In the last decade, the Internet of Things paradigm has caused an exponential increase in the number of connected devices. This trend brings the Internet closer to everyday activities and enables data collection that can be used to create and improve a great variety of services and applications. Despite its great benefits, this paradigm also comes with several challenges. More powerful storage and processing capabilities are required to service all these devices. Additionally, the need to deploy and manage the infrastructure to efficiently support these resources continues to pose a challenge. Modeling and simulation can help to design and analyze these scenarios, providing flexible and powerful mechanisms to study and compare different strategies and infrastructures. In this scenario, Micro Data Centers (MDCs) can be used as an effective way of reducing overwhelmed Cloud Data Center infrastructures. This paper explores an M\&S methodology to study the overall power consumption of a healthcare IoT scenario. The patients wear non-intrusive monitoring devices that periodically generate tasks to be executed in MDCs. We extract the layout of existing urban infrastructures, simulate the monitored population's behavior, and compare the power consumption of several data center configurations.
Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；School of Automation, Guangdong University of Technology（广东工业大学自动化学院）
HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System
United Arab Emirates University（阿联酋大学）；The University of Melbourne（墨尔本大学）
A Survey on Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions
Manchester Metropolitan University（曼彻斯特城市大学）；Vellore Institute of Technology（韦洛尔理工学院）；Trinity College Dublin, The University of Dublin（都柏林大学圣三一学院）；Lebanese American University（黎巴嫩美国大学）；Ho Chi Minh City University of Technology and Education（胡志明市技术教育大学）；University College Dublin（都柏林大学学院）
Federated Learning with Gramian Angular Fields for Privacy-Preserving ECG Classification on Heterogeneous IoT Devices
Laboratoire LITAN（LITAN实验室）；École supérieure en Sciences et Technologies de l’Informatique et du Numérique（信息与数字技术高级学院）；College of Engineering and IT（工程与信息技术学院）；University of Dubai（迪拜大学）；Department of Computer Science（计算机科学系）；University of Sharjah（沙迦大学）
A Real-Time DDS-Based Chest X-Ray Decision Support System for Resource-Constrained Clinics
Department of Computer Engineering King Fahd University of Petroleum \& Minerals Dhahran, Saudi Arabia；Social Development King Fahd University of Petroleum \& Minerals Dhahran, Saudi Arabia
