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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 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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Edge computing: Vision and challenges
Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2579198 · Cited by 8,733
Fog computing and its role in the internet of things
Bonomi, F., Milito, R., Zhu, J., & Addepalli, S. (2012) · Proceedings of the first edition of the MCC workshop on Mobile cloud computing · doi:10.1145/2342509.2342513 · Cited by 6,244
Edge-centric Computing: Vision and Challenges
Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P., & Riviere, E. (2015) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2831347.2831354 · Cited by 934
Fog Computing: Platform and Applications
Yi, S., Hao, Z., Qin, Z., & Li, Q. (2015) · 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb) · doi:10.1109/hotweb.2015.22 · Cited by 829
A survey on mobile edge computing
Ahmed, A., & Ahmed, E. (2016) · International Conference on Intelligent Systems and Control · doi:10.1109/isco.2016.7727082 · Cited by 611
An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare
Yang, Z., Zhou, Q., Lei, L., Zheng, K., & Xiang, W. (2016) · Journal of Medical Systems · doi:10.1007/s10916-016-0644-9 · Cited by 490
Fog Computing: Focusing on Mobile Users at the Edge
Luan, T. H., Gao, L., Li, Z., Xiang, Y., Wei, G., & Sun, L. (2015) · arXiv · doi:10.48550/arxiv.1502.01815 · Cited by 398
Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing
Skala, K., Davidović, D., Afgan, E., Sović, I., & Šojat, Z. (2015) · Institutional Repository of the Ruđer Bošković Institute (Ruđer Bošković Institute) · doi:10.19210/1002.2.1.16 · Cited by 155
Jacobs, P. G., Resalat, N., El Youssef, J., Reddy, R., Branigan, D., Preiser, N., Condon, J., & Castle, J. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815609371 · Cited by 105
Wireless technologies for telemedicine
Ng, H. S., Sim, M. L., Tan, C. M., & Wong, C. C. (2006) · BT Technology Journal · doi:10.1007/s10550-006-0050-9 · Cited by 86
A Review of Safety and Design Requirements of the Artificial Pancreas
Blauw, H., Keith-Hynes, P., Koops, R., & DeVries, J. H. (2016) · Annals of Biomedical Engineering · doi:10.1007/s10439-016-1679-2 · Cited by 64
Dargaville, P. A., Fathabadi, O. S., Plottier, G. K., Lim, K., Wheeler, K., Jayakar, R., & Gale, T. J. (2016) · Archives of Disease in Childhood Fetal & Neonatal · doi:10.1136/archdischild-2016-310650 · Cited by 48
Modulation of critical brain dynamics using closed-loop neurofeedback stimulation
Zhigalov, A., Kaplan, A., & Palva, J. M. (2016) · Clinical Neurophysiology · doi:10.1016/j.clinph.2016.04.028 · Cited by 26
IFCIoT: Integrated Fog Cloud IoT Architectural Paradigm for Future Internet of ThingsPreprint
Munir, A., Kansakar, P., & Khan, S. U. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.08474 · Cited by 23
Thiel, S., Mitchell, J. M., & Williams, J. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816676189 · Cited by 6
Smart Sensors for Maintaining Physiologic Homeostasis
Klonoff, D. C. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500301 · Cited by 5
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Javaid, M., & Khan, I. H. (2021) · Journal of Oral Biology and Craniofacial Research · doi:10.1016/j.jobcr.2021.01.015 · Cited by 423
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Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.05.016 · Cited by 318
An Empirical Study on System Level Aspects of Internet of Things (IoT)
Swamy, S. N., & Raju, K. S. (2020) · IEEE Access · doi:10.1109/access.2020.3029847 · Cited by 270
Edge Intelligence and Internet of Things in Healthcare: A Survey
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Metadata
- 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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