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Integration of Federated Learning and Blockchain for Smart Cities

Edited by Krishna Kant Singh, Akansha Singh and Mahesh T.R.
Copyright: 2025 2025   |   Expected Pub Date:2025/07/30
ISBN: 9781394166459  |  Hardcover  |  
742 pages

One Line Description
Stay ahead of the curve in urban innovation with this essential guide that
provides a comprehensive roadmap for federated learning and blockchain
to build secure, intelligent, and efficient smart city ecosystems.

Audience
Academics, researchers, industry professionals, and policymakers working in the fields of artificial intelligence, machine learning, blockchain, IoT, cybersecurity, smart city planning, and urban technology development.

Description
As cities grow smarter, the demand for secure, decentralized, and privacy-preserving technologies is greater than ever. This book explores how federated learning and blockchain are transforming urban landscapes by enabling intelligent, secure, and efficient systems. By combining the power of decentralized machine learning with the transparency and security of blockchain, this book provides a roadmap for tackling challenges in urban mobility, energy management, public safety, and healthcare, delving into theoretical frameworks, architectural designs, security considerations, and real-world case studies to illustrate the impact of these technologies. This book serves as a comprehensive guide for researchers, industry professionals, and policymakers seeking to understand, implement, and innovate within smart city ecosystems.
Readers will find the volume:
• Explores the synergy between federated learning and blockchain, offering cutting-edge solutions for smart city challenges;
• Addresses critical issues of data privacy, decentralized AI, and secure digital infrastructure in urban environments;
• Features practical case studies on smart transportation, energy management, healthcare, and governance;
• Provides a forward-looking perspective on how emerging technologies will shape the cities of tomorrow.

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Author / Editor Details
Krishna Kant Singh, PhD is the Director at the Delhi Technical Campus, Greater Noida, India. He has authored 25 books and over 160 research papers in international journals. He is an associate editor of IEEE Transactions on Computational Social Systems, and Senior editor of IEEE Access. His research interests include machine vision, remote sensing, deep learning, and generative AI.

Akansha Singh, PhD is a professor in the School of Computer Science, Engineering, and Technology at Bennett University, Greater Noida, India, with over 18 years of teaching and research experience. She has published over 100 research papers and authored over 30 books in advanced areas of computer science. Her expertise spans image processing, deep learning, machine learning, remote sensing, and IoT, with a strong focus on AI-driven solutions for healthcare and environmental sustainability.

Mahesh T.R., PhD is the Program Head of the Department of Computer Science and Engineering in the School of Engineering and Technology, at Jain (Deemed-to-be University), Bengaluru, India. He has published over 180 research articles in international and edited several books. His research interests include image processing, machine learning, deep learning, artificial intelligence, IoT, and data science.

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Table of Contents
Preface
Part I: Introduction and Fundamentals
1. Unlocking the Potential of Smart Cities: A Study of the Internet of Things and Artificial Intelligence Integration
Anuradha Dhull, Tripti Sharma, Shilpa Mahajan and Akansha Singh
1.1 Introduction
