Abstract
The expansion of Internet of Things (IoT) technologies has revolutionized various sectors, one of the most critical being healthcare. The effective management of Patient Medical Records (PMRs) is an area where IoT plays a significant role, and its integration with Cloud Computing offers an enormous opportunity to enhance data accessibility, efficiency, and cost-effectiveness. However, the challenge of securing PMRs in the cloud remains a key concern. This paper introduces SPaMeR, an innovative IoT platform based on microservice and brokerless architecture, tailored to address this challenge and the specific requirements of healthcare environments. SPaMeR platform incorporates and extends the core functionalities of the IoT platform designed in our previous work - data collection, device and user management, and remote device control - while specifically addressing six critical issues for healthcare data: a) secure and reliable transmission of medical data, b) energy efficiency for healthcare devices, c) high-speed and accurate data collection from medical devices, d) robust security mechanisms to protect sensitive patient information, e) scalability to accommodate the ever-growing number of patients and medical devices, and f) compliance with healthcare data regulations and standards. To demonstrate the effectiveness and feasibility of SPaMeR, we provide a comprehensive evaluation with two distinct healthcare scenarios. Our results indicate significant improvements in the areas of data security, energy efficiency, and system scalability compared to traditional healthcare platforms.
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Acknowledgement
We would like to extend our deepest gratitude to Engineer Le Thanh Tuan and Mr. Lam Nguyen Tran Thanh for their invaluable contribution and insight throughout the conceptualization, execution, and assessment of this project.
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Nam, T.B. et al. (2023). SPaMeR: Securing Patient Medical Records in the Cloud - A Microservice and Brokerless Architecture Approach. In: Zhang, Y., Zhang, LJ. (eds) Web Services – ICWS 2023. ICWS 2023. Lecture Notes in Computer Science, vol 14209. Springer, Cham. https://doi.org/10.1007/978-3-031-44836-2_3
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