Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog Environment Article Swipe
Navneet Kumar Rajpoot
,
Prabhdeep Singh
,
Bhaskar Pant
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2975684/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2975684/v1
Smart healthcare systems are novel innovations that can improve healthcare services by giving individuals details about their medical conditions in real time. However, processing and analyzing huge amounts of information stored in these systems can frequently result in latency and interruptions, negatively impacting the system's efficiency. To solve this issue, this research presents an innovative healthcare framework incorporating cloud and fog computing techniques to reduce latency and enhance whole system performance. The framework has three different tiers: the user tier, the fog tier, and the cloud tier. Health-related information is gathered from users in the user tier using cloud pulses. An optimized Ant Colony Optimization load balancing algorithm subsequently monitors this data. The load balancer allocates user requests to fog servers in the fog tier, considering factors including response time and cost. The fog tier is comprised of fog servers that are located in closer proximity to end-users and are tasked with the real-time processing and analysis of data. The cloud tier accommodates vast quantities of healthcare data and facilitates sophisticated analytics and processing functionalities. The performance of the proposed framework was assessed by implementing it on the Cloud Analyst tool and utilizing metrics such as response time, cost, and system throughput. The experiment findings indicate that the proposed framework performs better in mitigating delay in healthcare compared to conventional healthcare systems. The load balancer that was optimized using Ant Colony Optimization algorithm efficiently allocated the workload among the fog servers, resulting in enhanced system response time and decreased latency. The concept of fog computing proved to be highly productive in mitigating latency by enabling local data processing and analysis near the end-user, thereby reducing reliance on the cloud tier. The empirical findings indicate that the framework put forward can transform the provision of healthcare services, rendering them more streamlined, economical, and focused on the needs of patients.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2975684/v1
- https://www.researchsquare.com/article/rs-2975684/latest.pdf
- OA Status
- green
- Cited By
- 10
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4381278897
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4381278897Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2975684/v1Digital Object Identifier
- Title
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Implementation of Delay-Sensitive Smart Healthcare Framework in Cloud and Fog EnvironmentWork title
- Type
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preprintOpenAlex work type
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-30Full publication date if available
- Authors
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Navneet Kumar Rajpoot, Prabhdeep Singh, Bhaskar PantList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2975684/v1Publisher landing page
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https://www.researchsquare.com/article/rs-2975684/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-2975684/latest.pdfDirect OA link when available
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Cloud computing, Computer science, Server, Workload, Latency (audio), Response time, Distributed computing, Throughput, Real-time computing, Computer network, Operating system, Telecommunications, WirelessTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 8Per-year citation counts (last 5 years)
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23Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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