Healthcare sensor data management on the cloud
Tsiachri Renta, P. and Sotiriadis, Stelios and Petrakis, E.G.M. (2017) Healthcare sensor data management on the cloud. In: UNSPECIFIED (ed.) ARMS-CC '17 Proceedings of the 2017 Workshop on Adaptive Resource Management and Scheduling for Cloud Computing. New York, U.S.: ACM, pp. 25-30. ISBN 9781450351164.
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Abstract
The quality of medical services can be significantly improved by supporting health care procedureswith new technologies such as Cloud computing and Internet of Things (IoTs). The need to monitor patient's health remotely and in real time becomes more and more a vital requirement, especially for chronic patients and elderly. In this work, we focus on the management of health care related data stored on the Cloud produced by Bluetooth low energy devices. We present a Cloud based IoT Management System that collects vital user data (e.g. cardiac pulse rate and blood oxygen saturation) on real time. Our solution enables sensor data collection and processing fast and efficient, while users such as medical personnel can subscribe to patient’s data and get notifications. The system is designed based on microservices and includes a notification service for both health care providers and patients minimizing the risk of late response to emergency conditions. Alerts are produced according to predefined rules and on patient specific reaction plans. We present an experimental study where we evaluate our system based on real world sensors, while we generate a synthetic dataset for simulating thousands of users. The results are prosperous, as the system responds close to real time even under heavy loads binding to the limits of the web server that receives the service request. The heaviest workload simulates 2000 user requests (while 80 are executed concurrently) is completed in less than 13 seconds when the system deployed in a virtual machine of 2GB RAM, 1 VCPU and 20GB Disk.
Metadata
| Item Type: | Book Section |
|---|---|
| Keyword(s) / Subject(s): | cloud computing, internet of things |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Birkbeck Knowledge Lab |
| Depositing User: | Stelios Sotiriadis |
| Date Deposited: | 02 May 2018 13:52 |
| Last Modified: | 09 Aug 2023 12:43 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/21803 |
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