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Oueida, S, Kotb, Y, Aloqaily, M, Jararweh, Y and Baker, T (2018) An Edge Computing Based Smart Healthcare Framework for Resource Management. Sensors, 18 (12). ISSN 1424-2818
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An Edge Computing Based Smart Healthcare Framework for Resource Management.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
Abstract
The revolution in information technologies, and the spread of the Internet of Things (IoT) and smart city industrial systems, have fostered widespread use of smart systems. As a complex, 24/7 service, healthcare requires efficient and reliable follow-up on daily operations, service and resources. Cloud and edge computing are essential for smart and efficient healthcare systems in smart cities. Emergency departments (ED) are real-time systems with complex dynamic behavior, and they require tailored techniques to model, simulate and optimize system resources and service flow. ED issues are mainly due to resource shortage and resource assignment efficiency. In this paper, we propose a resource preservation net (RPN) framework using Petri net, integrated with custom cloud and edge computing suitable for ED systems. The proposed framework is designed to model non-consumable resources and is theoretically described and validated. RPN is applicable to a real-life scenario where key performance indicators such as patient length of stay (LoS), resource utilization rate and average patient waiting time are modeled and optimized. As the system must be reliable, efficient and secure, the use of cloud and edge computing is critical. The proposed framework is simulated, which highlights significant improvements in LoS, resource utilization and patient waiting time.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0301 Analytical Chemistry, 0906 Electrical And Electronic Engineering |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computer Science & Mathematics |
| Publisher: | MDPI AG |
| Date Deposited: | 06 Dec 2018 10:31 |
| Last Modified: | 04 Sep 2021 09:53 |
| DOI or ID number: | 10.3390/s18124307 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/9775 |
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