Hybrid Cloud-Edge Computing for Real-Time IoT-Based Health Monitoring: Performance Evaluation and Optimization

Real-time data processing helps doctors and nurses make informed patient care decisions. Original cloud environments have latency and bandwidth issues for handling big data healthcare data. Edge computing streamlines data processing near the source, reduces reaction time, and boosts system scalabili...

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Vydáno v:2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0 s. 1 - 6
Hlavní autoři: Tuteja, Gaurav, Singh, Anmol Rattan, Sunilgoli, G
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 09.04.2025
Témata:
Bandwidth
Cloud computing
Data processing
Distributed databases
Distributed systems
Edge computing
Healthcare data processing
Medical services
Real-time data
Real-time systems
Servers
Throughput
Time factors
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Shrnutí:Real-time data processing helps doctors and nurses make informed patient care decisions. Original cloud environments have latency and bandwidth issues for handling big data healthcare data. Edge computing streamlines data processing near the source, reduces reaction time, and boosts system scalability. An integrated cloud and edge computing paradigm for health data processing is studied in this research using data from distributed edge servers. Edge computing benefits can be quantified by comparing real-time server performance measures including latency, bandwidth, and data throughput. This research shows that edge computing can increase healthcare system data processing throughput and real-time dynamics, especially in jobs that demand near-instantaneity, such as patient monitoring or urgent reaction. Existing barriers to edge security and infrastructure-impacting projects are also mentioned. This research is crucial for healthcare data system researchers and professionals seeking the most efficient use of cloud and edge technologies.
DOI:10.1109/OTCON65728.2025.11071023