An IoT Healthcare System Based on Fog Computing and Data Mining: A Diabetic Use Case
Source abstract
Abstract
The advent of the Internet of Things (IoT) has revolutionized numerous sectors, with healthcare being particularly significant. Despite extensive studies addressing healthcare challenges, two persist: (1) the need for the swift detection of abnormalities in patients under medical care and timely notifications to patients or caregivers and (2) the accurate diagnosis of abnormalities tailored to the patient’s condition. Addressing these challenges, numerous studies have focused on developing healthcare systems, leveraging technologies like edge computing, which plays a pivotal role in enhancing system efficiency. Fog computing, situated at the edge of network hierarchies, leverages multiple nodes to expedite system processes. Furthermore, the wealth of data generated by sensors connected to patients presents invaluable insights for optimizing medical care. Data mining techniques, in this context, offer a means to enhance healthcare system performance by refining abnormality notifications and disease analysis. In this study, we present a system utilizing the K-Nearest Neighbor (KNN) algorithm and Raspberry Pi microcomputer within the fog layer for a diabetic patient data analysis. The KNN algorithm, trained on historical patient data, facilitates the real-time assessment of patient conditions based on past vital signs. A simulation using an IBM SPSS dataset and real-world testing on a diabetic patient demonstrate the system’s efficacy. The results manifest in prompt alerts or normal notifications, illustrating the system’s potential for enhancing patient care in healthcare settings.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: IoT and Edge/Fog Computing · Artificial Intelligence in Healthcare · Context-Aware Activity Recognition Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of IT, Azad University of North Tehran Branch, Tehran 1876, Iran
Faculty of IT, Crown Institute of Higher Education (CIHE), Sydney 2060, Australia
Funding
CROWN INSTITUTE of HIGHER EDUCATION, Sydney, Australia
References (33)
Show source-provided references
1. Tyagi, S., Agarwal, A., and Maheshvari, P. (2016, January 14–15). A conceptual framework for IOT based healthcare system using cloud computing Cloud System and Big Data Engineering (Confluence). Proceedings of the 2016 6th International Conference, Noida, India.10.1109/CONFLUENCE.2016.7508172 ↗
2. Atzori, L., Iera, A., and Morabito, G. (2016). Understanding the Internet of Things: Definition, Potentials, and Societal Role of a Fast Evolving Paradigm, Elsevier.10.1016/j.adhoc.2016.12.004 ↗
3. Minerva, R., Biru, A., and Rotondi, D. (2015). Towards a Definition of the Internet of Things, IEEE.
4. Smart Data Pricing Models for the Internet of Things: A Bundling Strategy Approach10.1109/MNET.2016.7437020 ↗
5. The Internet of Things for Health Care A Comprehensive Survey10.1109/ACCESS.2015.2437951 ↗
6. Pulimamidi, R. (2024). To Enhance Customer (or Patient) Experience Based on IoT Analytical Study through Technology (IT) Transformation for E-Healthcare, Elsevier.10.1016/j.measen.2024.101087 ↗
7. Aazam, M., and Huh, E.-N. (2015, January 23–27). EHAMC: Leveraging Fog Computing for Emergency Alert Service, Pervasive Computing and Communication Workshops (PerCom Workshops). Proceedings of the 2015 IEEE International Conference, St. Louis, MO, USA.10.1109/PERCOMW.2015.7134091 ↗
8. Ullah, A., and Yasin, S. (2024). Latency Aware Smart Health Care System Using Edge and Fog Computing, Springer.10.1007/s11042-024-19853-x ↗
9. Fog Computing in Healthcare A Review and Discussion10.1109/ACCESS.2017.2704100 ↗
10. Byers Architectural Imperatives for Fog Computing Techniques for Fog-Enabled IoT Networks10.1109/MCOM.2017.1600885 ↗
