Wei Ni – författare

Metal-Organic Framework Nanocomposites: From Design to Application assembles the latest advances in MOF nanocomposites, emphasizing their design, characterization, manufacturing, and application and offering a wide-ranging view of these materials with exceptional physical and chemical properties.

FEATURES

Aimed at researchers, academics, and advanced students in materials science and engineering, this book offers a comprehensive overview and analysis of these extraordinary materials.

Metal-Organic Framework Nanocomposites: From Design to Application assembles the latest advances in MOF nanocomposites, emphasizing their design, characterization, manufacturing, and application and offering a wide-ranging view of these materials with exceptional physical and chemical properties.

FEATURES

Aimed at researchers, academics, and advanced students in materials science and engineering, this book offers a comprehensive overview and analysis of these extraordinary materials.

This book delves into the critical realm of trust management within the Internet of Vehicles (IOV) networks, exploring its multifaceted implications on safety and security which forms part of the intelligent transportation system domain.

IoV emerges as a powerful convergence, seamlessly amalgamating the Internet of Things (IoT) and the intelligent transportation systems (ITS). This is crucial not only for safety-critical applications but is also an indispensable resource for non-safety applications and efficient traffic flows. While this paradigm holds numerous advantages, the existence of malicious entities and the potential spread of harmful information within the network not only impairs its performance but also presents a danger to both passengers and pedestrians. Exploring the complexities arising from dynamicity and malicious actors, this book focuses primarily on modern trust management models designed to pinpoint and eradicate threats. This includes tackling the challenges regarding the quantification of trust attributes, corresponding weights of these attributes, and misbehavior detection threshold definition within the dynamic and distributed IoV environment.

This will serve as an essential guide for industry professionals and researchers working in the areas of automotive systems and transportation networks. Additionally, it will also be useful as a supplementary text for students enrolled in courses covering cybersecurity, communication networks, and human factors in transportation.

Sarah Ali Siddiqui is a CSIRO Early Research Career (CERC) Fellow in the Cyber Security Automation and Orchestration Team, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Adnan Mahmood is a Lecturer in Computing – IoT and Networking at the School of Computing, Macquarie University, Sydney, Australia.

Quan Z. (Michael) Sheng is a Distinguished Professor and Head of the School of Computing, at Macquarie University, Sydney, Australia.

Hajime Suzuki is a Principal Research Scientist at the Cybersecurity & Quantum Systems Group, Software and Computational Systems Research Program, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Wei Ni is a Principal Scientist at the Commonwealth Scientific and Industrial Research Organisation, a Technical Expert at Standards Australia, a Conjoint Pro-fessor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University, Sydney, Australia.

This book delves into the critical realm of trust management within the Internet of Vehicles (IOV) networks, exploring its multifaceted implications on safety and security which forms part of the intelligent transportation system domain.

IoV emerges as a powerful convergence, seamlessly amalgamating the Internet of Things (IoT) and the intelligent transportation systems (ITS). This is crucial not only for safety-critical applications but is also an indispensable resource for non-safety applications and efficient traffic flows. While this paradigm holds numerous advantages, the existence of malicious entities and the potential spread of harmful information within the network not only impairs its performance but also presents a danger to both passengers and pedestrians. Exploring the complexities arising from dynamicity and malicious actors, this book focuses primarily on modern trust management models designed to pinpoint and eradicate threats. This includes tackling the challenges regarding the quantification of trust attributes, corresponding weights of these attributes, and misbehavior detection threshold definition within the dynamic and distributed IoV environment.

This will serve as an essential guide for industry professionals and researchers working in the areas of automotive systems and transportation networks. Additionally, it will also be useful as a supplementary text for students enrolled in courses covering cybersecurity, communication networks, and human factors in transportation.

Sarah Ali Siddiqui is a CSIRO Early Research Career (CERC) Fellow in the Cyber Security Automation and Orchestration Team, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Adnan Mahmood is a Lecturer in Computing – IoT and Networking at the School of Computing, Macquarie University, Sydney, Australia.

Quan Z. (Michael) Sheng is a Distinguished Professor and Head of the School of Computing, at Macquarie University, Sydney, Australia.

Hajime Suzuki is a Principal Research Scientist at the Cybersecurity & Quantum Systems Group, Software and Computational Systems Research Program, Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia.

Wei Ni is a Principal Scientist at the Commonwealth Scientific and Industrial Research Organisation, a Technical Expert at Standards Australia, a Conjoint Pro-fessor at the University of New South Wales, an Adjunct Professor at the University of Technology Sydney, and an Honorary Professor at Macquarie University, Sydney, Australia.

This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.

It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning.

This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.

This comprehensive guide addresses key challenges at the intersection of data science, graph learning, and privacy preservation.

It begins with foundational graph theory, covering essential definitions, concepts, and various types of graphs. The book bridges the gap between theory and application, equipping readers with the skills to translate theoretical knowledge into actionable solutions for complex problems. It includes practical insights into brain network analysis and the dynamics of COVID-19 spread. The guide provides a solid understanding of graphs by exploring different graph representations and the latest advancements in graph learning techniques. It focuses on diverse graph signals and offers a detailed review of state-of-the-art methodologies for analyzing these signals. A major emphasis is placed on privacy preservation, with comprehensive discussions on safeguarding sensitive information within graph structures. The book also looks forward, offering insights into emerging trends, potential challenges, and the evolving landscape of privacy-preserving graph learning.

This resource is a valuable reference for advance undergraduate and postgraduate students in courses related to Network Analysis, Privacy and Security in Data Analytics, and Graph Theory and Applications in Healthcare.

In an era where vehicular networks and Location-Based Services (LBS) are rapidly expanding, safeguarding location privacy has become a critical challenge. Privacy in Vehicular Networks delves into the complexities of protecting sensitive location data within the dynamic and decentralized environment of vehicular networks. This book stands out by addressing both the theoretical and practical aspects of location privacy, offering a thorough analysis of existing vulnerabilities and innovative solutions.

This book meticulously examines the interplay between location privacy and the operational necessities of road networks. It introduces a differential privacy framework tailored specifically for vehicular environments, ensuring robust protection against various types of privacy breaches. By integrating advanced detection algorithms and personalized obfuscation schemes, the book provides a multi-faceted approach to enhancing location privacy without compromising data utility.

The key features of this book can be summarized as follows:

This book is essential for researchers, practitioners, and policymakers in the fields of vehicular networks, data privacy, and cybersecurity. It provides valuable insights for anyone involved in the development and implementation of LBS, ensuring they are equipped with the knowledge to protect user privacy effectively.

In an era where vehicular networks and Location-Based Services (LBS) are rapidly expanding, safeguarding location privacy has become a critical challenge. Privacy in Vehicular Networks delves into the complexities of protecting sensitive location data within the dynamic and decentralized environment of vehicular networks. This book stands out by addressing both the theoretical and practical aspects of location privacy, offering a thorough analysis of existing vulnerabilities and innovative solutions.

This book meticulously examines the interplay between location privacy and the operational necessities of road networks. It introduces a differential privacy framework tailored specifically for vehicular environments, ensuring robust protection against various types of privacy breaches. By integrating advanced detection algorithms and personalized obfuscation schemes, the book provides a multi-faceted approach to enhancing location privacy without compromising data utility.

The key features of this book can be summarized as follows:

This book is essential for researchers, practitioners, and policymakers in the fields of vehicular networks, data privacy, and cybersecurity. It provides valuable insights for anyone involved in the development and implementation of LBS, ensuring they are equipped with the knowledge to protect user privacy effectively.