A fog computing-based architecture for medical records management
Open accessAbstract
The aging of the world’s population and the growth in the number of people with chronic diseases have increased expenses with medical care. Thus, the use of technological solutions has been widely adopted in the medical field to improve the patients’ health. In this context, approaches based on Cloud Computing have been used to store and process the information generated in these solutions. However, using Cloud can create delays that are intolerable for medical applications. Thus, the Fog Computing paradigm emerged as an alternative to overcome this problem, bringing computation and storage closer to the data sources. However, managing medical data stored in Fog is still a challenge. Moreover, characteristics of availability, performance, interoperability, and privacy need to be considered in approaches that aim to explore this problem. So, this article shows a software architecture based on Fog Computing and designed to facilitate the management of medical records. This architecture uses Blockchain concepts to provide the necessary privacy features and to allow Fog Nodes to carry out the authorization process in a distributed way. Finally, this paper describes a case study that evaluates the performance, privacy, and interoperability requirements of the proposed architecture in a home-centered healthcare scenario.
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Prior studies have promulgated the use of blockchain as a proficient method of managing medical data and electronic personal health information or records.
“Prior studies have promulgated the use of blockchain as a proficient method of managing medical data (Quaini et al., 2018;Hussein et al., 2018;Silva et al., 2019;Shen et al., 2019;Tian et al., 2019) and electronic personal health information or records (PHRs) (Roehrs et al., 2017;Dagher et al., 2018;Hussein et al., 2018;Guo et al., 2018).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Silva et al., 2019
Previously reported constraints include need for gateway layer for Tangle, limited fog layer storage, semantic interoperability, and incompatibility with legacy systems.
“Some previously reported constraints include the necessity of a gateway layer for Tangle to establish direct communication with sensors (Zheng et al. 2019), the limited storage provided by a fog layer (Silva et al., 2019), as well as semantic interoperability and incompatibility with legacy systems (Zhang et al., 2018).”
From Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda (2020) · cites this paper as Silva et al., 2019
Cites
Guidelines for conducting and reporting case study research in software engineering
Runeson, P., & Höst, M. (2009) · Empirical Software Engineering · doi:10.1007/s10664-008-9102-8 · Cited by 3,862 · see citing works
IoT security: Review, blockchain solutions, and open challenges
Khan, M. A., & Salah, K. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.11.022 · Cited by 2,488 · see citing works
Bitcoin: Economics, Technology, and Governance
Böhme, R., Christin, N., Edelman, B. G., & Moore, T. (2015) · The Journal of Economic Perspectives · doi:10.1257/jep.29.2.213 · Cited by 1,882 · see citing works
Documenting software architectures: views and beyond
Clements, P., Garlan, D., Little, R., Nord, R. L., & Stafford, J. A. (2003) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2003.1201264 · Cited by 1,434 · see citing works
Rahmani, A. M., Gia, T. N., Negash, B., Anzanpour, A., Azimi, I., Jiang, M., & Liljeberg, P. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2017.02.014 · Cited by 1,198 · see citing works
Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare
Farahani, B., Firouzi, F., Chang, V. I. C., Badaroglu, M., Constant, N. P., & Mankodiya, K. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.04.036 · Cited by 991 · see citing works
Case studies for method and tool evaluation
Kitchenham, B. A., Pickard, L. M., & Pfleeger, S. L. (1995) · IEEE Software · doi:10.1109/52.391832 · Cited by 602 · see citing works
Blockchain with Internet of Things: Benefits, Challenges, and Future Directions
Electronic and Computer Science Dept., U. O. S., Atlam, H. F., Alenezi, A., Alassafi, M. O., & Wills, G. B. (2018) · International Journal of Intelligent Systems and Applications · doi:10.5815/ijisa.2018.06.05 · Cited by 377 · see citing works
Fog Computing in Healthcare–A Review and Discussion
Kraemer, F. A., Bråten, A. E., Tamkittikhun, N., & Palma, D. (2017) · IEEE Access · doi:10.1109/access.2017.2704100 · Cited by 335 · see citing works
From Cloud to Fog and IoT-Based Real-Time U-Healthcare Monitoring for Smart Homes and Hospitals
Nandyala, C. S., & Kim, H.-K. (2016) · International Journal of Smart Home · doi:10.14257/ijsh.2016.10.2.18 · Cited by 148 · see citing works
Alsaffar, A. A., Pham, H. P., Hong, C. S., Huh, E., & Aazam, M. (2016) · Mobile Information Systems · doi:10.1155/2016/6123234 · Cited by 95 · see citing works
