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

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  • Adwitiya Mukhopadhyay

    (Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri 690525, India
    Department of Computer Science, Amrita School of Computing, Mysuru Campus, Amrita Vishwa Vidyapeetham, Bhogadi 570026, India)

  • Aryadevi Remanidevi Devidas

    (Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri 690525, India)

  • Venkat P. Rangan

    (Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri 690525, India)

  • Maneesha Vinodini Ramesh

    (Center for Wireless Networks & Applications (WNA), Amrita Vishwa Vidyapeetham, Amritapuri 690525, India)

Abstract

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 (IM 2 E) 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.

Suggested Citation

  • Adwitiya Mukhopadhyay & Aryadevi Remanidevi Devidas & Venkat P. Rangan & Maneesha Vinodini Ramesh, 2024. "A QoS-Aware IoT Edge Network for Mobile Telemedicine Enabling In-Transit Monitoring of Emergency Patients," Future Internet, MDPI, vol. 16(2), pages 1-22, February.
    • Handle: RePEc:gam:jftint:v:16:y:2024:i:2:p:52-:d:1333981
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    References listed on IDEAS

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    1. Richard M Wood & Christopher J McWilliams & Matthew J Thomas & Christopher P Bourdeaux & Christos Vasilakis, 2020. "COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care," Health Care Management Science, Springer, vol. 23(3), pages 315-324, September.
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