IRO Journals
Journal of Trends in Computer Science and Smart Technology

An Asynchronous Tri-Node IoT Architecture for Real-Time Environmental Mitigation in Bedridden Patients

Open Access
Published: 30 May, 2026
Pages: 413-428
  • Nitin Babu Daniel , Nitin Babu Daniel
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Sebastian Terence , Sebastian Terence
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Artificial Intelligence and Machine Learning, Karunya Institute of Technology and Sciences, Coimbatore, India
  • Jude Immaculate Jude Immaculate
    Division of Data Science and Cyber Security, Karunya Institute of Technology and Sciences, Coimbatore, India
    Division of Data Science and Cyber Security, Karunya Institute of Technology and Sciences, Coimbatore, India
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How to Cite

Daniel, Nitin Babu, Sebastian Terence, and Jude Immaculate. 2026. “An Asynchronous Tri-Node IoT Architecture for Real-Time Environmental Mitigation in Bedridden Patients”. Journal of Trends in Computer Science and Smart Technology 8 (2): 413-28. https://doi.org/10.36548/jtcsst.2026.2.011.
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Keywords

Internet of Medical Things (IoMT)
Edge Computing
Asynchronous Architecture
Multi-Modal Biosensing
Real-Time Anomaly Detection
Automated Mitigation

Abstract

Although IoT has enhanced health monitoring among immobile subjects, conventional cloud computing-based frameworks are riddled with perilous network latency, separating biometrics from environmental settings and hindering timely physical intervention. This paper discusses a unique tri-node, asynchronous edge-computing structure that shifts from passive monitoring to proactive, local intervention. Biometric abnormalities are constantly detected by the Smart Glove (Telemetry Node), which includes an ESP32-S3 microcontroller, an MPU-6050 gyroscope, and a CJMCU-6701 galvanic skin response sensor. Concurrently, the Dispenser Node, based on Arduino Nano, evaluates environmental factors using DHT22, GY-302, and MQ-135 sensors and operates as a robotized emergency pillbox driven by servos. The system then initiates a physical mitigation operation by activating exhaust fans, emergency LED lights, and window servos to improve air quality when biometric and environmental anomalies overlap, with an Automation Box, based on Arduino Nano ESP32, serving as the central processing unit. In order to maintain clinical documentation, all data is permanently stored in a centralized database. As a result, the bi-modal paradigm attains a 96.0% detection rate and a latency of only 72 milliseconds.

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