LMRPID-397482
Page 35
1st August 2024
Development of Low-Power Edge Computing Architectures for Real-Time Healthcare Monitoring Using IoT and Machine Learning Algorithms
Author: ABDULLA AL JEHAN | LGMID: 27199320190101961
Reviewed by:
Lilac institute of technology (LIT)
Paper preview
1. Abstract
2. Introduction
3. Literature Review
4. Methodology
5. Findings
6. Conclusion
7. References
Abstract
The rapid advancement of Internet of Things (IoT) technologies has transformed healthcare delivery, with an increasing emphasis on real-time monitoring through wearable sensors and smart medical devices. However, the reliance on cloud computing introduces latency, bandwidth limitations, and privacy concerns. This study focuses on the development of low-power edge computing architectures that address these challenges by enabling local data processing and intelligent analytics directly on the edge devices. We propose an energy-efficient, scalable, and autonomous edge framework that integrates IoT with machine learning algorithms for real-time healthcare monitoring. Our approach includes implementing predictive analytics and anomaly detection at the edge to ensure continuous and personalized care for patients. Special attention is given to optimizing computational resource management through dynamic scheduling algorithms such as heuristic-based scheduling and backfilling. These strategies are designed to maintain low energy consumption without compromising the performance of critical healthcare applications. The proposed architecture was evaluated using real-world healthcare scenarios such as remote patient monitoring for chronic illnesses, including diabetes and cardiovascular diseases. The system demonstrated strong potential for reducing latency, improving responsiveness, and supporting uninterrupted monitoring. Additionally, we address the critical issue of data privacy and security, offering techniques that align with healthcare regulations and data protection standards. This work highlights how combining IoT, machine learning, and edge computing can lead to powerful, self-sufficient healthcare systems that minimize dependency on central cloud services. Our findings suggest that such architectures not only reduce the burden on healthcare infrastructure but also provide timely, intelligent interventions, which are essential for saving lives and improving patient outcomes. The paper concludes by exploring future improvements in AI model adaptability, enhanced system scalability, and interoperability with existing healthcare platforms.
References
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Keywords
Edge Computing, Internet of Things (IoT), Real-Time Healthcare, Monitoring, Machine Learning, Low-Power Systems
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