## 2 Background & Current Research
It aims to close the gap between computations and data sources from medical facilities. Furthermore, the novel computing platform established on the fog computing paradigm can aid in reducing latency while sending and communicating signals with faraway servers, which might speed up medical services in the spatial and temporal dimensions. Finally, the last and more recent works we have studied are between 2021 and 2023. Using the Digital Twin paradigm, [PERSON] et al. [37] suggested and put into practice an intelligent, context-aware healthcare system. This framework is a useful addition to digital healthcare and will enhance operational efficiency in the healthcare industry. In order to diagnose heart disease and identify cardiac issues, a classifier model for ECG heart rhythms was created using machine learning. The built models accurately and reliably predicted a specific heart condition using several strategies. The application of machine learning and artificial intelligence with various human body parameters for continuous monitoring and abnormality detection was also inspired by the implementation of an ECG classifier that recognizes cardiac problems. Furthermore, Digital twin research and application are the main topics covered by [PERSON] et al. [38]. Additionally, they examined current digital twin breakthroughs before summarizing the theory behind the technology and concluding with concrete advancements in several digital twin application fields. Moreover, [PERSON] et al. [39] introduced the Cloud-based Object Tracking and Behavior Identification System (COTBIS), which can incorporate the edge computing capability framework at the gateway level. This research is an emerging research area of the IoTthat can bring robustness and intelligence to distributed video surveillance systems by minimizing network bandwidth and response time between wireless cameras and cloud servers. Also, the proposal of an IoT-based smart healthcare video surveillance system uses edge computing to reduce the network bandwidth and response time and maximize the fall behavior prediction accuracy significantly compared to existing cloud-based video surveillance systems. On this basis, [PERSON] et al. 's [40] research aims to do Big Data Analysis on the huge amounts of data generated by the Internet of Things (IoT) in smart cities, causing the smart city to shift in the direction of good governance and effective and secure data processing. This study introduces the Deep Learning method utilizing Big Data Analysis and advances the distributed parallelism technique of Convolutional Neural Network with a focus on the multi-source data gathered in the smart city. [PERSON] et al. 's research led them to the conclusion that enhancing the IoT-Big Data Analysis system of smart cities can reduce data transmission delay, improve data forecasting accuracy, and offer actual efficacy, providing experimental references for the future digital development of smart cities. Furthermore, through their research, [PERSON] et al. [41] have focused on the integration of these two promising technologies for use in real-time and life-critical scenarios, as well as for simplicity in governance in applications based on smart cities. The authors also present a thorough overview of the numerous Federated Learning model applications in Digital Twin s based on smart cities. This study's key finding is that there are some significant obstacles to overcome and promising approaches moving forward for better Federated Learning-Digital Twin integration in applications. At last, [PERSON] et al. [42] anticipate exploring the Digital Twins of a Smart City's Building Information Modeling (BIM) Big Data (BD) processing approach in order to hasten the development of a Smart City and enhance the precision of data processing. Building Information Modeling, which optimizes diverse resources and building configurations, creates the keel and structure of the building.