Deep Learning and Blockchain Technology for Smart and Sustainable Cities
1st Edition
Published on 22. May 2025
381 pages
978-1-040-34626-6 (ISBN)
Description
In this digital era, a smart city can become an intelligent society by using advances in emerging technologies. Specifically, the rapid adoption of deep learning (DL) in fusion with blockchain technology has led to a new digital smart city ecosystem. A broad spectrum of DL and blockchain applications promises solutions for problems in areas ranging from risk management and financial services to cryptocurrency to public and social services. Furthermore, the convergence of artificial intelligence and blockchain technology is revolutionizing the smart city network architecture to build sustainable ecosystems. However, these advances in technology bring both opportunities and challenges in creating sustainable smart cities.
To help planners and developers to meet these challenges and exploit these opportunities, Deep Learning and Blockchain Technology for Smart and Sustainable Cities takes a deep dive into the technologies and applications that enable smart and sustainable cities. It provides a comprehensive literature review of the security issues and problems that impact the deployment of blockchain systems in smart cities. It presents a detailed discussion of key factors in the convergence of blockchain and DL technologies that help form sustainable smart societies. This book also discusses blockchain security enhancement solutions and summarizes main key points necessary for developing various blockchain and DL-based intelligent transportation systems.
This book concludes with a discussion of open issues and future research direction.These include new security suggestions and guidelines for a sustainable smart city ecosystem. Also discussed is 6G-enabled DL and blockchain in real-time applications.
To help planners and developers to meet these challenges and exploit these opportunities, Deep Learning and Blockchain Technology for Smart and Sustainable Cities takes a deep dive into the technologies and applications that enable smart and sustainable cities. It provides a comprehensive literature review of the security issues and problems that impact the deployment of blockchain systems in smart cities. It presents a detailed discussion of key factors in the convergence of blockchain and DL technologies that help form sustainable smart societies. This book also discusses blockchain security enhancement solutions and summarizes main key points necessary for developing various blockchain and DL-based intelligent transportation systems.
This book concludes with a discussion of open issues and future research direction.These include new security suggestions and guidelines for a sustainable smart city ecosystem. Also discussed is 6G-enabled DL and blockchain in real-time applications.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
College/higher education
Illustrations
112 Line drawings, black and white; 112 Illustrations, black and white
File size
13,96 MB
ISBN-13
978-1-040-34626-6 (9781040346266)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Subramaniyaswamy V | G. Revathy | Logesh Ravi
Deep Learning and Blockchain Technology for Smart and Sustainable Cities
Book
05/2025
1st Edition
Auerbach
€263.10
Shipment within 10-20 days
Persons
Dr. V. Subramaniyaswamy is a professor at the School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.
Dr. G Revathy is an assistant professor, Department of CSE, Srinivasa Ramanujan Centre, SASTRA (Deemed University), Thanjavur, India.
Dr. Logesh Ravi is an assistant professor at the Centre for Advanced Data Science (CADS), Vellore Institute of Technology, Chennai, India.
Dr. N. Thillaiarasu is an associate professor at the School of Computing and Information Technology, REVA University, Bengaluru, India.
Dr. Naresh Kshetri is faculty member, Department of Cybersecurity at the Rochester Institute of Technology, Rochester, New York, USA.
Dr. G Revathy is an assistant professor, Department of CSE, Srinivasa Ramanujan Centre, SASTRA (Deemed University), Thanjavur, India.
Dr. Logesh Ravi is an assistant professor at the Centre for Advanced Data Science (CADS), Vellore Institute of Technology, Chennai, India.
Dr. N. Thillaiarasu is an associate professor at the School of Computing and Information Technology, REVA University, Bengaluru, India.
Dr. Naresh Kshetri is faculty member, Department of Cybersecurity at the Rochester Institute of Technology, Rochester, New York, USA.
Content
1. Applications of Smart City 2. Assessing the Efficiency of Integrated RBSA and SBFS Approaches Utilizing Machine Learning Techniques for Email Categorization in Smart City Infrastructure 3. Unleashing the Power of Data and Security by Integrating Deep Learning and Blockchain in Smart City Infrastructure Development - A Future Perspective 4. An Extensive Analysis of Blockchain Technology Addressing Many Facets of IoT, Smart Cities, and Deep Learning 5. Exploring the Potential of Blockchain and Deep Learning in Smart City Applications 6. Enabling Smart Cities: A Comprehensive Study of IoT and IIoT Integration in Diverse Industries 7. Smart City Applications Using Blockchain Technologies 8. Insights into Tomorrow: A Review of Rainfall Intelligence Integration in Smart City Innovations for Future Urban Development 9. IIOT for Smart Cities 10. Enhancing Smart City Sustainability through Multiple Coal Classification Utilizing Deep Learning. 11. Preventive Care: Smart Strategies for Elderly Fall Detection and Prevention 12. smartVAST: Vulnerability, Attacks, Security, and Threats on Smart Cities with Impact and Ability in Blockchain Technology 13. Empowering Urban Planning with Accurate Air Quality Index Prediction: Hybrid Learning Models for Smart Cities 14. Enhancing Healthcare in Smart Cities: The Fusion of MCPS and IoT 15. Efficiency Analysis of LSTM-Based Earthquake Forecasting for Smart City Resilience: A Time Complexity Perspective 16. Smart Farming: Empowering Agriculture in Smart Cities 17. Anomaly Detection of Parkinson Diseases with Voice Analysis Patterns Using ML Algorithms in Smart Cities 18. Location and Weather-Based Prediction of Yields Using Machine Learning Algorithms in Smart Cities 19. Gesture Language Translator in Smart Cities 20. Blockchain-Driven AI Framework for Early Human Monkeypox Detection
