Fog Computing and Edge Computing Architectures for Processing Data From Diabetes Devices Connected to the Medical Internet of Things
Open accessAbstract
The Internet of Things (IoT) is generating an immense volume of data. With cloud computing, medical sensor and actuator data can be stored and analyzed remotely by distributed servers. The results can then be delivered via the Internet. The number of devices in IoT includes such wireless diabetes devices as blood glucose monitors, continuous glucose monitors, insulin pens, insulin pumps, and closed-loop systems. The cloud model for data storage and analysis is increasingly unable to process the data avalanche, and processing is being pushed out to the edge of the network closer to where the data-generating devices are. Fog computing and edge computing are two architectures for data handling that can offload data from the cloud, process it nearby the patient, and transmit information machine-to-machine or machine-to-human in milliseconds or seconds. Sensor data can be processed near the sensing and actuating devices with fog computing (with local nodes) and with edge computing (within the sensing devices). Compared to cloud computing, fog computing and edge computing offer five advantages: (1) greater data transmission speed, (2) less dependence on limited bandwidths, (3) greater privacy and security, (4) greater control over data generated in foreign countries where laws may limit use or permit unwanted governmental access, and (5) lower costs because more sensor-derived data are used locally and less data are transmitted remotely. Connected diabetes devices almost all use fog computing or edge computing because diabetes patients require a very rapid response to sensor input and cannot tolerate delays for cloud computing.
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Edge computing is a viable option for processing data from medical devices such as blood glucose meters in real time, helping detect changes that could indicate development of chronic diseases such as diabetes.
“Edge computing is a viable option for processing data obtained from medical devices such as blood glucose meters in real time.Processing data is an indispensable component that helps detect changes in a person's health that could indicate the development of chronic diseases such as diabetes [35].”
From Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases: Opportunities, Challenges, and Future Directions (2023) · cites this paper as [35]
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Architecture, Framework, and Models for Edge-AI in Healthcare
Batra, I., & Som, S. (2024) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/979-8-3693-2909-2.ch004 · Cited by 1 · see citing works
Iot In Healthcare: Challenges and Opportunities for Improved Patient Outcomes
Chattopadhyay, S. (2019) · INFORMATION TECHNOLOGY IN INDUSTRY · doi:10.17762/itii.v7i3.813 · Cited by 1 · see citing works
DewMonitor: Dew Computing Monitoring System for Sustainable IoT
Karmakar, A., Ghosh, P., Hirsch, M., Banerjee, P. S., De, D., Mateos, C., & Zunino, A. (2023) · Internet of things · doi:10.1007/978-981-99-4590-0_3 · Cited by 1 · see citing works
Blockchain-based smart contract for IoT enabled food industries: A systematic mapping study
Supriyadi, S., Djatna, T., Arkeman, Y., Muslich, M., & Hendrawan, V. S. (2025) · AIP conference proceedings · doi:10.1063/5.0253572 · Cited by 1 · see citing works
Research on Implementation Methods of Edge Computing in Intelligent Hydrology
Lin, Y., Wan, D., Zhao, Q., & Yang, Y. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-19063-7_18 · Cited by 1 · see citing works
Lv, R., Tian, Y., Yu, Z., & Liu, C. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04118-3 · Cited by 1 · see citing works
Bakker‐Reynolds, G., Erturk, E., & Lengyel, I. (2021) · doi:10.1002/9781119748342.ch8 · Cited by 1 · see citing works
RaviKrishna, B., Potluri, S., Jeny, J. R. V., Sajja, G. S., & Rao, K. S. (2022) · doi:10.1002/9781394156887.ch10 · Cited by 1 · see citing works
Thomas, L. A. B. (2019) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-5225-9023-1.ch008
Thomas, L. A. B. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-5700-9.ch009
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Majumder, D., & Kumar, S. M. (2022) · Cardiometry · doi:10.18137/cardiometry.2022.22.444455
Cyber Security and Cyber Resilience for the Australian E-Health Records
Venkatachalam, N., O'Connor, P., & Palekar, S. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-7132-6.ch044
A Healthcare System Based on Fog Computing
Alhazzani, M. A., Chelloug, S. A., Alomari, R. A., Alshammari, M. K., & Alqahtani, R. S. (2023) · Advances in intelligent systems and computing · doi:10.1007/978-3-031-28106-8_5
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Mohanty, S., Dash, A., Mohapatra, S., Sahoo, A., & Mondal, S. K. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-1203-2_15
Hybrid and Effective Model for the Prediction of DiabetesPreprint
Tripathy, N., Pande, S. K., & Das, S. (2024) · Research Square · doi:10.21203/rs.3.rs-3952667/v1
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Gušev, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-54321-0_7
Impact of Edge Computing on Cloud Infrastructure- A Review Paper
Mahida, A. (2019) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24314130019
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Sony, P., Shanmugam, G. S., & Nagarajan, S. (2024) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2024.04.004
An Intelligent Diagnostic System for Type 2 Diabetes Mellitus
Pati, A., Parhi, M., Pattanayak, B. K., Barisal, S. K., & Nayak, G. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-5015-7_4
An intelligent fog computing–based diabetes prediction system for remote healthcare applications
Tripathy, B., Bebortta, S., Tripathy, S. S., & Pani, S. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95686-4.00021-6
- Authors
- Klonoff, D. C.
- Affiliations
- Diabetes Research Institute; Mills-Peninsula Medical Center, San Mateo, CA, USA
- Corresponding author
- Klonoff, David C.
- ORCID
- Klonoff, David C.
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2017-07
- Volume
- 11
- Issue
- 4
- Pages
- 647-652
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC5588847
- OpenAlex
- W2736668803
- Cited by
- 110
- References
- 15
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 18 · 2023: 14 · 2022: 20 · 2021: 18 · 2020: 9 · 2019: 13 · 2018: 2
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Diabetes Science and Technology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Abbott Diabetes Care
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- fog computing, edge computing, Internet of Medical Things, diabetes technology, continuous glucose monitoring, insulin pump, closed-loop systems, blood glucose monitoring, sensor data processing, data privacy and security, low-latency data transmission
- MeSH terms
- Computing Methodologies, Diabetes Mellitus, Type 1, Humans, Insulin Infusion Systems, Blood Glucose Self-Monitoring, Internet
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