TY - JOUR
T1 - RDSF—Responsive Data-Sharing Framework for User-Centric Internet of Vehicles Assisted Healthcare Systems
AU - Thota, Chandu
AU - Mavromoustakis, Constandinos X.
AU - Mastorakis, George
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023
Y1 - 2023
N2 - Internet of Vehicles (IoV) assisted healthcare systems are designed for providing reliable and dynamic medical assistance for the connected users/ patients. The healthcare systems inherit the intelligent computing models in Internet of Vehicles (IoV) for processing user requests and allocating medical data. In this manuscript, the problem is due to stagnancy in request processing and latency in response dissemination, which is addressed using a responsive data-sharing framework (RDSF). This framework is designed for addressing the afore-mentioned issues in healthcare data searching and allocation. The proposed framework performs searching, organizing, and data allocation without stagnancy in request processing. In this processing, K-means clustering is deployed to differentiate and classify the allocation and searching instances and interrupt in different time intervals. The proposed framework identifies the interrupting instances for improving the response allocation ratio. The linearization of the classification and allocation of the clustering process reduces the complexity in handling healthcare data. The proposed framework’s performance shows that it is capable of improving data handling rate by reducing complexity, latency, and request failures. The proposed EDSF increases data response by 12.8%, reduces response latency and interrupt complexity by 7.65% and 10.55% respectively.
AB - Internet of Vehicles (IoV) assisted healthcare systems are designed for providing reliable and dynamic medical assistance for the connected users/ patients. The healthcare systems inherit the intelligent computing models in Internet of Vehicles (IoV) for processing user requests and allocating medical data. In this manuscript, the problem is due to stagnancy in request processing and latency in response dissemination, which is addressed using a responsive data-sharing framework (RDSF). This framework is designed for addressing the afore-mentioned issues in healthcare data searching and allocation. The proposed framework performs searching, organizing, and data allocation without stagnancy in request processing. In this processing, K-means clustering is deployed to differentiate and classify the allocation and searching instances and interrupt in different time intervals. The proposed framework identifies the interrupting instances for improving the response allocation ratio. The linearization of the classification and allocation of the clustering process reduces the complexity in handling healthcare data. The proposed framework’s performance shows that it is capable of improving data handling rate by reducing complexity, latency, and request failures. The proposed EDSF increases data response by 12.8%, reduces response latency and interrupt complexity by 7.65% and 10.55% respectively.
KW - Data Classification
KW - Data Sharing
KW - Healthcare Systems
KW - Internet of Vehicles (IoV)
KW - K-Means Clustering
UR - http://www.scopus.com/inward/record.url?scp=85146647107&partnerID=8YFLogxK
U2 - 10.1007/s11042-023-14387-0
DO - 10.1007/s11042-023-14387-0
M3 - Article
AN - SCOPUS:85146647107
SN - 1380-7501
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
ER -