Resumen
The aging of the world?s population, the willingness of elderly to remain independent, and the recent COVID-19 pandemic have demonstrated the urgent need for home-based diagnostic and patient monitoring systems to reduce the financial and organizational burdens that impact healthcare organizations and professionals. The Internet of Medical Things (IoMT), i.e., all medical devices and applications that connect to health information systems through online computer networks. The IoMT is one of the domains of IoT where real-time processing of data and reliability are crucial. In this paper, we propose RAMi, which is a Real-Time Architecture for the Monitoring of elderly patients thanks to the Internet of Medical Things. This new architecture includes a Things layer where data are retrieved from sensors or smartphone, a Fog layer built on a smart gateway, Mobile Edge Computing (MEC), a cloud component, blockchain, and Artificial Intelligence (AI) to address the specific problems of IoMT. Data are processed at Fog level, MEC or cloud in function of the workload, resource requirements, and the level of confidentiality. A local blockchain allows workload orchestration between Fog, MEC, and Cloud while a global blockchain secures exchanges and data sharing by means of smart contracts. Our architecture allows to follow elderly persons and patients during and after their hospitalization. In addition, our architecture allows the use of federated learning to train AI algorithms while respecting privacy and data confidentiality. AI is also used to detect patterns of intrusion.