Adnan Mahmood – författare

The Internet of Vehicles (IoV) is referred to as an efficient and inevitable convergence of the Internet of Things, intelligent transportation systems, edge / fog and cloud computing, and big data, all of which could be intelligently harvested for the cooperative vehicular safety and non-safety applications as well as cooperative mobility management. A secure and low-latency communication is, therefore, indispensable to meet the stringent performance requirements of the safety-critical vehicular applications. Whilst the challenges surrounding low latency are being addressed by the researchers in both academia and industry, it is the security of an IoV network which is of paramount importance, as a single malicious message is perfectly capable enough of jeopardizing the entire networking infrastructure and can prove fatal for the vehicular passengers and the vulnerable pedestrians. This book thus investigates the promising notion of trust in a bid to strengthen the resilience of the IoV networks. It not only introduces trust categorically in the context of an IoV network, i.e., in terms of its fundamentals and salient characteristics, but further envisages state-of-the-art trust models and intelligent trust threshold mechanisms for segregating both malicious and non-malicious vehicles. Furthermore, open research challenges and recommendations for addressing the same are discussed in the same too.

The Internet of Vehicles (IoV) is referred to as an efficient and inevitable convergence of the Internet of Things, intelligent transportation systems, edge / fog and cloud computing, and big data, all of which could be intelligently harvested for the cooperative vehicular safety and non-safety applications as well as cooperative mobility management. A secure and low-latency communication is, therefore, indispensable to meet the stringent performance requirements of the safety-critical vehicular applications. Whilst the challenges surrounding low latency are being addressed by the researchers in both academia and industry, it is the security of an IoV network which is of paramount importance, as a single malicious message is perfectly capable enough of jeopardizing the entire networking infrastructure and can prove fatal for the vehicular passengers and the vulnerable pedestrians. This book thus investigates the promising notion of trust in a bid to strengthen the resilience of the IoV networks. It not only introduces trust categorically in the context of an IoV network, i.e., in terms of its fundamentals and salient characteristics, but further envisages state-of-the-art trust models and intelligent trust threshold mechanisms for segregating both malicious and non-malicious vehicles. Furthermore, open research challenges and recommendations for addressing the same are discussed in the same too.

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 book examines the uses and potential risks of location-based services (LBS) in the context of big data, with a focus on location privacy protection methods.The growth of the mobile Internet and the popularity of smart devices have spurred the development of LBS and related mobile applications. However, the misuse of sensitive location data could compromise the physical and communication security of associated devices and nodes, potentially leading to privacy breaches. This book explores the potential risks to the location privacy of mobile users in the context of big data applications. It discusses the latest methods and implications of location privacy from different perspectives. The author offers case studies of three applications: statistical disclosure and privacy protection of location-based big data using a centralized differential privacy model; a user location perturbation mechanism based on a localized differential privacy model; and terminal location perturbation using a geo-indistinguishability model. Linking recent developments in three-dimensional positioning and artificial intelligence, the book also predicts future trends and provides insights into research issues in location privacy.This title will be a valuable resource for researchers, students, and professionals interested in location-based services, privacy computing and protection, wireless network security, and big data security.

This book looks at conversational search in intelligent dialogue systems, as it investigates and addresses the challenges pertinent to effective context incorporation in conversational question answering (ConvQA). The authors explore the possibility of designing a scalable Conversational Question Answering Agent that can handle the challenges of incomplete/ambiguous questions, better able to relate to co-references to cope with the problems of effective weights and optimal threshold selection in vehicular networks. A fundamental emphasis is the understanding of ambiguous follow-up questions and the generation of contextual and question entities to fill in the missing information gaps. Key topics are studied, such as ‘hard history selection’ to filter out the context that is not relevant and performing a re-ranking of the selected turns based on their significance to answer the question as a part of the soft history selection process.This book aims to demonstrate that the history selection and modelling approaches proposed can effectively improve the performance of ConvQA models in different settings. The proposed models are compared with the state-of-the-art vis-à-vis different conversational datasets and provide new insights into conversational information retrieval. Through a systematic study of structured representations, entity-aware history selection, and open-domain passage retrieval using contrastive learning, this book presents a robust framework for advancing multi-turn QA systems.It is an essential resource for researchers, practitioners, and graduate students working at the intersection of NLP, dialogue systems, and intelligent information access.

This book, at first, explores the evolution of the IoT to SIoT and offers a comprehensive understanding of SIoT and trust management vis-à-vis SIoT.