Digital Twin Technologies and Smart Cities

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 of computational 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 Amin Hosseinian-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 of the Institution of Engineering and Technology (IET), Senior Fellowship of the H

The Convergence of Digital Twin, IoT and Machine Learning: Transforming Data into Action.- A Novel Approach Towards Enhancing the Quality of Life in Smart Cities using Clouds and IoT Based Technologies.- The Future of Mobility with Connected and Autonomous Vehicles in Smart Cities.- A Digital Twin Model for Enhancing Performance Measurement in Assembly Lines.- Information Sharing in Sustainable Value Chain Network (SVCN): The Perspective of Transportation in Cities.- Healthcare in the Cyberspace: Medical Cyber-Physical System and Digital Twin Challenges.- Present scenarios of IoT projects with security aspects focused.- IoT Security, Privacy, Safety and Ethics.- CoAP-application layer connection-less lightweight protocol for the Internet of Things (IoT) and CoAP-IPSEC Security with DTLS Supporting CoAP.- Some computational considerations for kernel-based support vector machine.- Secure Hybrid RSA (SHRSA) based multilayered authenticated, efficient and End to End secure 6-layered personal messaging communication protocol.