Design and implementation of the internet of medical things data platform based on cloud-edge-end architecture_Journal of Biomedical Engineering

Authors：

LIANG Hong ¹ , SUN Jipeng ¹ , REN Hao ² , LU Xingping ² , LIU Tongze ² , FAN Yong ¹ , CHAO Yong ² , CAO Desen ² , HE Kunlun ¹ ,  ZHANG Zhengbo ¹ ,  LYU Junwen ²

1. Department of Medical Innovation and Research, Chinese PLA General Hospital, Beijing 100853, P. R. China;
2. Department of Medical Engineering, Chinese PLA General Hospital, Beijing 100853, P. R. China;

Corresponding?author：

ZHANG Zhengbo, Email: zhengbozhang@126.com; LYU Junwen, Email: lvjunwen@301hospital.com.cn

Keywords：

Internet of medical things; Data platform; Cloud-edge-end architecture; Edge platform; Cloud platform

DOI：

10.7507/1001-5515.202510002

Video：

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In the internet of medical things, data primarily exhibits time-series and streaming characteristics, featuring typical attributes such as large-scale volume, high transmission rates, and significant heterogeneity. Given these data properties and the application requirements of medical scenarios, the development of specialized data platforms tailored to these needs holds considerable research significance and practical value. This study innovatively proposes the internet of medical things data platform solution based on a cloud-edge-end architecture, and elaborates on its architecture, functions, and implementation effects. The edge side is responsible for streaming data access, storage, and computation; the cloud side encompasses three layers of services: resources, data, and applications, constructing a data lake to provide data analysis services. This study has been implemented in PLA General Hospital for verification. From 2021 to 2024, 263 medical devices have been connected accumulatively, with a total data volume of 24.07 TB and stable operation within 4 years. In the performance stress test, the platform achieved the data access throughput of 23.91 MB/s and the data storage efficiency of 30.98 MB/s. These results demonstrate the feasibility of the architecture platform. This study has engineered and successfully applied the cloud-edge-end architecture in complex internet of medical things scenarios, addressing challenges such as heterogeneous protocol compatibility of medical devices, real-time response to clinical operations, and large-scale storage and application of the internet of things data. The established data platform provides a solid data foundation for smart medical applications and holds significant value for the research of medical artificial intelligence and the construction of future smart hospitals.

Citation： LIANG Hong, SUN Jipeng, REN Hao, LU Xingping, LIU Tongze, FAN Yong, CHAO Yong, CAO Desen, HE Kunlun, ZHANG Zhengbo, LYU Junwen. Design and implementation of the internet of medical things data platform based on cloud-edge-end architecture. Journal of Biomedical Engineering, 2026, 43(2): 382-390. doi: 10.7507/1001-5515.202510002 Copy

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25.	Baimukhanov S, Ali H, Yazici A. Enhancing ml-based anomaly detection in data management for security through integration of IOT, cloud, and edge computing. Expert Syst Appl, 2025, 293: 128700.
26.	Nadeem F. Evaluating and ranking cloud IaaS, PaaS and SaaS models based on functional and non-functional key performance indicators. IEEE Access, 2022, 10: 63245-63257.
27.	Zhang L C, Bai J, Xu J J. Optimal allocation strategy of cloud resources with uncertain supply and demand for saas providers. IEEE Access, 2023, 11: 80997-81010.
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31.	Ataei M, Eghmazi A, Shakerian A, et al. Publish/subscribe method for real-time data processing in massive IOT leveraging blockchain for secured storage. Sensors, 2023, 23(24): 9692.
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34.	Nasab A S, Ghaffarian H, Rahmani M. Apache Flink and clustering-based framework for fast anonymization of IoT stream data. Intell Syst Appl, 2023, 20: 200267.
35.	Li H J, Li J L, Duan X L, et al. Energy-aware scheduling and two-tier coordinated load balancing for streaming applications in Apache Flink. Future Gener Comput Syst, 2025, 166: 107681.
36.	Deepthi B G, Rani K S, Krishna P V, et al. An efficient architecture for processing real-time traffic data streams using Apache Flink. Multimed Tools Appl, 2024, 83: 37369-37385.
37.	Samha A K. Strategies for efficient resource management in federated cloud environments supporting infrastructure as a service (IaaS). J Eng Res, 2024, 12(2): 101-114.
38.	Heuchert S, Rimal B P, Reisslein M, et al. Design of a small-scale and failure-resistant IaaS cloud using OpenStack. Appl Comput Inform, 2025, 21(1-2): 164-183.
39.	Manconi A, Gnocchi M, Milanesi L, et al. Framing Apache Spark in life sciences. Heliyon, 2023, 9(2): e13368.
40.	Raptis T P, Cicconetti C, Falelakis M, et al. Engineering resource-efficient data management for smart cities with Apache Kafka. Future Internet, 2023, 15(2): 43.
41.	Hlybovets A, Zvazhii D. Implementation of a suffix tree-based index for searching for substrings in a large DBMS. Cybern Syst Anal, 2025, 61: 265-275.
42.	Aljuhani A, Alamri A, Kumar P, et al. An intelligent and explainable SaaS-based intrusion detection system for resource-constrained IOMT. IEEE Internet Things J, 2024, 11(15): 25454-25463.

