Smart Healthcare Monitoring through Edge Intelligence

  • Authors

    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India. Author
    • H. N. Mahabala Computer Scientist, Tata Institute of Fundamental Research, India. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-2019PI2T2F

    Published 02-04-2019

  • Smart Healthcare, Edge Intelligence, Edge Computing, Artificial Intelligence, Internet of Things, Machine Learning, Remote Patient Monitoring, Wearable Sensors, Healthcare Analytics, Biomedical Signal Processing, Cloud Computing, Intelligent Healthcare Systems

    Issue

    Vol. 2 No. 1 (2019)

    Section

    Articles

    How to Cite

    [1]
    S. N and M. H. N, “Smart Healthcare Monitoring through Edge Intelligence”, ijmiet, vol. 2, no. 1, pp. 01–16, Feb. 2019, doi: 10.67228/30715628/IJMIET-2019PI2T2F.

  • Abstract

    Digital healthcare technologies have significantly improved patient monitoring and disease prediction; however, conventional cloud-based healthcare systems face challenges such as communication latency, bandwidth consumption, privacy concerns, and delayed clinical responses. This study proposes a Smart Healthcare Monitoring through Edge Intelligence framework that integrates Artificial Intelligence (AI), Edge Computing, the Internet of Things (IoT), and Machine Learning (ML) to enable real-time and secure healthcare services. The proposed architecture employs wearable sensors to continuously monitor vital physiological parameters, including heart rate, ECG, blood oxygen saturation (SpO₂), body temperature, blood pressure, respiratory rate, glucose level, and physical activity. Medical data is processed locally at edge devices for noise removal, anomaly detection, risk assessment, and selective cloud synchronization. AI-based edge analytics support early disease prediction, personalized healthcare recommendations, and rapid emergency alerts while reducing communication overhead and protecting patient privacy. The proposed framework demonstrates improved monitoring accuracy, lower response latency, enhanced network efficiency, better data security, and reliable healthcare decision-making. It provides a scalable and intelligent solution for telemedicine, remote patient monitoring, and next-generation smart healthcare systems.

  • References

    [1] J. Jara, M. A. Zamora, and A. F. Skarmeta, "An Internet of Things–Based Personal Device for Diabetes Therapy Management in Ambient Assisted Living," Personal and Ubiquitous Computing, vol. 15, no. 4, pp. 431–440, 2011.

    [2] D. Bandyopadhyay and J. Sen, "Internet of Things: Applications and Challenges in Technology and Standardization," Wireless Personal Communications, vol. 58, no. 1, pp. 49–69, 2011.

    [3] M. Chen, Y. Ma, J. Song, C. Lai, and B. Hu, "Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring," Mobile Networks and Applications, vol. 21, no. 5, pp. 825–845, 2016.

    [4] S. Yi, C. Li, and Q. Li, "A Survey of Fog Computing: Concepts, Applications and Issues," in Proceedings of the ACM Workshop on Mobile Big Data, 2015, pp. 37–42.

    [5] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, "Edge Computing: Vision and Challenges," IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, Oct. 2016.

    [6] F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, "Fog Computing and Its Role in the Internet of Things," in Proceedings of the MCC Workshop on Mobile Cloud Computing, 2012, pp. 13–16.

    [7] M. Satyanarayanan, "The Emergence of Edge Computing," Computer, vol. 50, no. 1, pp. 30–39, Jan. 2017.

    [8] Rahmani et al., "Exploiting Smart e-Health Gateways at the Edge of Healthcare Internet-of-Things," Future Generation Computer Systems, vol. 78, pp. 641–658, 2018.

    [9] H. Alemdar and C. Ersoy, "Wireless Sensor Networks for Healthcare: A Survey," Computer Networks, vol. 54, no. 15, pp. 2688–2710, 2010.

    [10] G. R. Reddy and P. K. Reddy, "A Survey on Wearable Sensor-Based Healthcare Monitoring Systems," Journal of Medical Systems, vol. 42, no. 11, pp. 1–20, 2018.

    [11] J. Konečný, H. B. McMahan, D. Ramage, and P. Richtárik, "Federated Optimization: Distributed Machine Learning for On-Device Intelligence," arXiv preprint arXiv:1610.02527, 2016.

    [12] McMahan et al., "Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of AISTATS, 2017, pp. 1273–1282.

    [13] K. Zhang, Y. Mao, S. Leng, A. Vinel, and Y. Zhang, "Delay Constrained Offloading for Mobile Edge Computing in Cloud-Enabled Vehicular Networks," in Proceedings of IEEE ICC, 2016, pp. 1–6.

    [14] K. Fan, S. Wang, Y. Ren, H. Li, and Y. Yang, "MedBlock: Efficient and Secure Medical Data Sharing via Blockchain," Journal of Medical Systems, vol. 42, no. 8, pp. 136–145, 2018.

    [15] S. R. Islam, D. Kwak, M. H. Kabir, M. Hossain, and K. Kwak, "The Internet of Things for Health Care: A Comprehensive Survey," IEEE Access, vol. 3, pp. 678–708, 2015.

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