An SDN-enabled fog computing framework for wban applications in the healthcare sector
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10254922" target="_blank" >RIV/61989100:27240/24:10254922 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S254266052400088X?via%3Dihub#sec0011" target="_blank" >https://www.sciencedirect.com/science/article/pii/S254266052400088X?via%3Dihub#sec0011</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.iot.2024.101150" target="_blank" >10.1016/j.iot.2024.101150</a>
Alternative languages
Result language
angličtina
Original language name
An SDN-enabled fog computing framework for wban applications in the healthcare sector
Original language description
For healthcare systems utilizing Wireless Body Area Networks (WBANs), maintaining the network's diverse Quality of Service (QoS) metrics necessitates effective communication among Fog Computing resources. While fog nodes efficiently handle local requests with substantial processing resources, it is crucial to acknowledge the unpredictable availability of these nodes, potentially resulting in a decline in system performance. This study explores a software-defined fog architecture supporting different healthcare applications in Internet of Things (IoT) environment to ensure consistent specialized medical care amidst evolving health issues. Even minor delays, packet losses, or network overhead could adversely affect patient health. The article establishes a mathematical foundation based on transmitted and sensed data, ensuring each fog node executes an ideal quantity of processes. This study formulates an optimization problem to maximize the utility of fog nodes, leveraging the Lagrangian approach and Karush-Kuhn-Tucker conditions to streamline and resolve the optimization problem. Performance analysis demonstrates a significant reduction in delays by approximately 38 %, 29 %, and 32 %, along with energy savings of roughly 26.89 %, 12.16 %, and 22.50 %, compared to benchmark approaches. This study holds promise in healthcare, cloud-fog simulation, and WBANs, emphasizing the critical need for swift and accurate data processing. (C) 2024 Elsevier B.V.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20200 - Electrical engineering, Electronic engineering, Information engineering
Result continuities
Project
—
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Internet of Things
ISSN
2543-1536
e-ISSN
2542-6605
Volume of the periodical
26
Issue of the periodical within the volume
101150
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
15
Pages from-to
—
UT code for WoS article
001224457800001
EID of the result in the Scopus database
2-s2.0-85187383792