1. Ashton K. That “internet of things” thing. RFID J, 2009, 22: 97-114.
2. Gulzar B, Sofi S A, Sholla S. Convergence of personal internet of things into social internet of things. Cluster Comput, 2025, 28: 277.
3. Yavari A, Korala H, Georgakopoulos D, et al. Sazgar IOT: a device-centric IOT framework and approximation technique for efficient and scalable IoT data processing. Sensors, 2023, 23(11): 5211.
4. Oliveira A S R, Carvalho N B, Santos J, et al. All-digital RFID readers: an RFID reader implemented on an FPGA chip and/or embedded processor. IEEE Microw Mag, 2021, 22(3): 18-24.
5. Maderb?ck M, Ussmueller T. Multicarrier communication for UHF RFID: increased reliability and coverage for UHF RFID systems. IEEE Microw Mag, 2023, 24(10): 51-58.
6. Kao H W, Wu E H K. QoE sustainability on 5G and beyond 5G networks. IEEE Wirel Commun, 2023, 30(1): 118-125.
7. Almeida A, Rito P, Brás S, et al. A machine learning approach to forecast 5G metrics in a commercial and operational 5G platform: 5G and mobility. Comput Commun, 2024, 228: 107974.
8. Rady M, Iova O, Rivano H, et al. How does Wi-Fi 6 fare? An industrial outdoor robotic scenario. Ad Hoc Netw, 2024, 156: 103418.
9. Karamyshev A, Liubogoshchev M, Lyakhov A, et al. Enabling industrial internet of things with Wi-Fi 6: an automated factory case study. IEEE Trans Ind Inform, 2024, 20(11): 13090-13100.
10. Li X, Zhao Y L, Wang W, et al. Deadline-driven signaling scheduling scheme for deterministic service restoration in optical transport networks of F5G. Opt Express, 2025, 33(12): 24204-24220.
11. Liu W H, Zhao Y L, Li Y J, et al. Segmented protection scheme based on maximum bandwidth sharing in F5G. J Opt Commun Netw, 2024, 16(11): 1145-1158.
12. Pinhanez C, Michahelles F, Schmidt A. Human-centered AI. IEEE Pervasive Comput, 2023, 22(1): 7-8.
13. Bosch J, Olsson H H, Brinne B, et al. AI engineering: realizing the potential of AI. IEEE Softw, 2022, 39(6): 23-27.
14. El-Saleh A A, Sheikh A M, Albreem M A M, et al. The internet of medical things (IOMT): opportunities and challenges. Wirel Netw, 2024, 31: 327-344.
15. Huang C X, Wang J, Wang S H, et al. Internet of medical things: a systematic review. Neurocomputing, 2023, 557: 126719.
16. Alizadehsani R, Roshanzamir M, Izadi N H, et al. Swarm intelligence in internet of medical things: a review. Sensors, 2023, 23(3): 1466.
17. 梁洪, 孫繼鵬, 范勇, 等. 重癥監護病房醫療設備物聯網方案設計與應用實現. 生物醫學工程學雜志, 2025, 42(1): 65-72.
18. 范勇, 梁洪, 孫繼鵬, 等. 基于云邊端架構的急救醫療設備物聯網設計與實現. 生物醫學工程學雜志, 2023, 40(1): 103-109.
19. 李麗軒, 梁洪, 范勇, 等. 基于物聯網和可穿戴技術的智能監護系統研發及其應用模式探索研究. 生物醫學工程學雜志, 2023, 40(6): 1053-1061.
20. Arney D, Plourde J, Goldman J M. OpenICE medical device interoperability platform overview and requirement analysis. Biomed Tech, 2018, 63(1): 39-47.
21. Arney D, Zhang Y, Kennedy-Metz L R, et al. An open-source, interoperable architecture for generating real-time surgical team cognitive alerts from heart-rate variability monitoring. Sensors, 2023, 23(8): 3890.
22. 楊策源, 康季槐. ICU醫療設備物聯網解決方案研究. 中國醫療設備, 2020, 35(2): 115-119.
23. Zovko K, ?eri? L, Perkovi? T, et al. IOT and health monitoring wearable devices as enabling technologies for sustainable enhancement of life quality in smart environments. J Clean Prod, 2023, 413: 137506.