1.2 The Rise of Smart Cities
1.3 Challenges of Urbanization
1.4 The Promise of AI and IoT
1.5 The Internet of Things (IoT) in Smart Cities
1.6 What is IoT
1.6.1 Components of an IoT Ecosystem
1.7 Applications of IoT in Smart Cities
1.7.1 Smart Infrastructure
1.7.2 Smart Transportation
1.7.3 Smart Energy Management
1.7.4 Public Safety and Security
1.8 Artificial Intelligence (AI) for Smart Cities
1.9 Types of AI Relevant to Smart Cities
1.9.1 Machine Learning
1.9.2 Deep Learning
1.9.3 Natural Language Processing (NLP)
1.10 Applications of AI in Smart Cities
1.10.1 Traffic Management and Optimization
1.10.2 Smart Parking Management
1.10.3 Smart Energy
1.10.4 Smart Pavement Management System
1.11 AI-Empowered IoT Security for Smart Cities
1.12 Secure Smart Cities Framework Using IoT and AI
1.13 IoT Paradigm into the Smart City Vision: A Survey
1.13.1 Smart Cities and the Internet of Things
1.14 Challenges Related to Implementing AI and IoT in Smart Cities
1.15 The Future of AI and IoT in Smart Cities
1.16 Conclusion
References
2. Cutting Edge Smart IoT Applications: Transforming Everyday Life
Anuradha Dhull, Anshita Gera, Monika Lamba and Akansha Singh
2.1 Introduction
2.2 Transition of Internet to IoT
2.3 The IoT Architecture
2.3.1 The Architectural Three-Layer Structure
2.3.2 The Five-Layer Architecture
2.3.3 Sensors and Actuators
2.4 Integration of IoT and Big Data Analytics
2.4.1 Relationship between IoT and Big Data Analytics
2.5 IoT Innovation: Emerging Trends and Applications—A Literature Review
2.6 Mainstream Use Cases for Emerging Smart Applications for IoT
2.7 IoT-Driven Intelligent Agricultural Applications
2.8 The Emergence of Internet of Things (IoT) Devices into Smart Grids
2.9 IoT Developments for Smart Home Usage
2.10 IoT’s Contribution to Industry 4.0 Adoption
2.11 IoT-Driven Smart Transportation/Vehicles
2.12 Utilizing IoT to Create Intelligent Energy Systems
2.13 AI Supported IoT Technologies in Developing Smart Libraries
2.14 IoT-Powered Wearable Biosensors with Nano-Integration
2.15 Innovations in IoT-Powered Smart Environment Monitoring Systems
2.15.1 Smart Water Pollution Monitoring (SWPM) Systems
2.15.2 Smart Air Quality Monitoring (SAQM) Systems
2.16 Conclusion
References
3. Federated Learning in Smart Cities
Suman Chahar and Kuldeep Kaswan
3.1 Introduction
3.1.1 Overview of Smart Cities
3.2 The Role of Machine Learning in Smart City Infrastructure
3.3 Federated Learning: Concept and Principles
3.3.1 Definition and Fundamentals of Federated Learning
3.3.2 Centralized Vs. Decentralized Machine Learning
3.4 Federated Learning Working
3.4.1 Loading of a Global Model
3.4.2 Implementation of the Global Model to Clients
3.4.3 Training on Site Using the Client’s Equipment
3.4.4 Updating of Local Model
3.4.5 Aggregation of Local Updates on the Central Server
3.4.6 Global Model Update
3.4.7 Iteration Process
3.4.8 Final Model Deployment
3.4.9 Applications of Federated Learning in Smart Cities
3.4.10 Smart Transport Systems
3.4.11 Smart Healthcare Systems
3.4.12 Smart Energy Management
3.4.13 Enhancing Security in Public Spaces
3.4.14 Architecture of Federated Learning Systems for Smart Cities
3.4.15 Key Components of Federated Learning Systems
3.4.16 Layers in Architecture of Federated Learning Systems for Smart Cities
3.5 Security and Privacy Considerations in Federated Learning for Smart Cities
3.5.1 Data Privacy Challenges in Smart Cities
3.5.2 Privacy-Preserving Mechanisms in Federated Learning
3.5.3 Differential Privacy and Secure Multi-Party Computation (SMPC)
3.5.3.1 Differential Privacy (DP)
3.5.3.2 Secure Multi-Party Computation (SMPC)
References
4. Blockchain Revolutionizing Tourism Supply Chain Management: Transparency, Traceability, and Security
Suresh N., Sundar Rajan S. and Anitha G.
