Towards a cooperative hierarchical healthcare architecture using the HMAN offloading scenarios and SRT calculation algorithm. Issue 1 (29th September 2022) - Jasim, Ahmed M.; Al‐Raweshidy, Hamed
Abstract: The Internet of medical things (IoMT) is one of the most promising fields that is expected to rapidly expand in the near future. Consequently, vast amounts of data will be generated, necessitating faster and more intensive processing capabilities. Several healthcare architectures based on Edge/Fog technologies have been created to lower healthcare expenses and provide better and more reliable services. Scalability, availability, capacity, latency, and privacy are some of the most pressing issues to consider when designing such architectures due to the critical and sensitive nature of healthcare data. To contribute to the reliability and robustness of electronic health services, this work proposes Healthcare Metropolitan Area Network (HMAN), a cooperative hierarchical Edge/Fog computing‐based architecture for the urban healthcare systems. The presented architecture suggests HMAN offloading scenarios and system response time calculation (HOSSC) algorithm which is specially designed to provide an abundance of offloading and processing scenarios within the network. The architecture also connects patients to the healthcare system by utilising the existing infrastructure in cities (e.g. medical centres and hospitals). Simulation results revealed that the designed architecture produced a ubiquitous and scalable healthcare system with promising and competitive performance, such as the computing capacity and service availability, by adopting multiple cooperative hierarchical offloading scenarios across the framework units. Moreover, the HMAN system was evaluated for latency and found to be very robust, with a short response time ranging between 6.043 and 31.45 ms in responding to 1 to 300 patients simultaneously sending. In addition to these appealing features, the proposed architecture ensures patients' privacy because the data are locally stored and processed in the most anticipated scenarios. The proposed architecture is a viable solution to providing healthcare services to a large number of patients. Abstract :