Human Digital Twin (eBook, PDF) - Okegbile, Samuel D.; Cai, Jun; Yi, Changyan
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Human digital twin (HDT) is a transformative technology poised to transform various human-centric systems. With its potential applications in personalized healthcare, HDT promises swift, precise, and efficient healthcare services through the integration of cutting-edge technologies such as artificial intelligence, data analytics, internet of things, cybersecurity as well as virtual and augmented reality. This book addresses the critical gap in the existing literature by offering a comprehensive exploration of connectivity solutions for HDT while prioritizing reliability, security, and privacy.…mehr

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Produktbeschreibung
Human digital twin (HDT) is a transformative technology poised to transform various human-centric systems. With its potential applications in personalized healthcare, HDT promises swift, precise, and efficient healthcare services through the integration of cutting-edge technologies such as artificial intelligence, data analytics, internet of things, cybersecurity as well as virtual and augmented reality. This book addresses the critical gap in the existing literature by offering a comprehensive exploration of connectivity solutions for HDT while prioritizing reliability, security, and privacy. Key topics covered include the foundational concept of HDT, its design requirements and associated challenges, edge-assisted human-to-virtual solutions, blockchain-enabled data-sharing mechanisms, and differentially private federated multi-task learning (DPFML) methodologies tailored for HDT.
The discussion on the concept of HDT encompasses an overview of its applications and its specific characteristics when compared to the conventional digital twin models. In addressing the design requirements and challenges of HDT, the text delves into the complexities of securing high-quality data, ensuring ultra-reliable and low-latency communication, data privacy and integrity, while managing storage, computation and analytics. Exploring edge-assisted human-to-virtual solutions, the book also introduces a connectivity framework and details the modeling process for human-to-virtual twin connectivity. The book later presents an in-depth examination of the practical Byzantine fault tolerance framework, followed by a discussion on shard-based Byzantine fault-tolerant schemes tailored for HDT, along with analyses of latency and throughput.
Furthermore, the book outlines the framework for DPFML-assisted human-to-virtual twin connectivity, accompanied by a novel consensus algorithm known as the proof of model quality. Finally, it presents future research directions aimed at addressing communication challenges hindering the seamless design and development of HDT. By offering this comprehensive exploration, the book serves as a valuable resource for researchers, practitioners, and policymakers seeking to navigate the evolving landscape of HDT and its transformative potential in various domains.
Autorenporträt
Samuel D. Okegbile received the Ph.D. degree in computer engineering from the University of Pretoria, South Africa, in 2021. He is currently a Postdoctoral Fellow in the Network Intelligence and Innovation Laboratory, Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada. His research interests are in the area of human digital twin as well as pervasive and mobile computing which includes various interesting topics on the Internet of Things, data sharing, artificial intelligence, wireless communication networks, and blockchain. He has received several awards, including the Horizon postdoctoral scholarship, the SENTECH scholarship and the University of Pretoria Doctoral Scholarship. He is also a regular reviewer for some IEEE journals and conferences and served as the Publication Chair for the 2023 Biennial Symposium on Communications.
Jun Cai received the Ph.D. degree in electrical engineering from the University of Waterloo, Waterloo, ON, Canada, in 2004. From 2004 to 2006, he was a Postdoctoral Fellow with the Natural Sciences and Engineering Research Council of Canada (NSERC), McMaster University, Hamilton, ON, Canada. From 2006 to 2018, he was with the Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB, Canada, where he was a Full Professor and the NSERC Industrial Research Chair. In 2019, he joined the Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada, as a Full Professor and the PERFORM Centre Research Chair. His current research interests include edge/fog computing, eHealth, radio resource management in wireless communications networks, and performance analysis. He received the Best Paper Award from Chinacom in 2013, the Rh Award for outstanding contributions to research in applied sciences in 2012 from the University of Manitoba, and the Outstanding Service Award from the IEEE Globecom 2010. He served as the Registration Chair for QShine 2005, the Track/Symposium Technical Program Committee (TPC) Co-Chair for the IWCMC 2008, the IEEE Globecom 2010, the IEEE VTC 2012, the IEEE CCECE 2017, and the IEEE VTC 2019, and the Publicity Co-Chair for the IWCMC 2010, 2011, 2013, 2014, 2015, 2017, and 2020, the TPC CoChair for the IEEE GreenCom 2018 and the General chair for the 2023 Biennial Symposium on Communications. He also served on the Editorial Board of the IEEE Internet of Things Journal, IET Communications, and Wireless Communications and Mobile Computing.

Changyan Yi is currently a Professor with the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics (NUAA), Nanjing, China. He received the Ph.D. degree from the Department of Electrical and Computer Engineering, University of Manitoba, MB, Canada, in 2018. From September 2018 to August 2019, he worked as a research associate in the University of Manitoba, MB, Canada. He was awarded Changkong Scholor of NUAA in 2018, and the Chinese Government Award for Outstanding Students Abroad in 2017. His research interests include game theory, queueing theory, machine learning and their applications in various wireless networks.