Integration of Federated Learning and Blockchain for Smart Cities.

Format:

Book

Author/Creator:

Singh, Krishna Kant.

Contributor:

Singh, Akansha.

T. R., Mahesh.

Language:

English

Subjects (All):

Smart cities--Technological innovations.

Smart cities.

Physical Description:

1 online resource (744 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2025.

Summary:

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.As cities grow smarter, the demand for secure, decentralized, and privacy-preserving technologies is greater than ever.

Contents:

Cover

Series Page

Title Page

Copyright Page

Contents

Preface

Part I: Introduction and Fundamentals

Chapter 1 Unlocking the Potential of Smart Cities: A Study of the Internet of Things and Artificial Intelligence Integration

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

Chapter 2 Cutting Edge Smart IoT Applications: Transforming Everyday Life

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

Chapter 3 Federated Learning in Smart Cities

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)

Chapter 4 Blockchain Revolutionizing Tourism Supply Chain Management: Transparency, Traceability, and Security

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

Part II: Core Technologies and Methodologies

Chapter 5 Enhancing Threshold Cryptosystems with Blockchain Technology: A Cost-Effective and Scalable Approach Using Smart Contracts and ZkSNARKs

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

Chapter 6 Perspective of Blockchain, Federated Learning, Smart Cities, and Economy

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

Chapter 7 Federal Learning Approach for Smart Cities

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

Chapter 8 Federated Learning Applications in Retail, Finance, and Banking for Smart Cities

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

Part III: Integration of Technologies for Smart Cities

Chapter 9 Leveraging Blockchain and Federated Learning for Smart Cities

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

Chapter 10 Integrating Blockchain and Federated Learning for Enhanced Security and Privacy in Smart Cities

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.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-16776-8

1-394-16774-1

9781394167746

OCLC:

1543513584