A QoS-Aware IoT Edge Network for Mobile Telemedicine Enabling In-Transit Monitoring of Emergency Patients

• Published in: Future internet 2024-02, Vol.16 (2), p.52
• Main Authors: Mukhopadhyay, Adwitiya, Remanidevi Devidas, Aryadevi, Rangan, Venkat P., Ramesh, Maneesha Vinodini
• Format: Article
• Language: English
• Subjects: Algorithms; Ambulances; Analysis; Communication; COVID-19; Data transmission; Developing countries; Disease transmission; edge; Edge computing; Electrocardiography; Emergency medical services; Emergency vehicles; fog; Health aspects; Health care access; Health care facilities; Health services; Hospitals; Internet; IoT; LDCs; Packets (communication); Pandemics; Patients; Physicians; Portable equipment; Quality of service architectures; Real time; Risk assessment; Rural areas; Rural communities; Telemedicine; VANETs; Wi-Fi
• ISSN: 1999-5903; 1999-5903
• DOI: 10.3390/fi16020052

Addressing the inadequacy of medical facilities in rural communities and the high number of patients affected by ailments that need to be treated immediately is of prime importance for all countries. The various recent healthcare emergency situations bring out the importance of telemedicine and demand rapid transportation of patients to nearby hospitals with available resources to provide the required medical care. Many current healthcare facilities and ambulances are not equipped to provide real-time risk assessment for each patient and dynamically provide the required medical interventions. This work proposes an IoT-based mobile medical edge (IM2E) node to be integrated with wearable and portable devices for the continuous monitoring of emergency patients transported via ambulances and it delves deeper into the existing challenges, such as (a) a lack of a simplified patient risk scoring system, (b) the need for architecture that enables seamless communication for dynamically varying QoS requirements, and (c)the need for context-aware knowledge regarding the effect of end-to-end delay and the packet loss ratio (PLR) on the real-time monitoring of health risks in emergency patients. The proposed work builds a data path selection model to identify the most effective path through which to route the data packets in an effective manner. The signal-to-noise interference ratio and the fading in the path are chosen to analyze the suitable path for data transmission.