4.1 Introduction
4.2 Blockchain in Tourism Supply Chain
4.3 Supply Chain Structure in the Tourism Industry
4.4 Enabling Framework for Blockchain in Tourism
4.5 Traceability of Tourism Products and Services
4.6 Challenges and Benefits
4.7 Authentication of Tourist Experiences
4.8 Blockchain for Trust Between Tourists and Service Providers
4.9 Smart Contracts for Contractual Agreements
4.9.1 Smart Contracts in the Tourism Supply Chain
4.10 Inventory and Asset Management
4.11 Data Security and Privacy
4.12 Ensuring Compliance while Leveraging Blockchain Technology
4.13 Integration with Existing Systems
4.14 Cost-Benefit Analysis
4.15 Conclusion
References
Part II: Core Technologies and Methodologies
5. Enhancing Threshold Cryptosystems with Blockchain Technology: A Cost-Effective and Scalable Approach Using Smart Contracts and ZkSNARKs
Rahul Raghavan Tharammal
5.1 Introduction
5.2 Background of Study
5.2.1 Shamir Secret Sharing Method
5.2.2 Threshold Cryptosystem
5.2.3 Zero-Knowledge Proof Method
5.3 Design Solution
5.4 Message Model
5.5 Gas Fees Considering Model
5.6 Smart Contract Model
5.7 User Involvement
5.8 Threshold Cryptosystem Protocol Using Smart Contracts and ZkSNARKs
5.9 Implementation of the Prototype: User Software and Smart Contract
5.9.1 Smart Contracts
5.9.2 User Software
5.10 Evaluation of the Protocol: Cost, Throughput, and Memory Usage
5.11 Gas Consumption
5.11.1 Gas Consumption Analysis
5.11.2 Performance
5.11.3 Performance Analysis
5.12 Memory Usage
5.12.1 Memory Usage Analysis
5.13 Related Work
5.14 Conclusion
References
6. Perspective of Blockchain, Federated Learning, Smart Cities, and Economy
Rahul Vadisetty
6.1 Introduction
6.2 Blockchain and Smart Cities
6.2.1 Blockchain in Healthcare of Smart Cities
6.2.2 Blockchain, Smart Cities, and Communities
6.3 Case Study
6.4 Federated Learning and Smart Cities
6.4.1 Sector-Wise Application of Blockchain in Smart Cities
6.4.2 Blockchain, Smart Cities, and Economy
References
7. Federal Learning Approach for Smart Cities
Rahul Vadisetty
7.1 Introduction
7.2 Federated Learning
7.2.1 Split Learning
7.2.2 Client Selection in Federated Learning
7.2.3 Some Aspects of Game Theory in Federal Learning
7.3 Federated Learning for Smart Cities
7.3.1 Federated Learning of Cyber Attack for Smart Cities
7.3.2 Federated Learning for Traffic of Smart Cities
7.4 Federated Learning of Urban Smart Cities
7.5 Federated Learning for Clients of Smart Cities
7.6 Federated Learning Applications, Challenges, and Solutions
7.6.1 Application of Federated Learning
7.6.2 Federated Learning Challenges
7.6.3 Federated Learning Solution
7.7 Conclusion
References
8. Federated Learning Applications in Retail, Finance, and Banking for Smart Cities
Madhuri Gupta, Prince Gupta and Sameer Malik
8.1 Introduction
8.1.1 Background and Overview
8.1.2 Scope and Objectives
8.2 Federated Learning Framework
8.2.1 Overview of Federated Learning
8.2.2 Supervised Machine Learning in Federated Learning
8.2.3 Federated Learning Architecture
8.3 Federated Learning Techniques
8.4 Application in Smart Cities
8.4.1 Applications in Retail
8.4.2 Applications in Finance
8.5 Applications in Banking
8.6 Traffic Management
8.6.1 Challenges and Solutions
8.6.2 Compliance Challenges
8.6.3 Privacy Concerns
8.6.4 Data Security Measures
8.7 Future Directions
8.8 Emerging Technologies
8.8.1 Potential Innovations
8.9 Conclusion
References
Part III: Integration of Technologies for Smart Cities
9. Leveraging Blockchain and Federated Learning for Smart Cities
Umesh Gupta, Gopal Singh Rawat, Jay Vardhan Singh and Akshat Jain
9.1 Introduction
9.1.1 Evolution and History of Blockchain
9.1.2 History and Evolution of Federated Learning
9.2 Challenges and Hurdles
9.2.1 Challenges and Hurdles in Blockchain
9.2.2 Challenges and Hurdles in Federated Learning
9.3 Future of Blockchain and Federated Learning for Smart Cities
9.3.1 Blockchain: Future
9.3.2 Federated Learning: Future
9.4 Impact of Federated Learning on Smart Cities