Chen, X., & Wang, L. (2017) · IEEE Communications Letters · doi:10.1109/lcomm.2016.2647595 · Cited by 65 · see citing works
A residential maintenance-free long-term activity monitoring system for healthcare applications
Fafoutis, X., Tsimbalo, E., Mellios, E., Hilton, G., Piechocki, R., & Craddock, I. (2016) · EURASIP Journal on Wireless Communications and Networking · doi:10.1186/s13638-016-0534-3 · Cited by 34 · see citing works
Personalized Health Tracking with Edge Computing Technologies
Distefano, S., Bruneo, D., Longo, F., Merlino, G., & Puliafito, A. (2016) · BioNanoScience · doi:10.1007/s12668-016-0388-5 · Cited by 6 · see citing works
Zhou, Y., & Zhao, P. (2016) · International Journal of Smart Home · doi:10.14257/ijsh.2016.10.6.19 · Cited by 3 · see citing works
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Tandon, A., Dhir, A., Islam, A. K. M. N., & Mäntymäki, M. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103290 · Cited by 439 · see citing works
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Saeed, H., Malik, H., Bashir, U., Ahmad, A., Riaz, S., Ilyas, M., Bukhari, W. A., & Khan, M. I. A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266462 · Cited by 276 · see citing works
Durneva, P., Cousins, K. C., & Chen, M. M. (2020) · Journal of Medical Internet Research · doi:10.2196/18619 · Cited by 133 · see citing works
Elhoseny, M., Thilakarathne, N. N., Alghamdi, M., Mahendran, R. K., Gardezi, A. A., Weerasinghe, H., & Welhenge, A. M. (2021) · Sustainability · doi:10.3390/su132111645 · Cited by 124 · see citing works
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Kishor, A., Chakraborty, C., & Jeberson, W. (2021) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2020.12.004 · Cited by 94 · see citing works
AlShorman, O., Alshorman, B., Al-khassaweneh, M., & Alkahtani, F. (2020) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v20.i1.pp414-422 · Cited by 84 · see citing works
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Alzoubi, Y. I., Al‐Ahmad, A., & Kahtan, H. (2021) · Computer Communications · doi:10.1016/j.comcom.2021.11.005 · Cited by 76 · see citing works
Mayer, A. H., Rodrigues, V. F., da Costa, C. A., Righi, R. D. R., Roehrs, A., & Antunes, R. S. (2021) · IEEE Access · doi:10.1109/access.2021.3109822 · Cited by 78 · see citing works
Ijaz, M., Li, G., Lin, L., Cheikhrouhou, O., Hamam, H., & Noor, A. (2021) · Electronics · doi:10.3390/electronics10091077 · Cited by 70 · see citing works
Alzoubi, Y. I., Gill, A., & Mishra, A. (2022) · Journal of Cloud Computing · doi:10.1186/s13677-022-00353-y · Cited by 60 · see citing works
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Kim, T. M., Lee, S.-J., Chang, D. J., Koo, J., Kim, T., Yoon, K., & Choi, I. (2021) · Applied Sciences · doi:10.3390/app11041612 · Cited by 41 · see citing works
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Chen, X., Zhu, H., Geng, D., Liu, W., Yang, R., & Li, S. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/2452683 · Cited by 18 · see citing works
RetractedAli, H. M., Bomgni, A. B., Bukhari, S. A. C., Hameed, T., & Liu, J. (2023) · Mobile Networks and Applications · doi:10.1007/s11036-023-02107-9 · Cited by 14 · see citing works
Lopez-Barreiro, J., Sabucedo, L. M. Á., Soidán, J. L. G., & Santos-Gago, J. M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19138129 · Cited by 11 · see citing works
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Dedetürk, B. A., Bakır-Güngör, B., & Soran, A. (2020) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu49456.2020.9302168 · Cited by 10 · see citing works
Singh, C. E. J., & Jagatheeswari, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13719-w · Cited by 9 · see citing works
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- Authors
- Silva, C. A., Aquino, G. S., Melo, S. R. M., & Egídio, D. J. B.
- Affiliations
- Universidade Federal do Rio Grande do Norte (BR)
- Corresponding author
- Silva, Cícero A.
- Venue
- Wireless Communications and Mobile Computing
- Publisher
- Wiley
- Published
- 2019-02-27
- Volume
- 2019
- Pages
- 1-16
- ISSN
- 1530-8669 (ISSN-L)
- 1530-8677
- OpenAlex
- W2917815110
- Cited by
- 50
- References
- 15
- Citations by year
- 2025: 5 · 2024: 5 · 2023: 8 · 2022: 10 · 2021: 13 · 2020: 9
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Wireless Communications and Mobile Computing (publishedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (465614/2014-0, APQ-0399-1.03/17, PRONEX APQ/0388-1.03/14)
- INES (465614/2014-0, APQ-0399-1.03/17, PRONEX APQ/0388-1.03/14)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (465614/2014-0, APQ-0399-1.03/17, PRONEX APQ/0388-1.03/14)
- Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (465614/2014-0, APQ-0399-1.03/17, PRONEX APQ/0388-1.03/14)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- fog computing, medical records management, blockchain, privacy, distributed authorization, fog nodes, home health care, interoperability, healthcare data storage, software architecture
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