traditional Banking
6.3 Rise of FinTech and the New Era of Banking
6.3.1 Smart Banking
6.3.2 Data-Driven Fintech Industry and Literature Review
6.4 Technology: A Catalyst for Smart Banking
6.4.1 Natural Language Processing
6.4.1.1 The First Wave and Classicalism
6.4.1.2 The Second Wave and Rationalism
6.4.2 Deep Learning
6.4.3 Deep-NLP: A Revolution
6.5 Transitions from Now to Future
6.6 Application of Deep-NLP in Smart Banking
6.6.1 Application of NLP in Smart Banking
6.6.1.1 Chatbots
6.6.1.2 Auto Feedback and Offline Messaging System
6.6.1.3 Developing Self-Learning and Training Models
6.6.1.4 Detecting Phishing Attacks Using NLP
6.6.2 Application of Deep Learning in Smart Banking
6.6.2.1 Deep Learning in Marketing
6.6.2.2 Deep Learning in CRM
6.6.2.3 Deep Learning in RM
6.6.2.4 Deep Learning on Detecting Cyber Threats
6.6.2.5 Deep Learning on Real-Time Detection in Banks
6.7 Conclusion
References
Chapter 7: Blockchain Based Smart Card for Smart City
7.1 Introduction
7.2 Blockchain Overview
7.2.1 Characteristics of Blockchain System
7.2.2 Properties of a Block
7.2.3 Blockchain Classification
7.2.4 Consensus Algorithm
7.2.4.1 Details of PoW
7.2.4.2 Algorithmic Approach of PoS
7.2.4.3 DPoS: Proof of Stake
7.2.4.4 Development of the PBFT Protocol
7.2.5 Blockchain Architecture
7.2.5.1 Data Level of Blockchain
7.2.5.2 Components of the Blockchain Network
7.2.5.3 Consensus Layer
7.2.5.4 Process Inside Incentive Layer
7.2.5.5 Mechanism of Contract Layer
7.2.5.6 Network Insider: Application Layer
7.2.6 Transaction Phase
7.3 Industrial Adoption
7.3.1 Automation of Supply Chain Management
7.3.2 Security and It’s Privacy
7.3.3 Tracking of Product Manufacturing Phases
7.3.4 Payment Systems
7.4 Smart Card Usage in Smart Transportation
7.4.1 Benefits of Smart Transportation?
7.4.1.1 Sustainability
7.4.1.2 Livability
7.4.1.3 Workability
7.4.1.4 Increased Safety
7.4.1.5 Better Accessibility
7.5 Crypto Wallet with Smart Card
7.5.1 Security
7.5.2 Backup Wallet
7.5.3 Encrypt Online Backups
7.5.4 Use Many Secure Locations
7.5.5 Make Regular Backups
7.6 Crypto Wallet Features
7.6.1 Blockchain Wallet Types
7.7 Research Directions and Security
7.7.1 Integration
7.7.2 Resource Limitations
7.7.3 System Scalability
7.7.4 System Regulations
7.8 Conclusion
References
Chapter 8: Blockchain-Powered Smart E-Healthcare System: Benefits, Use Cases, and Future Research Directions
8.1 Introduction
8.2 Background and Application of Blockchain for E-Healthcare
8.2.1 Background of Blockchain
8.2.2 Blockchain Features for E-Healthcare
8.2.3 Categories of Blockchain
8.2.4 IoT and Blockchain for E-Healthcare
8.2.5 Blockchain Consensus Algorithms in E-Healthcare
8.2.5.1 Proof of Work
8.2.5.2 Proof of Stake
8.2.5.3 Delegated Proof of Stake
8.2.5.4 Leased Proof of Stake
8.2.5.5 Proof of Importance
8.2.5.6 Practical Byzantine Fault Tolerance
8.2.5.7 Delegated Byzantine Fault Tolerance
8.2.5.8 Proof of Capacity
8.2.5.9 Proof of Activity
8.2.5.10 Proof of Burn
8.3 Benefit of Blockchain Technology in E-Healthcare Record Administration
8.3.1 Health Records Exactness
8.3.2 Health Records Interoperability
8.3.3 Health Records Safety
8.3.4 Health Data Managing Costs
8.3.5 Worldwide Health Records Distribution
8.3.6 Enhanced Healthcare Records Audit
8.4 Use Cases of E-Healthcare System
8.4.1 Medical Record Access
8.4.2 EHR Claim and Billing Valuation