24. Damera V K, Cheripelli R, Putta N, et al. Enhancing remote patient monitoring with ai-driven IOMT and cloud computing technologies. Sci Rep, 2025, 15: 24088.
25. Baimukhanov S, Ali H, Yazici A. Enhancing ml-based anomaly detection in data management for security through integration of IOT, cloud, and edge computing. Expert Syst Appl, 2025, 293: 128700.
26. Nadeem F. Evaluating and ranking cloud IaaS, PaaS and SaaS models based on functional and non-functional key performance indicators. IEEE Access, 2022, 10: 63245-63257.
27. Zhang L C, Bai J, Xu J J. Optimal allocation strategy of cloud resources with uncertain supply and demand for saas providers. IEEE Access, 2023, 11: 80997-81010.
28. Ed-daoudy A, Maalmi K. A new internet of things architecture for real-time prediction of various diseases using machine learning on big data environment. J Big Data, 2019, 6: 104.
29. Y?ld?r?m E, Cicio?lu M, ?alhan A. Fog-cloud architecture-driven internet of medical things framework for healthcare monitoring. Med Biol Eng Comput, 2023, 61: 1133-1147.
30. Wn?k K, Bory?o P. A data processing and distribution system based on Apache NiFi. Photonics, 2023, 10(2): 210.
31. Ataei M, Eghmazi A, Shakerian A, et al. Publish/subscribe method for real-time data processing in massive IOT leveraging blockchain for secured storage. Sensors, 2023, 23(24): 9692.
32. Debauche O, Mahmoudi S A, Cock N D, et al. Cloud architecture for plant phenotyping research. Concurr Comput, 2020, 32(17): 5661.
33. Raptis T P, Cicconetti C, Passarella A. Efficient topic partitioning of Apache Kafka for high-reliability real-time data streaming applications. Future Gener Comput Syst, 2024, 154: 173-188.
34. Nasab A S, Ghaffarian H, Rahmani M. Apache Flink and clustering-based framework for fast anonymization of IoT stream data. Intell Syst Appl, 2023, 20: 200267.
35. Li H J, Li J L, Duan X L, et al. Energy-aware scheduling and two-tier coordinated load balancing for streaming applications in Apache Flink. Future Gener Comput Syst, 2025, 166: 107681.
36. Deepthi B G, Rani K S, Krishna P V, et al. An efficient architecture for processing real-time traffic data streams using Apache Flink. Multimed Tools Appl, 2024, 83: 37369-37385.
37. Samha A K. Strategies for efficient resource management in federated cloud environments supporting infrastructure as a service (IaaS). J Eng Res, 2024, 12(2): 101-114.
38. Heuchert S, Rimal B P, Reisslein M, et al. Design of a small-scale and failure-resistant IaaS cloud using OpenStack. Appl Comput Inform, 2025, 21(1-2): 164-183.
39. Manconi A, Gnocchi M, Milanesi L, et al. Framing Apache Spark in life sciences. Heliyon, 2023, 9(2): e13368.
40. Raptis T P, Cicconetti C, Falelakis M, et al. Engineering resource-efficient data management for smart cities with Apache Kafka. Future Internet, 2023, 15(2): 43.
41. Hlybovets A, Zvazhii D. Implementation of a suffix tree-based index for searching for substrings in a large DBMS. Cybern Syst Anal, 2025, 61: 265-275.
42. Aljuhani A, Alamri A, Kumar P, et al. An intelligent and explainable SaaS-based intrusion detection system for resource-constrained IOMT. IEEE Internet Things J, 2024, 11(15): 25454-25463.

Journal of Biomedical Engineering

Design and implementation of the internet of medical things data platform based on cloud-edge-end architecture

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