Decentralized Pub/Sub Architecture for Real-Time Remote Patient Monitoring: A Feasibility Study
Haque, Kazi Nymul; Islam, Johirul; Ahmad, Ijaz; Harjula, Erkki (2024-05-05)
Springer
05.05.2024
Haque, K.N., Islam, J., Ahmad, I., Harjula, E. (2024). Decentralized Pub/Sub Architecture for Real-Time Remote Patient Monitoring: A Feasibility Study. In: Särestöniemi, M., et al. Digital Health and Wireless Solutions. NCDHWS 2024. Communications in Computer and Information Science, vol 2083. Springer, Cham. https://doi.org/10.1007/978-3-031-59080-1_4
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© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
https://creativecommons.org/licenses/by/4.0/
© 2024 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202405133345
https://urn.fi/URN:NBN:fi:oulu-202405133345
Tiivistelmä
Abstract
The confluence of the Internet of Things (IoT) within the healthcare sector, called Internet of Medical Things (IoMT), has ushered in a transformative approach to real-time patient monitoring. Traditional methods that typically involve the direct transmission of medical sensor data to the cloud, falter under the constraints of medical IoT devices. In response, Multi-access Edge Computing (MEC), as defined by the European Telecommunications Standards Institute (ETSI), brings forth an innovative solution by relocating computing resources closer to the origin of data. However, MEC alone does not fully address the exigencies of constrained medical IoTs in the realm of real-time monitoring. Our architecture advances the computing continuum by seamlessly integrating local edge computing for direct data capture, MEC for nuanced data processing, and cloud computing for the comprehensive synthesis and presentation of data. This synergy is further enhanced by the introduction of a robust message queue mechanism, assuring data resilience and uninterrupted data streaming during network disruptions. With a steadfast commitment to security, our system employs stringent measures to ensure the integrity and confidentiality of sensitive patient data during transmission. This architecture represents a significant leap in healthcare technology, emphasizing the criticality of patient safety, data security, and meticulous data management. The implications of this study are profound, indicating a trajectory for future exploration into the integration of sophisticated data types and AI-driven models to further refine patient monitoring and healthcare outcomes.
The confluence of the Internet of Things (IoT) within the healthcare sector, called Internet of Medical Things (IoMT), has ushered in a transformative approach to real-time patient monitoring. Traditional methods that typically involve the direct transmission of medical sensor data to the cloud, falter under the constraints of medical IoT devices. In response, Multi-access Edge Computing (MEC), as defined by the European Telecommunications Standards Institute (ETSI), brings forth an innovative solution by relocating computing resources closer to the origin of data. However, MEC alone does not fully address the exigencies of constrained medical IoTs in the realm of real-time monitoring. Our architecture advances the computing continuum by seamlessly integrating local edge computing for direct data capture, MEC for nuanced data processing, and cloud computing for the comprehensive synthesis and presentation of data. This synergy is further enhanced by the introduction of a robust message queue mechanism, assuring data resilience and uninterrupted data streaming during network disruptions. With a steadfast commitment to security, our system employs stringent measures to ensure the integrity and confidentiality of sensitive patient data during transmission. This architecture represents a significant leap in healthcare technology, emphasizing the criticality of patient safety, data security, and meticulous data management. The implications of this study are profound, indicating a trajectory for future exploration into the integration of sophisticated data types and AI-driven models to further refine patient monitoring and healthcare outcomes.
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