9.5 Collaboration of Federated Learning with Blockchain for Smart Cities
9.6 Conclusion and Future Scope
References
10. Integrating Blockchain and Federated Learning for Enhanced Security and Privacy in Smart Cities
Dipali Sarvate, Siddharth Shankar Mishra, V. Shanmugapriya and Dheerendra Panwar
10.1 Introduction
10.2 Background
10.3 Problem Statement
10.4 Need for a Blockchain–Federated Learning Approach
10.5 Proposed Framework
10.5.1 Architecture Overview
10.5.2 Technical Specifications
10.5.3 Workflow Diagram
10.6 Implementation Considerations
10.6.1 Scalability and Performance
10.6.2 Data Privacy and Security
10.6.3 Interfacing with Apps and Systems
10.6.4 The Compliance Paradigm of Regulatory and Ethical Compliance
10.6.5 Energy Consumption
10.6.6 Regulatory and Compliance Considerations
10.6.7 Economic and Logistical Implications for City Administrators
10.7 Potential Impacts
10.7.1 Increased Data Security and Privacy
10.7.2 Urban Governance, Efficiency, and Citizen Trust w.r.t. Broader Impacts
10.7.3 Assessment of the Framework Navigating Distinct Urban Contexts
10.8 Future Directions
10.8.1 Technological Trend Influencing
10.8.2 Integration with Emerging Technologies Like
10.8.3 Knowledge Gap and Direction for Future Research
10.9 Conclusion
References
11. Harnessing Federated Learning for Smart City Data Management in Cloud Environments
Naween Kumar, Akansha Singh, Vaibhav Saini, Ankit Dubey, Subham Sharma, Sasmita Pathy and Krishna Kant Singh
11.1 Introduction
11.2 Smart City Ecosystems and Data Management
11.2.1 IoT Devices
11.2.2 Public Services
11.2.3 Data Infrastructure
11.2.4 Environmental Data
11.2.5 Healthcare Data
11.2.6 Public Safety Data
11.3 Understanding Cloud Computing
11.3.1 Key Models of Cloud Computing
11.3.2 Benefits of Cloud Computing
11.4 Security Considerations
11.5 How Cloud Computing Works
11.5.1 Virtualization
11.5.2 Distributed Computing
11.5.3 Networking
11.5.4 Cloud Computing in Smart Cities
11.6 Transforming Urban Infrastructure: The Role of Cloud Computing in Smart City Data Management
11.7 Introduction to Federated Learning
11.8 Federated Learning in Smart City Data Management
11.9 Applications of Federated Learning in Smart City Use Cases
11.10 Privacy and Security Considerations
11.11 Synergy Between FL and Cloud Environments
11.12 Hybrid Model of Cloud and Edge Computing
11.12.1 Challenges of Integrating FL in Cloud Environments
11.12.2 Architectures for Implementing FL in Smart Cities
11.12.3 Considerations for Implementation
11.13 Decentralized Data Processing Framework
11.14 Cloud-Edge Orchestration in Smart Cities
11.15 Role of AI and ML in FL Architectures
11.16 Challenges in Federated Learning for Smart City Data
11.17 Use Case Examples
11.18 Future Trends in Federated Learning for Smart Cities
11.19 Advances in Cloud and Edge Computing
11.20 Future Trends
11.21 Conclusion
References
Part IV: Applications and Case Studies
12. Smart Environments—A Fusion of Technology and Context-Aware Systems
Ashima Narang, Poonam Sharma, Akansha Singh and Krishna Kant Singh
12.1 Introduction
12.2 Characteristics of Smart Environments
12.3 A Smart Environment’s Elements
12.4 Smart Home and Smart Environment Examples
12.5 Advantages of Intelligent Environments
12.6 Implementing Smart Environments Presents Difficulties
12.7 Working of Blockchain
12.8 Blockchain’s Principal Contributions
12.9 Consensus Algorithms Relevant to Smart Environments
12.9.1 Obstacles and Restrictions
12.10 Architecture of Blockchain-Enabled Smart Environments
12.10.1 Key Architectural Layers of Blockchain
12.11 Blockchain Applications in Intelligent Environments
12.12 Blockchain Design Limitations and Difficulties in Smart Environments
12.13 Case Studies of Smart Environments Powered by Blockchain: A Comparative Study
12.14 Conclusion and Future Scope
References
13. Federated Learning Applications for Urban Intelligence: A Holistic Examination in Retail, Finance, and Banking
Baskar Kasi, Saravanan Ramalingam, T. Sathish Kumar and A. Mohan
