Digital Twin Technologies and Smart Cities

This book provides a holistic perspective on Digital Twin (DT) technologies, and presents cutting-edge research in the field. It assesses the opportunities that DT can offer for smart cities, and covers the requirements for ensuring secure, safe and sustainable smart cities. Further, the book demonstrates that DT and its benefits with regard to:

• data visualisation, real-time data analytics, and learning leading to improved confidence in decision making;
• reasoning, monitoring and warning to support accurate diagnostics and prognostics;
• acting using edge control and what-if analysis; and
• connection with back-end business applications hold significant potential for applications in smart cities, by employing a wide range of sensory and data-acquisition systems in various parts of the urban infrastructure.

The contributing authors reveal how and why DT technologies that are used for monitoring, visualising, diagnosing and predicting in real-time are vital to cities' sustainability and efficiency. The concepts outlined in the book represents a city together with all of its infrastructure elements, which communicate with each other in a complex manner. Moreover, securing Internet of Things (IoT) which is one of the key enablers of DT's is discussed in details and from various perspectives. The book offers an outstanding reference guide for practitioners and researchers in manufacturing, operations research and communications, who are considering digitising some of their assets and related services. It is also a valuable asset for graduate students and academics who are looking to identify research gaps and develop their own proposals for further research.

Gathers fundamental concepts on various applications of Digital Twin technologies in smart cities Discusses Digital Twin applications for asset management in smart cities, IoT and artificial intelligence Lays out a roadmap for the implementation of Digital Twin technologies in smart cities with regard to the Triple Bottom Line

Auteur
Dr Maryam Farsi is a Research Fellow in Manufacturing Systems Modelling and has over 13 years' experience in computational model development, data analysis and optimisation and additional experience in complex systems simulation and data visualisation. She is currently working on different system design and cost engineering projects funded by EPSRC and Innovate UK studying digital technologies, digital twin, automation and digital manufacturing. Dr Farsi gained her PhD in Nonlinear Structural Mechanics from Imperial College London and her MSc in Structures from City, University of London. She has experience in mathematical and computational modelling of manufacturing processes including dynamic data analysis and visualisation, resources' utilisation, inventory optimisation, cost analysis, and impact analysis concerning lean principles applications and new technology implementation. Her current research work involves studying complex systems simulation using a wide range ofcomputational techniques, Life-Cycle Costing (LCC), system design and flexible manufacturing. Dr Farsi is a member of the Institution of Engineering and Technology (IET) and an Fellow of Higher Education Academy (HEA). She is also the Associate Editor of the International Journal of Strategic Engineering (IJoSE). Maryam's research contributions are published in the forms of journal and conference papers, and book chapters.
Dr Alireza Daneshkhah is a Senior Lecturer in Statistics, and course director of M.Sc. Data Science and Computational Intelligence in the Faculty of Engineering, Environment and Computing of Coventry University. Alireza is Bayesian statistician interested in modelling interdependencies of large scale data and simulation of complex systems using the probabilistic methods including graphical models and Gaussian process emulators. He uses these tools in risk assessment of chain complex models common in environmental modelling and engineering applications (EPSRC funded project) to generate information about scenario's of interest to the decision makers. His current research interests are in probabilistic deep learning, in probabilistic risk and reliability analysis of networked infrastructure (EPSRC-UKWIR funded project); uncertainty/sensitivity analysis of complex engineering and environmental systems; remote condition monitoring and maintenance for networked infrastructure using advanced dynamic graphical models in the presence of massive heterogeneous information, including on-line data (SCADA and sensor data); expert judgement; modelling Big data using a wide range of probabilistic graphical models with applications in environmental risk assessment, reliability analysis, financial modelling, health economics, etc. Dr Daneshkhah most recent research interest is to employ Deep Gaussian process for image process with applications in processing medical images, satellite images.
Dr AminHosseinian-Far holds the position of Senior Lecturer & Deputy Subject Leader in Business Systems and Operations at the University of Northampton. In his previous teaching experience, Amin was a Staff Tutor at the Open University, and prior to that a Senior Lecturer and Course Leader at Leeds Beckett University. He has held lecturing and research positions at the University of East London, and at a number of private HE institutions and strategy research firms. Dr Hosseinian-Far has also worked as Deputy Director of Studies at a large private higher education institute in London. Dr Hosseinian-Far received his B.Sc. (Hons) in Business Information Systems from the University of East London, an M.Sc. degree in Satellite Communications and Space Systems from the University of Sussex, a Postgraduate Certificate in Research and a Ph.D. degree titled 'A Systemic Approach to an Enhanced Model for Sustainability' which he acquired from the University of East London. Amin holds Membership ofthe Institution of Engineering and Technology (IET), Senior Fellowship of the Higher Education Academy (HEA), and Fellowship of the Royal Society of Arts (RSA). He is also foundling editor and the Editor-in-Chief of the International Journal of Strategic Engineering (IJoSE).
Professor Hamid Jahankhani gained his Ph.D. from the Queen Mary College, University of London. In 1999 he moved to the University of East London (UEL) to become the first Professor of Information Security and Cyber Criminology at the university in 2010. Over the last 15 years Hamid have also been involved in developing new and innovative programmes and introducing block mode delivery approach at UEL, including MSc Information Security and Computer Forensics, Professional Doctorate Information Security. Hamid's principal research area for a number of years has been in the field of cyber security, information security and digital forensics. In partnership with the key industrial sectors, he has examined and established several innovative research projects that are of direct relevance to the needs of UK and European information security, digital forensics industries, Critical National Infrastructure and law enforcement agencies. Hamid have planned, proposed and managed several collaborative projects, and secured a substantial research income of up to £6m. Professor Jahankhani is the Editor-in-Chief of the International Journal of Electronic Security and Digital Forensics, International Journal of Electronic democracy, both published by Inderscience and general chair of the annual International Conference on Global Security, Safety and Sustainability (ICGS3). Hamid has edited and contributed to over 15 books and has over 150 conference and journal publications together with Various BBC radio interviews. I have supervised to completion 13 PhD and professional doctorate degree students and overseen 67 PhD students progressing. In summer 2017 Hamid was trained as the GCHQ cyberist to train…