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. 2025 Aug 28;20(8):e0330695.
doi: 10.1371/journal.pone.0330695. eCollection 2025.

Energy-efficient communication between IoMT devices and emergency vehicles for improved patient care

Radwa Ahmed Osman  1
Affiliations

Energy-efficient communication between IoMT devices and emergency vehicles for improved patient care

Radwa Ahmed Osman. PLoS One. .

Abstract

The rising integration of emergency healthcare services with the Internet of Medical Things (IoMT) creates a significant opportunity to improve real-time communication between patients and emergency vehicles like ambulances. Fast and reliable data interchange is crucial in an emergency, especially for those with chronic conditions who rely on wearable IoMT devices to monitor vital health signs. However, establishing consistent communication in real-world conditions such as restricted signal strength, changing distances, and power constraints remains a major difficulty. This paper provides an intelligent communication framework that uses a one-dimensional deep convolutional neural network (1D-CNN) and Lagrange optimization techniques to improve energy efficiency and data transmission speeds. Unlike many earlier models, our technique takes into consideration real-world characteristics such as signal-to-interference-plus-noise ratio (SINR), transmission power, and the distance between the ambulance and the patient's device. The primary goal is to identify the ideal communication distance for dependable, energy-efficient data transfer during urgent emergency situations. The findings show that the suggested system enhances communication reliability, consumes less energy, and increases the possible data rate. This framework accelerates, smartens, and strengthens emergency healthcare communication systems by combining deep learning and mathematical optimization. These findings contribute to the progress of intelligent healthcare infrastructure, opening the way for responsive and dependable emergency services that can adapt to changing conditions while maintaining high performance and patient safety.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Proposed model.
Fig 2
Fig 2. Pearson correlation coefficients of each input parameter (dCB, dDD, dVV, SINRth, PI, PC, PD and PV) and the output (dIA, EE and R).
Fig 3
Fig 3. Proposed deep learning model.
Fig 4
Fig 4. Training and validation mean absolute error generated during training the proposed model.
Fig 5
Fig 5. Transmission distance between any interfere transmitter and its receiver and required transmission distance between p-IoT and EV (dIA).
Fig 6
Fig 6. Transmission distance between any interfere transmitter and its receiver vs Overall system energy efficiency (EE).
Fig 7
Fig 7. Transmission distance between any interfere transmitter and its receiver vs Overall system achievable data rate (R).
Fig 8
Fig 8. p-IoT transmission power (PI) vs required transmission distance between p-IoT and EV (dIA).
Fig 9
Fig 9. p-IoT transmission power (PI) vs Overall system energy efficiency (EE).
Fig 10
Fig 10. p-IoT transmission power (PI) vs Overall system achievable data rate (R).
Fig 11
Fig 11. p-IoT transmission power (PI) vs Overall energy efficiency (EE).
Fig 12
Fig 12. Error distribution histograms for each output parameter: (a) dIA, (b) EE, and (c) R.
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