13.1 Introduction to Smart Cities and Federated Learning
13.1.1 Smart Cities Definition
13.2 Overview of Federated Learning
13.2.1 Federated Learning Definition
13.2.2 Core Principles of Federated Learning
13.2.3 Model Training without Raw Data Exchange
13.2.4 Decentralized Learning Process
13.2.5 Collaborative Model Updates
13.2.6 Privacy-Preserving Nature of Federated Learning
13.2.7 Decentralized Model Training
13.3 Significance of Federated Learning in the Smart Cities
13.3.1 Privacy Preservation in Smart Cities
13.3.2 Scalability and Efficiency in Smart Cities
13.4 Characteristics
13.5 Foundations of Federated Learning
13.5.1 Privacy-Preserving Nature
13.5.2 Decentralized Model Training
13.6 Federated Learning in Retail
13.6.1 Customer Behavior Analysis
13.6.2 Demand Forecasting
13.7 Inventory Management
13.7.1 Federated Learning in Retail: Minimizing Overstock and Stockouts
13.7.2 Federated Learning in Retail: Adaptive Pricing Strategies
13.8 Customer Experience Enhancement
13.9 Federated Learning in Finance
13.9.1 Credit Scores
13.9.2 Customer-Focused Personalization
13.10 Federated Learning in Banking
13.11 Challenges and Solutions of Federated Learning Implementation in Smart Cities
13.12 Trends and Prospects in Federated Learning for Smart Cities in the Future
13.13 Conclusion
References
14. Innovative Urban Data Processing: Federated Learning and Blockchain in Smart City Ecosystems
Naween Kumar, Akansha Singh, Subham Sharma, Ankit Dubey, Vaibhav Saini and Krishna Kant Singh
14.1 Introduction
14.2 Addressing Data Privacy and Security in Smart Cities
14.3 Federated Learning
14.3.1 Key Benefits
14.4 Blockchain
14.4.1 Key Benefits
14.5 Federated Learning and Blockchain: Concepts and Applications
14.5.1 Synergistic Integration
14.5.2 Data Security and Privacy
14.5.3 Decentralized Data Governance
14.6 Use Case Examples
14.7 Public Wi-Fi and Communication Architecture
14.8 Environmental Monitoring and Quality Control
14.8.1 Structural Health Monitoring (SHM) Sensors
14.8.2 Environmental Monitoring Sensors
14.8.3 Water Flow and Leak Detection Sensors
14.8.4 Traffic and Usage Monitoring
14.9 Federated Learning and Blockchain: Implementation Considerations
14.10 Cloud Computing as the Backbone
14.10.1 Role of Cloud in Smart Cities
14.10.2 Cloud for Scalable Data Processing
14.11 Integration of Cloud with Blockchain and Federated Learning
14.11.1 Model Aggregation in Federated Learning
14.11.2 Cloud-Based Blockchain Nodes
14.12 Synergy for Smart Cities
14.13 Beginner to Intermediate: Core Concepts and Workings
14.14 Frameworks for Federated Learning
14.14.1 Intermediate to Advanced: Models, Security, and Implementation
14.15 Federated Learning Implementation
14.16 Advanced Topics: Convergence and Optimization
References
15. Transforming Urban Landscapes: The Role of IoT and Drones in Smart City Development
Naween Kumar, Akansha Singh, Vaibhav Saini, Subham Sharma, Sahani Pooja Jaiprakash and Krishna Kant Singh
15.1 Introduction
15.2 What are Smart Cities and How Does IoT Contribute to Them
15.2.1 Traffic Management and Mobility
15.2.2 Air Quality and Environmental Monitoring
15.2.3 Weather Monitoring and Disaster Management
15.3 Energy Management and Sustainability
15.4 Urbanization Challenges and the Need for Smart Cities
15.5 The Role of IoT Drones in Smart Cities
15.5.1 Drones for Environmental Data Collection
15.6 Real-Time Data Transfer and Analysis
15.6.1 Cloud Computing and Edge Analytics for Efficient Data Processing
15.6.2 IoT and Drone Integration in Urban Environments
15.6.3 Environmental Sensors
15.6.4 Smart Grid Devices
15.6.5 Traffic Sensors
15.6.6 Public Safety Sensors
15.6.7 Smart Building Sensors
15.7 Drones: Aerial Applications in Urban Environments
15.8 The Growing Role of IoT in Urbanization
15.9 Current Needs and Gaps in Urban Data Collection and Management
15.10 Security in Smart City Operations
15.11 Challenges in Implementing IoT and Drones for Data Collection
15.12 Future Innovations in IoT and Drone-Driven Analytics
