Federated Learning Based Intelligent Systems To Handle Issues And Challenges In Iovs Part 2
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Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 2)
Author: Shelly Gupta, Puneet Garg, Jyoti Agarwal, Hardeo Kumar Thakur, Satya Prakash Yadav
language: en
Publisher: Bentham Science Publishers
Release Date: 2025-04-25
Federated Learning for Internet of Vehicles: IoV Image Processing, Vision, and Intelligent Systems (Volume 3) explores how federated learning is revolutionizing the Internet of Vehicles (IoV) by enabling secure, decentralized, and scalable solutions. Combining theoretical insights with practical applications, this book addresses key challenges such as data privacy, heterogeneous information, and network latency in IoV systems. This volume offers cutting-edge strategies to build intelligent, resilient vehicular systems, from privacy-enhanced data collection to blockchain-based payments, smart transportation systems, and vehicle number plate recognition. It highlights how federated learning drives advancements in secure data sharing, identity-based authentication, and real-time road safety improvements. Key Features: - In-depth exploration of federated learning applications in IoV. - Solutions for privacy, security, and scalability challenges. - Practical examples of blockchain integration and smart systems. - Insights into future research directions for IoV.
Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 1)
Author: Shelly Gupta, Puneet Garg, Jyoti Agarwal, Hardeo Kumar Thakur, Satya Prakash Yadav
language: en
Publisher: Bentham Science Publishers
Release Date: 2024-12-13
Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 1) examines how federated learning can address key challenges within the Internet of Vehicles, from data security to routing efficiency. This volume explores how federated learning, a decentralized approach to machine learning, enables secure and adaptive IoV systems that enhance road safety, optimize traffic flow, and support reliable data sharing. Chapters cover essential topics, including technologies to address IoV routing issues, secure data exchange using blockchain, privacy-preserving methods, and NLP applications for vehicle safety. By combining theoretical insights with practical solutions, the book highlights how federated learning fosters scalable, resilient IoV systems that respond dynamically to the demands of connected vehicles. Key Features: - Addresses data privacy, secure communication, and adaptive solutions in IoV - Explores federated learning applications in real-time IoV systems - Combines practical examples with theoretical foundations in IoV technology - Includes emerging research areas in IoV federated learning frameworks
Driving Innovation at the Intersection of Renewable Energy and the Internet of Vehicles
The Internet of Vehicles (IoV) is revolutionizing transportation by enabling smarter, more connected mobility solutions in urban environments. However, the rapid expansion of connected vehicles and infrastructure brings significant energy demands that challenge sustainability goals. Addressing these concerns through green IoV strategies is essential to reduce the environmental impact of modern transportation systems. Achieving energy efficiency in IoV not only helps mitigate fuel and electricity consumption but also ensures long-term viability of smart city technologies. As cities continue to adopt intelligent transport networks, sustainable energy practices in vehicular systems become critical to balancing innovation with environmental responsibility. Driving Innovation at the Intersection of Renewable Energy and the Internet of Vehicles explores the innovative fusion of renewable energy sources with the IoV, driving the transformation toward eco-friendly and energy-efficient transportation systems. It delves into the integration of green technologies like solar, wind, and energy-efficient communications to reduce the environmental impact of vehicular networks. Covering topics such as artificial intelligence, machine learning, and sustainability, this book is an excellent resource for academicians, researchers, engineers, policymakers, and more.