Cloud-Continuum-Based Deep Learning Optimization Framework for Next-Generation Healthcare Data Performance on IoT Platform

Abstract: The development of healthcare data performance analysis is becoming more driven by the incorporation of intelligent computing paradigms that guarantee real-time, scalable, and personalized feedback for coaches and athletes. However, existing healthcare data analytics systems are challenged with severe issues such as decision-making latency, processing capacity limitations at the edge, data fragmentation, and the inability to integrate across heterogeneous computing environments seamlessly. Athletic data in this scenario refers to a combination of biomechanical factors (motion capture, joint angles, gait patterns), biometric signals (heart rate, oxygen saturation, muscle activity), and sport-specific performance indicators (workload, speed, and acceleration). This paper introduces the Cloud-Continuum-based Deep Learning Optimization Framework (CC-DLOF). This novel architecture leverages the synergistic potential of edge, fog, and cloud computing to provide a dynamic and smart healthcare data performance on an IoT platform. CC-DLOF is a hierarchical continuum architecture, with real-time data gathering and lightweight analytics performed in the edge layer, contextual processing and federated learning in the fog layer, and global intelligence, deep model training, and long-term data storage in the cloud layer. A new Cloud-Fog-Edge Orchestration Device (CFEOD) provides dynamic allocation of computational tasks in terms of latency sensitivity and device capability. At the same time, a blockchain-supported access control is used to maintain data security and privacy. Simulation analysis, done in a simulated training environment that combines with real-world data sets, illustrates the performance of the framework in mitigating latency by 35%, increasing model accuracy by 22%, and boosting system scalability and reliability. CC-DLOF is a revolutionary way in healthcare data technology, leading to smart, responsive, and safe next-generation healthcare data performance on an IoT platform.

G. Aravindh and K. P. Sridhar. “Cloud-Continuum-Based Deep Learning Optimization Framework for Next-Generation Healthcare Data Performance on IoT Platform”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.2 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170262

@article{Aravindh2026,
title = {Cloud-Continuum-Based Deep Learning Optimization Framework for Next-Generation Healthcare Data Performance on IoT Platform},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170262},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170262},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {2},
author = {G. Aravindh and K. P. Sridhar}
}