Articles Open Access
Cloud-Assisted Internet of Things and Cloud Computing Architecture for Smart Connected Healthcare Monitoring Systems
Jaypee Institute of Information Technology, Lucknow, India
Abstract
The rapid advancement of Internet of Things (IoT) technologies and cloud computing has transformed modern healthcare systems by enabling smart, connected, and real-time patient monitoring solutions. Cloud-assisted IoT healthcare monitoring systems integrate wearable sensors, smart medical devices, wireless communication technologies, and cloud platforms to collect, process, store, and analyze patient health data continuously. These systems improve healthcare quality by providing remote patient monitoring, early disease detection, emergency response, and personalized treatment recommendations. The architecture of cloud-assisted healthcare systems generally includes sensing layers, communication networks, cloud infrastructure, data analytics modules, and application interfaces for healthcare professionals and patients. IoT sensors capture physiological parameters such as heart rate, blood pressure, oxygen saturation, glucose levels, and body temperature, while cloud platforms provide scalable storage, high computational power, and intelligent analytics. Furthermore, artificial intelligence and machine learning techniques enhance predictive healthcare services and decision-making processes. Despite significant advantages such as cost reduction, accessibility, automation, and improved patient outcomes, several challenges remain, including data privacy, cybersecurity threats, interoperability issues, latency, and infrastructure complexity. This study explores the architecture, literature, methodologies, benefits, and limitations of cloud-assisted IoT healthcare monitoring systems and highlights their growing importance in the development of next-generation smart healthcare environments.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 1 |
| Number | 2 |
| Pages | 361-370 |
| Published | 2017-09-05 |
| Issue | Vol. 1 No. 2 (2017): International Research Journal of Innovative Engineering (IRJIE) |
References
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2. Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54(15), 2688–2710.
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4. Miorandi, D., Sicari, S., De Pellegrini, F., & Chlamtac, I. (2012). Internet of Things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497–1516.
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7. Botta, A., De Donato, W., Persico, V., & Pescapé, A. (2016). Integration of cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684–700.
8. Hossain, M. S., & Muhammad, G. (2016). Cloud-assisted Industrial Internet of Things (IIoT)-enabled framework for health monitoring. Computer Networks, 101, 192–202.
9. Yang, Z., Zhou, Q., Lei, L., Zheng, K., & Xiang, W. (2016). An IoT-cloud based wearable ECG monitoring system for smart healthcare. Journal of Medical Systems, 40(12), 286.
10. Villari, M., Celesti, A., Fazio, M., Dustdar, S., & Rana, O. (2016). Leveraging the Internet of Things and cloud computing for smart healthcare systems. International Journal of Distributed Sensor Networks, 12(7), 1–12.
11. Li, S., Xu, L. D., & Zhao, S. (2015). The Internet of Things: A survey. Information Systems Frontiers, 17(2), 243–259.
12. Xu, L. D., He, W., & Li, S. (2014). Internet of Things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243.
13. Doukas, C., & Maglogiannis, I. (2012). Bringing IoT and cloud computing towards pervasive healthcare. IEEE International Conference on Innovative Mobile and Internet Services, 922–926.
14. Zhang, Y., & Liu, J. (2010). Novel healthcare architecture integrating cloud computing and wireless sensor networks. Journal of Network and Computer Applications, 35(1), 260–267.
15. Chen, M., Ma, Y., Song, J., Lai, C. F., & Hu, B. (2016). Smart clothing: Connecting human with clouds and big data for sustainable health monitoring. Mobile Networks and Applications, 21(5), 825–845.
16. Rao, S., & Selvamani, K. (2015). Data security challenges and its solutions in cloud computing. Procedia Computer Science, 48, 204–209.
17. Stergiou, C., Psannis, K. E., Kim, B. G., & Gupta, B. (2016). Secure integration of IoT and cloud computing. Future Generation Computer Systems, 78, 964–975.
18. Dinh, H. T., Lee, C., Niyato, D., & Wang, P. (2013). A survey of mobile cloud computing: Architecture, applications, and approaches. Wireless Communications and Mobile Computing, 13(18), 1587–1611.
19. Roman, R., Zhou, J., & Lopez, J. (2013). On the features and challenges of security and privacy in distributed Internet of Things. Computer Networks, 57(10), 2266–2279.
20. Wang, L., Laszewski, G., Younge, A., He, X., Kunze, M., Tao, J., & Fu, C. (2010). Cloud computing: A perspective study. New Generation Computing, 28(2), 137–146.
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26. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
27. Kumar, J. (2013). Preservation of the Privacy for Multiple Custodian Systems with Rule Sharing. Journal of Computer Science.
28. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
29. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
30. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
31. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
32. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
33. Jagadeesh, S., & Soundappan, R. S. (2014). Survey on knowledge discovery in speech emotion detection. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4476-4481.
34. Murugeshwari, B., & Sujatha, R. (2014). Preservation of Privacy for Multiparty Computation System with Homomorphic Encryption. International Journal of Emerging Technology and Advanced Engineering, 4(3), 530-535.
35. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
36. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
37. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
38. G. Vimal Raja, K. K. Sharma (2015). Applying Clustering technique on Climatic Data. Envirogeochimica Acta, 2(1), 21-27.
39. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
40. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
41. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
42. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
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