15.13 Conclusion
References
16. Next-Generation Urban Infrastructure: Leveraging Cloud and Edge Computing for Smart City Development
Naween Kumar, Akansha Singh, Sahani Pooja Jaiprakash, Vaibhav Saini, Subham Sharma and Krishna Kant Singh
16.1 Introduction
16.2 Edge Computing: Real-Time Responsiveness
16.3 Scalability with Cloud Computing: Supporting Growth and Future Demands
16.4 Flexibility with Edge Computing: Real-Time Local Processing and Adaptability
16.5 Combining Cloud Scalability with Edge Flexibility: A Cohesive Smart City Infrastructure
16.6 Enhanced Data Security and Privacy
16.7 Data Privacy at the Edge: Localized Processing to Mitigate Risk
16.8 Enhanced Security with Cloud Storage: Centralized Protection and Long-Term Security
16.9 The Combined Strength of Edge and Cloud: A Dual-Layered Approach to Data Privacy and Security
16.10 Key Technologies Enabling Cloud-Edge Convergence
16.10.1 12 IoT Devices and Sensors: Enabling Smart Cities through Data Generation and Edge Processing
16.11 Edge Processing: Enabling Real-Time Insights and Reducing Latency
16.12 Fog Computing: Enhancing Edge and Cloud Integration
16.13 Challenges in Implementing Cloud-Edge Convergence
16.14 Future Directions and Innovations in Cloud-Edge Convergence for Smart Cities
16.15 Future of Cloud-Edge Convergence in Urban Innovation
16.16 Conclusion
References
Part V: Governance and Societal Impacts
17. Blockchain, Governance, and Government for Smart Cities
Rahul Vadisetty
17.1 Introduction
17.2 Background
17.2.1 Smart Cities
17.2.2 Blockchain Technology
17.2.3 Governance
17.3 Blockchain and Governance for Smart Cities
17.3.1 Blockchain in Government Services
17.3.2 Blockchain in Decentralized Governance
17.4 Role of Blockchain in Governance
17.5 Blockchain-Based Smart Governance
17.6 Rise of e-Governance and Smart Cities e-Governance
17.7 Conclusion
References
18. Internet of Things and Artificial Intelligence in Smart Cities
Ashutosh Srivastava, Divya Srivastava, Madhushi Verma, Arpita Singh, Ishita Adhikari and Ayan Singh Rana
18.1 Introduction
18.2 IoT in Forming Smart Cities
18.3 Home Automation a Need in Smart Cities
18.4 Smart Cities Technologies
18.4.1 Inclusion of Smart Hotels in Smart Cities
18.4.2 The Intersection of AI and IoT in Smart Cities
18.4.3 Benefit of AI in IoT
18.4.4 AI-Enabled IoT
18.4.5 Challenges in Collaborating AI and IoT
18.5 Conclusion
References
19. Effectiveness of Education and Blockchain for Smart Cities
Rahul Vadisetty
19.1 Introduction
19.2 Background
19.2.1 Blockchain and Smart Cities
19.2.2 Education and Smart Cities
19.3 Education and Smart Cities
19.4 Methodology
19.4.1 Academic Research
19.5 Implementation
19.6 Conclusion
References
Part VI: Advanced Applications and Future Directions
20. Data Science and Big Data Analytics for Enhanced Urban Planning in Smart Cities
Naween Kumar, Akansha Singh, Vaibhav Saini, Ankit Dubey, Subham Sharma and Krishna Kant Singh
20.1 Introduction
20.2 The Role of Data Science and Big Data Analytics in Urban Planning
20.2.1 Optimizing Urban Infrastructure
20.2.2 Improving Resource Management and Sustainability
20.2.3 Enhancing Public Safety and Emergency Response
20.2.4 Improving Public Health and Quality of Life
20.2.5 Citizen Engagement and Smart Governance
20.3 Big Data in Smart City Environments
20.3.1 Enhanced Urban Planning and Infrastructure Development
20.3.2 Traffic Management and Transportation Optimization
20.3.3 Efficient Resource Management
20.3.4 Public Safety and Emergency Response
20.3.5 Environmental Monitoring and Sustainability
20.3.6 Public Health and Quality of Life
20.3.7 Enhanced Civic Engagement and Governance
20.3.8 Economic Development and Job Creation
20.4 Smart City Ecosystem and Future Technologies
20.4.1 Interoperability and Scalability
20.4.2 Innovation and Future Readiness
20.5 Data Collection and Processing in Smart Cities
20.6 Big Data Analytics Techniques for Urban Planning
20.6.1 Predictive Analytics for Population Growth and Density
