Insights into BLE 5.x Data Transfer Modes for IoT-Empowered Remote Patient Monitoring

Remote Patient Monitoring (RPM) systems are transforming healthcare by enabling continuous health data collection and providing real-time insights for better patient care. This article explores the potential of Bluetooth Low Energy (BLE) technology for IoT-based RPM. We provide a comprehensive overview of the various data transfer modes in the latest BLE 5.x versions, including legacy, connection, extended, periodic, periodic with response and LE audio broadcast modes. Each mode's operational characteristics and implications for RPM are discussed, with an emphasis on the trade-offs between data transfer and energy consumption. Using real-time experimental measurements, we analyze current consumption across these modes, offering insights into their suitability for tracking physical activities using motion sensor data. The extended mode transmits data 98.21% and 83.14% more efficiently than the traditional legacy and connection modes, respectively, by requiring fewer events. Periodic advertising demonstrates a 96.17%, 96.28%, and 89.02% reduction in energy usage compared to legacy, connection, and extended modes, respectively. This study also pioneers a current consumption comparison between classic BLE and the latest LE Audio mode. Additionally, this work introduces PAwR as an innovative approach to RPM, a concept that has not been explored in existing research. While LE Coded PHY mode excels in long-range data delivery, LE 2M mode significantly reduces current usage by 91.22%. Our findings highlight BLE's potential for data transfer while maintaining energy efficiency, supporting the evolution of wearable BLE beacons for e-healthcare.

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Publication Details

Journal
IEEE Internet of Things Magazine
Published
2025-06-24
DOI
https://doi.org/10.1109/iotm.001.2400267
Primary Topic
IoT and Edge/Fog Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Insights into BLE 5.x Data Transfer Modes for IoT-Empowered Remote Patient Monitoring

Ridhima Verma, Suman Kumar
1 links
IEEE Internet of Things Magazine
IoT and Edge/Fog Computing
article

Insights into BLE 5.x Data Transfer Modes for IoT-Empowered Remote Patient Monitoring

Ridhima Verma, Suman Kumar
article en

Abstract

Remote Patient Monitoring (RPM) systems are transforming healthcare by enabling continuous health data collection and providing real-time insights for better patient care. This article explores the potential of Bluetooth Low Energy (BLE) technology for IoT-based RPM. We provide a comprehensive overview of the various data transfer modes in the latest BLE 5.x versions, including legacy, connection, extended, periodic, periodic with response and LE audio broadcast modes. Each mode's operational characteristics and implications for RPM are discussed, with an emphasis on the trade-offs between data transfer and energy consumption. Using real-time experimental measurements, we analyze current consumption across these modes, offering insights into their suitability for tracking physical activities using motion sensor data. The extended mode transmits data 98.21% and 83.14% more efficiently than the traditional legacy and connection modes, respectively, by requiring fewer events. Periodic advertising demonstrates a 96.17%, 96.28%, and 89.02% reduction in energy usage compared to legacy, connection, and extended modes, respectively. This study also pioneers a current consumption comparison between classic BLE and the latest LE Audio mode. Additionally, this work introduces PAwR as an innovative approach to RPM, a concept that has not been explored in existing research. While LE Coded PHY mode excels in long-range data delivery, LE 2M mode significantly reduces current usage by 91.22%. Our findings highlight BLE's potential for data transfer while maintaining energy efficiency, supporting the evolution of wearable BLE beacons for e-healthcare.

IEEE Internet of Things MagazineVol. 8(4)
Indian Institute of Technology Ropar (IN)
Indian Institute of Technology Ropar
Openalex Percentile: Top 84%
IoT and Edge/Fog Computing
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