8.4.3 Medical Research
8.4.4 Drug Supply Chain Administration
8.4.5 IoBHealth
8.5 Recent Case Studies
8.5.1 Estonian E-Healthcare System
8.5.2 Healthcare and Pharma Data in UAE Using Blockchain
8.5.3 Medical Device Tracking in Swiss Hospitals by Permission Blockchain
8.6 Future Research Directions
8.6.1 AI and Data Analytics in Combination with Blockchain
8.6.2 Parallel Blockchain-Based Healthcare Organizations
8.6.3 Cloud Computing in Combination with Blockchain
8.6.4 Healthcare Regulations and Standardization Based on Blockchain
8.6.5 Development of E-Healthcare System Considering Blockchain
8.7 Conclusion
References
Chapter 9: A Comprehensive Review of Wireless Medical Biosensor Networks in Connected Healthcare Applications
9.1 Introduction
9.2 Wireless Body Sensor Networks (WBSNs)
9.3 The Architecture of WBSN
9.4 Biosensor
9.5 WBSN Applications
9.6 Main Challenges of WBSNs
9.7 Early Warning Score
9.8 Data Gathering
9.9 Data Fusion
9.10 Telemedicine and Remote Patient Monitoring
9.11 Decision Making
9.12 Energy Consumption
9.13 Machine Learning Applications in Smart Cities
9.13.1 Convolutional Neural Network (CNN)
9.13.2 The RNN (Recurrent Neural Network)
9.13.3 The DRL (Deep Reinforcement Learning)
9.13.4 Support Vector Machine
9.14 Conclusions
References
Chapter 10: Smart Intelligent System for Cervix Cancer Image Classification Using Google Cloud Platform
10.1 Introduction
10.2 Related Works
10.2.1 Smart Healthcare Using the Internet of Things (IoT)
10.2.2 Smart Healthcare Using Machine Learning
10.2.3 Smart Healthcare Using Vision AI
10.2.4 Smart Healthcare Using GCP
10.2.5 Clinical Methods and Practices for Cervical Cancer Under a Smart Healthcare System
10.2.5.1 Visual Inspection
10.2.5.2 Cytology Based Screening
10.2.5.2.1 Pap Test with Conventional Cytology
10.2.5.2.2 Pap Test Using Liquid-Based Cytology
10.2.5.2.3 Automated Pap Smear
10.2.5.3 Digital Screening
10.2.5.3.1 Digital Colposcopy
10.2.5.3.2 Smart Colposcopy
10.3 Proposed Methodology
10.3.1 Mathematical Background
10.3.2 Workflow of AutoML Model
10.4 Experimental Observations
10.4.1 Model Evaluation
10.4.2 Model Deployment
10.4.3 Model Testing
10.5 Conclusion and Future Works
References
Chapter 11: IoT and an Intelligent Cloud-Based Framework to Build a Smart City Traffic Management System
11.1 Introduction
11.1.1 Problem Statements
11.2 Related Works
11.3 Proposed System
11.3.1 Layer for Data Capture and Gathering
11.3.2 The Layer of Selection and Data Analysis
11.3.3 The Layer of Actuation and Utilization
11.3.4 Component Description of Proposed System
11.3.4.1 HC-SR04 Ultrasonic Sensors
11.3.4.2 Arduino Mega 2560 Controller
11.3.4.3 RFID Sensor Module
11.3.4.4 ESP8266 Wi-Fi Module
11.3.4.5 LED Signal
11.3.4.6 IR Sensor
11.3.5 Software
11.4 Mathematical Modelling of Systems
11.5 Results and Discussion
11.5.1 Different Lanes and Signal View
11.5.2 Sequence Diagram of Signal Control System (Fig. 11.9)
11.6 Conclusion
References
Chapter 12: Emersion and Immersion of Technology in the Development of Smart Cities: A Bibliometric Analysis
12.1 Introduction
12.2 Literature Review
12.2.1 Smart Cities Concepts
12.3 Data Collection and Research Methodology
12.4 Analysis
12.4.1 Description of Literature Published Globally
12.4.2 Scientific Annual Production
12.4.3 Bradford’s Law Analysis