11. Gia, T.N., Rahmani, M.J.A., Westerlund, T., and Tenhunen, H. (2015, January 26). FogComputing in Healthcare Internet of Things: ACase Study on ECG Feature Extraction. Proceedings of the Computer and Information Technology; Ubiquitous Computing and Communications, 2015 IEEE International Conference, Liverpool, UK.10.1109/CIT/IUCC/DASC/PICOM.2015.51 ↗
12. Data Mining for the Internet of Things Literature Review and Challenges10.1155/2015/431047 ↗
13. Zamani, A.S., Hashim, A.H.A., and Shatat, A.S.A. (2024). Implementation of Machine Learning Techniques with Big Data and IoT to Create Effective Prediction Models for Health Informatics, Elsevier.10.1016/j.bspc.2024.106247 ↗
14. Chen, M., Ma, Y., Song, J., Lai, C., and Hu, B. (2016). Smart Clothing: Connecting Human with Clouds and BigData for Sustainable Health Monitoring, Springer.10.1007/s11036-016-0745-1 ↗
15. Yang, Y., Qi, Q., Hu, J., Dai, J., and Yang, C. (2023). Adaptive Fault-tolerant Control for Consensus of Nonlinear Fractional Order Multi-Agent systems with diffusion. Fractal Fract., 7.10.3390/fractalfract7100760 ↗
16. Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data10.1109/JSYST.2015.2460747 ↗
17. Disease Prediction by Machine Learning Over Big Data from Healthcare Communities10.1109/ACCESS.2017.2694446 ↗
18. Oryema, B., Kim, H.-S., Li, W., and Park, J.T. (2017, January 8–11). Design and Implementation of an Interoperable Messaging System for IoT Healthcare Services. Proceedings of the Consumer Communications & Networking Conference (CCNC), 2017 14th IEEE Annual, Las Vegas, NV, USA.10.1109/CCNC.2017.7983080 ↗
19. A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications10.1109/ACCESS.2017.2685434 ↗
20. Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing10.1145/2677046.2677052 ↗
21. Habibi, P., Farhoudi, M., Kazemian, S., Khorsandi, S., and Leon-Garcia, A. (2020). Fog Computing: A Comprehensive Architectural Survey, IEEE.10.1109/ACCESS.2020.2983253 ↗
22. Fog and IoT: An Overview of Research Opportunities10.1109/JIOT.2016.2584538 ↗
23. Kaur, J., Verma, R., Alharbe, N.R., and Agrawal, A. (2020). Importance of Fog Computing in Healthcare 4.0, Springer.10.1007/978-3-030-46197-3_4 ↗
24. Bibani, O., Mouradian, C., Yangui, S., and Glitho, R.H. (2016, January 12–15). A Demo of IoT Healthcare Application Provisioning in Hybrid Cloud/Fog Environment, Cloud Computing Technology and Science (CloudCom). Proceedings of the 2016 IEEE International Conference, Luxembourg.10.1109/CloudCom.2016.0081 ↗
25. Amir, M., Gia, R.T.N., and Negash, B. (2017). Exploiting Smart E-Health Gateways at the Edge of Healthcare Internet-of-Things: A Fog Computing Approach, Elsevier.
26. Liu, Y., Bi, J., and Fan, Z. (2017). MultiClass Sentiment Classification: The Experimental Comparison of Feature Selection and Machine Learning Algorithm, Elsevier.10.1016/j.eswa.2017.03.042 ↗
27. A Survey on NetworkMethodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues10.1109/COMST.2017.2694469 ↗
28. Abosinnee, A.S., Issa, S.S., Alkhayyat, A., Naser, Z.L., Ramadan, G.M., and Jaffer, Z. (2023). Probabilistic Neural Network Based Visual Data Mining for Healthcare Sector, IEEE.10.1109/IICETA57613.2023.10351287 ↗
29. Rastogi, R., and Bansal, M. (2023). Diabetes Prediction Model Using Data Mining Techniques, Elsevier.10.1016/j.measen.2022.100605 ↗
30. Robot Assistant in Management of Diabetes in Children Based on the Internet of Things10.1109/JIOT.2016.2623767 ↗
31. Al-Taee, M.A., Al-Nuaimy, W., and Al-Ataby, A. (2015, January 3–5). Mobile Health platform for diabetes management based on Internet of Things Applied Electrical Engineering and Computing Technologies (AEECT). Proceedings of the 2015 IEEE Jordan Conference, Amman, Jordan.10.1109/AEECT.2015.7360551 ↗
32. Jara, A.J. (2011). An Internet of Things–Based Personal Device for Diabetes Therapy Management in Ambient Assisted Living (AAL), Springer.10.1007/s00779-010-0353-1 ↗
33. A context-aware interactive M-health system for Diabetics