20.6.2 Resource Optimization and Infrastructure Planning
20.6.3 Managing Urban Sprawl and Land Use
20.7 Sustainable Urban Planning and Sustainability
20.7.1 Spatial Data Analytics and Geographic Information Systems (GIS)
20.8 Integrated Approach for Smart Cities
20.9 Applications of Data Science in Smart City Urban Planning
20.9.1 Infrastructure Development and Optimization
20.9.2 Energy and Resource Management
20.9.3 Challenges in Data-Driven Urban Planning
20.9.4 Data Privacy and Ethical Concerns
20.9.5 Data Quality and Management
20.9.6 Resource Constraints and Technical Limitations
20.9.7 Mitigation Strategies
20.10 Future Directions in Data-Driven Urban Planning
20.10.1 Augmented and Virtual Reality for Urban Visualization
20.10.2 Artificial Intelligence for Predictive Urban Management
20.10.3 Geospatial Analytics and Remote Sensing for Environmental Planning
20.11 Conclusion
References
21. Enhancement of Smart Cities Through Blockchain
Anand Polamarasetti
21.1 Introduction
21.2 Blockchain and Smart Cities
21.2.1 Smart Cities
21.2.2 Blockchain and Smart Cities
21.2.3 Blockchain Solutions for Smart Mobility
21.2.4 Distributor Ledger Technology and Smart Cities
21.3 Implementation and Application of Implementation of Blockchain for Smart Cities
21.4 Application of Blockchain for Smart Cities
21.4.1 Tracking of Waste
21.4.2 Channelization
21.4.3 Efficiency
21.4.4 Non-Compliance
21.5 Conclusion
References
22. Federated Learning in Image Processing for Clothes Recognition
Madhuri Gupta, Harhsit Budhraja, Nipun Bhardwaj, Lakshit Agarwal, Ritvik Singh and Richa Chaturvedi
22.1 Introduction
22.2 The Need for Decentralization in Clothes Image Processing
22.3 Overview of Federated Learning
22.3.1 Federated Learning in Fashion: Use Cases
22.3.2 Relevance of Federated Learning in Fashion Industry
22.4 Image Processing Strategies for Recognition of Clothes
22.4.1 Image Preprocessing Techniques
22.4.2 Feature Extraction Techniques
22.5 Applications of Federated Learning in Clothes Image Processing
22.5.1 Classification of Apparel
22.5.2 Virtual Try-On Systems
22.5.3 Clothes Detection and Segmentation
22.5.4 Challenges in Federated Learning for Image Processing of Clothes
22.5.5 Data Heterogeneity
22.5.6 Privacy and Security
22.5.7 Computational Constraints
22.6 Privacy-Preserving and Secure Federated Learning Techniques
22.6.1 Differential Privacy
22.6.2 Secure Multiparty Computation (SMPC)
22.6.3 Homomorphic Encryption
22.7 Evaluation Metrics for Federated Learning in Clothes Image Processing
22.7.1 Classification Metrics
22.7.2 Communication Efficiency
22.7.3 Privacy and Security Metrics
22.8 Conclusion
References
23. Performance Analysis of Segmentation Techniques for Knee Osteoarthritis Detection from X-Ray Images
Shashikala H.K. and Suresh M.B.
23.1 Introduction
23.2 Osteoarthritis (OA): Types, Symptoms, and Causes
23.2.1 Types of Osteoarthritis
23.2.2 Symptoms of Osteoarthritis
23.2.3 Causes of Osteoarthritis
23.2.4 Kellgren-Lawrence (KL) Grading System and Severity Levels
23.2.5 Implications of KL Grading in Clinical Practice
23.3 Related Work
23.4 Methodology
23.5 Experimental Results
23.6 Conclusion
References
24. Blockchain for Smart Industry Management
Priya N. and Sudhagar Kalyanasundaram
24.1 Introduction
24.2 Blockchain Technology for the Smart Industry
24.3 Smart Industries Overview
24.4 Related Works
24.5 Challenges in Smart Industry Management
24.6 Blockchain Solutions for Smart Industry
24.7 Use Cases and Applications
24.8 Implementation Strategies in the Smart Industry Using Blockchain
24.9 Implementation Tools
24.10 Proposed Methodology
24.11 Results
24.12 Interoperability and Standards
24.13 Ensuring Compatibility with Existing Systems
24.14 Regulatory and Legal Considerations
24.14.1 Compliance Issues in Blockchain for the Smart Industry
24.14.2 Legal Frameworks and Regulations
24.15 Future Trends and Developments
24.16 Conclusion
References
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