Decentralized pub/sub architecture for real-time remote patient monitoring

Abstract

The combination of the Internet of Things (IoT) and medical devices, known as the Internet of Medical Things (IoMT), is changing the way that patients are monitored in scenarios with real-time requirements. When medical IoT devices are used, traditional methods that involve sending data directly from medical sensors to the cloud don't work optimally due to high latency, low tolerance to network failures and network resource-inefficiency. The European Telecommunications Standards Institute (ETSI) defines Multi-access Edge computer (MEC) as a new way to solve the problems related to latency, failure tolerance and resource-efficiency by moving computing resources closer to data sources. In real-time patient tracking, however, MEC alone does not fully meet the needs of medical IoT in ,e.g., situations where monitoring needs to continue during access network outage that may occur in bad coverage areas and tunnels in ambulance scenarios. In this thesis, we develop a RabbitMQ-based message queue service has been developed and evaluated, ensuring uninterrupted data flow from the patient to healthcare systems, adapting to various exceptional situations. The developed message queue service utilizes a three-tier edge-cloud architecture, combining local edge computing, mobile network edge computing, and cloud computing to enhance real-time patient monitoring in medical IoT systems, while ensuring resilience, efficiency, and data security in health information management. The developed architecture uses local edge computing for data collection, data preprocessing, and interim storage, MEC for more demanding data processing at the network edge, and cloud computing to compile all the data, store it in healthcare systems, and present it to healthcare staff. Because healthcare systems dealing with patient data need to be secure, the Transport Layer Security (TLS) protocol is used to ensure that personal patient information remains safe and private during data transmission and processing. This design is a big step forward in healthcare technology, and on its behalf is a significant step forward in healthcare technology development indicating a path for future research on how patient monitoring, treatment outcomes, and patient safety can be improved by combining AI-based algorithms with edge-cloud computing and a secure message queue service.