Abstract
Recently, the internet of things (IoT) technologies plays a very important role in various important sectors such as healthcare, education and industry. It has changed the conventional way of diagnosing some diseases and accelerating the check-up process through using IoT medical devices. Many IoT devices are available for measuring the biomarkers such as heart rate, sugar level and blood pressure, etc. However, the privacy of these data collected via IoT medical devices remains a challenge that hinders the use of these devices in clinical practice. The massive data collected by these devices vary in their sensitivity to patients. The more sensitive data requires fast computation and processing to avoid any delay that may occur. The processing of such data in the cloud may lead to operations delay which is needed by a real-time monitoring application. Therefore, this research intends to provide Healthcare Internet of Thing (H-IoT)-based framework for the classification of streamed data according to their criticality level. After the classification, the more crucial data will be computed in fog rather than in the cloud to avoid latency. Future work will extend this work by implementing the proposed framework and evaluating its outcomes.
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Acknowledgements
This work is funded by Omantel under the project code [EG/SQU-OT /1802]. This work is as a part of project title “Internet of Things (IoT) security and privacy aspects related to architecture, connectivity, and collected data”.
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Sarrab, M., Alshohoumi, F. (2022). Assisted Fog Computing Approach for Data Privacy Preservation in IoT-Based Healthcare. In: Abd El-Latif, A.A., Abd-El-Atty, B., Venegas-Andraca, S.E., Mazurczyk, W., Gupta, B.B. (eds) Security and Privacy Preserving for IoT and 5G Networks. Studies in Big Data, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-030-85428-7_8
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