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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68172
Volume:
10 |
10 |
Oct 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
School of Engineering and Technology
Sushant University
School of Engineering and Technology
Sushant University
The Internet of Things (IoT) is transforming conventional healthcare delivery into a proactive, connected and data-driven ecosystem popularly known as Smart Healthcare. By embedding sensors, wearable devices and communication modules into everyday medical objects, IoT enables continuous monitoring of vital physiological parameters such as heart rate, blood pressure, oxygen saturation (SpO2), body temperature and glucose level, without requiring the patient to be physically present at a clinic. This paper presents a comprehensive review and an original three-layer architecture (Perception, Network and Application) for an IoT-based smart healthcare system that integrates edge pre-processing with cloud-based analytics to reduce latency and improve reliability. We survey a set of recent works published between 2018 and 2024, classify them by sensing modality, communication protocol and machine-learning technique, and identify open research gaps such as interoperability, energy efficiency and security. A prototype pipeline combining an ESP32 microcontroller, MAX30100 pulse-oximeter sensor, MQTT protocol and a Random Forest classifier is evaluated on a benchmark vital-sign dataset, achieving 96.7% classification accuracy and an average end-to-end latency of 118 ms, outperforming five baseline architectures compared in this study. The results confirm that a hybrid edge-cloud design offers the best trade-off between accuracy, latency and energy consumption for real-time patient monitoring. Future directions including 6G-enabled healthcare, federated learning for privacy preservation, and blockchain-based health record management are also discussed.
KEYWORDS
This paper presented a comprehensive review of IoT-based smart healthcare systems and proposed a hybrid edge-cloud architecture that addresses the latency and accuracy limitations identified in prior work. Experimental evaluation on a benchmark vital-signs dataset demonstrated that the proposed system achieves 96.7% classification accuracy with an average latency of 118 ms, outperforming cloud-only, fog-based and edge-only baseline designs. These findings confirm that a carefully partitioned edge-cloud pipeline is a practical and scalable approach for real-time remote patient monitoring.
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Publication Date
10/22/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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