» Articles » PMID: 31109073

On Providing Multi-Level Quality of Service for Operating Rooms of the Future

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2019 May 22
PMID 31109073
Citations 2
Authors
Affiliations
Soon will be listed here.
Abstract

The Operating Room (OR) plays an important role in delivering vital medical services to patients in hospitals. Such environments contain several medical devices, equipment, and systems producing valuable information which might be combined for biomedical and surgical workflow analysis. Considering the sensibility of data from sensors in the OR, independently of processing and network loads, the middleware that provides data from these sensors have to respect applications quality of service (QoS) demands. In an OR middleware, there are two main bottlenecks that might suffer QoS problems and, consequently, impact directly in user experience: () simultaneous user applications connecting the middleware; and () a high number of sensors generating information from the environment. Currently, many middlewares that support QoS have been proposed by many fields; however, to the best of our knowledge, there is no research on this topic or the OR environment. OR environments are characterized by being crowded by persons and equipment, some of them of specific use in such environments, as mobile x-ray machines. Therefore, this article proposes QualiCare, an adaptable middleware model to provide multi-level QoS, improve user experience, and increase hardware utilization to middlewares in OR environments. Our main contributions are a middleware model and an orchestration engine in charge of changing the middleware behavior to guarantee performance. Results demonstrate that adapting middleware parameters on demand reduces network usage and improves resource consumption maintaining data provisioning.

Citing Articles

Toward QoS Monitoring in IoT Edge Devices Driven Healthcare-A Systematic Literature Review.

Younas M, Iqbal M, Aziz A, Sodhro A Sensors (Basel). 2023; 23(21).

PMID: 37960584 PMC: 10650388. DOI: 10.3390/s23218885.


The Advent of the Internet of Things in Airfield Lightning Systems: Paving the Way from a Legacy Environment to an Open World.

Buzzoni E, Forlani F, Giannelli C, Mazzotti M, Parisotto S, Pomponio A Sensors (Basel). 2019; 19(21).

PMID: 31683502 PMC: 6864635. DOI: 10.3390/s19214724.

References
1.
Mortazavi B, Desai N, Zhang J, Coppi A, Warner F, Krumholz H . Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures. IEEE J Biomed Health Inform. 2017; 21(6):1719-1729. DOI: 10.1109/JBHI.2017.2675340. View

2.
Dorrell R, Vermillion S, Clark C . Feasibility of real-time location systems in monitoring recovery after major abdominal surgery. Surg Endosc. 2017; 31(12):5457-5462. DOI: 10.1007/s00464-017-5625-7. View

3.
Shi Y, Zhang Y, Jacobsen H, Tang L, Elliott G, Zhang G . Using Machine Learning to Provide Reliable Differentiated Services for IoT in SDN-Like Publish/Subscribe Middleware. Sensors (Basel). 2019; 19(6). PMC: 6471939. DOI: 10.3390/s19061449. View

4.
Kranzfelder M, Zywitza D, Jell T, Schneider A, Gillen S, Friess H . Real-time monitoring for detection of retained surgical sponges and team motion in the surgical operation room using radio-frequency-identification (RFID) technology: a preclinical evaluation. J Surg Res. 2011; 175(2):191-8. DOI: 10.1016/j.jss.2011.03.029. View

5.
Vaccarella A, Comparetti M, Enquobahrie A, Ferrigno G, Momi E . Sensors management in robotic neurosurgery: the ROBOCAST project. Annu Int Conf IEEE Eng Med Biol Soc. 2012; 2011:2119-22. DOI: 10.1109/IEMBS.2011.6090395. View