http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Integration of federated learning and blockchain for smart cities
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.
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 -- References -- 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
edited by Krishna Kant Singh, Akansha Singh, and Mahesh T.R.