12.4.4 Core Article Analysis Globally
12.4.5 Thematic Analysis Using Thematic Map and Word Cloud
12.4.6 Word Cloud
12.5 Conclusion
12.6 Limitations of the Study
References
Chapter 13: Examining Social Media, Citizen Engagement and Risk Communication: A Smart City Perspective
13.1 Introduction
13.2 Literature Review
13.2.1 Smart City: Characteristics and Pillars
13.2.2 Online Engagement and Risk Management
13.2.3 Government Social Media and COVID-19
13.3 Methodology and Data
13.3.1 Data Collection
13.3.2 Inter-Coder Reliability and Data Analysis
13.3.3 Coding
13.4 Results and Discussion
13.5 Conclusion
References
Chapter 14: 5G and 6G Technologies for Smart City
14.1 Introduction
14.2 The Evolution of Mobile Communications
14.2.1 0 Generation
14.2.2 1 Generation
14.2.3 2 Generation
14.2.4 3 Generation
14.2.5 4 Generation
14.2.6 5 Generation
14.2.7 6 Generation
14.3 Fifth-Generation Technology
14.3.1 Characteristics of the 5G Communication Technology
14.3.1.1 Improved Network Efficiency
14.3.1.2 Adaptive Network Operations
14.3.1.3 The Flexibility of Network Functions Is Greater
14.3.1.4 Network Ecology Is More Environmentally Friendly
14.3.1.5 Comparison of Reliabilities
14.3.1.6 Expense Comparison
14.3.1.7 Comparative Analysis of Safety
14.4 Sixth Generation Technology
14.5 Emerging Technologies of 5G and 6G
14.5.1 Brain Computer Interface
14.5.2 Artificial Intelligence -AI
14.5.3 The Internet of Everything in Industry (IIoE)
14.5.4 Block Chain
14.5.5 Extended Reality (XR)
14.5.6 Wireless Communication with Tera-Hertz Support
14.6 5G: Smart Cities’ Technology Enabler
14.6.1 Drivers of Smart City Demand
14.6.1.1 Drivers of City Governance
14.6.1.2 Residents’ Drivers with Inside the City
14.6.1.3 Drivers of the City Business
14.6.2 5G: Enabler of Smart City Technologies
14.6.2.1 Device Interoperability
14.6.2.2 Ultra-Low Latency
14.6.2.3 5G’s ‘constantly on’ connectivity
14.6.2.4 Energy Conservation
14.7 5G Enables Smart City Services and Applications
14.7.1 Smart Homes Residents
14.7.2 Smart Education
14.7.3 Smart Health
14.7.4 Smart Transportation Systems
14.7.5 Surveillance Systems and Smart Safety
14.7.6 Smart Power
14.8 Smart Cities and the Economic Implications of 5G
14.9 Vision 6G
14.9.1 Intelligent Personal Edge
14.9.2 Sensor to AI Fusion
14.9.3 Super-Functional Components
14.9.4 Smart Materials
14.9.5 Mobility as each a Service
14.9.6 Smart City Services
14.9.7 Personalized Surfaces
14.9.8 Multi-object Monitoring
14.9.9 Bio-Cybernetic Identity
14.9.10 Autonomous Port
14.9.11 Smart displays
14.10 Challenges
14.11 Conclusions
References
Chapter 15: Software Defined Virtual Clustering-Based Content Distribution Mechanism in VNDN
15.1 Introduction
15.1.1 Data Dissemination in SDV
15.1.2 Contributions
15.1.3 Chapter Organization
15.2 Literature Review
15.2.1 Named Data Networking
15.2.2 Vehicular Named Data Networking
15.2.3 Software Defined Network
15.2.4 Clustering in V-NDN
15.3 System Model
15.4 Network Model
15.5 Implementation and Result
15.6 Conclusion
References
Chapter 16: Sustainable Energy Usage in Urban and Rural Context-A Study
16.1 Introduction
16.2 Literature Review
16.3 Criteria in Energy Planning for Sustainable Development
16.4 “Internet of Energy” Technology for Energy Management
16.5 Challenges in Energy Management for Sustainability
16.6 Conclusion and Future Scope
References
