ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring
Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the “ScalableDigitalHealth” (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a nove...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/4/1346 |
| _version_ | 1797296981764210688 |
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| author | Hisham Alasmary |
| author_facet | Hisham Alasmary |
| author_sort | Hisham Alasmary |
| collection | DOAJ |
| description | Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the “ScalableDigitalHealth” (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the “SDH” enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing’s proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage. |
| first_indexed | 2024-03-07T22:13:39Z |
| format | Article |
| id | doaj.art-10f77cd2ca7242b6a4248db463ba6d9e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:13:39Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-10f77cd2ca7242b6a4248db463ba6d9e2024-02-23T15:34:14ZengMDPI AGSensors1424-82202024-02-01244134610.3390/s24041346ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient MonitoringHisham Alasmary0Department of Computer Science, College of Computer Science, King Khalid University, Abha 61421, Saudi ArabiaAddressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the “ScalableDigitalHealth” (SDH) framework. The framework integrates smart digital health solutions with latency-aware edge computing autoscaling, providing a novel approach to remote patient monitoring. By leveraging IoT technology and application autoscaling, the “SDH” enables the real-time tracking of critical health parameters, such as ECG, body temperature, blood pressure, and oxygen saturation. These vital metrics are efficiently transmitted in real time to AWS cloud storage through a layered networking architecture. The contributions are two-fold: (1) establishing real-time remote patient monitoring and (2) developing a scalable architecture that features latency-aware horizontal pod autoscaling for containerized healthcare applications. The architecture incorporates a scalable IoT-based architecture and an innovative microservice autoscaling strategy in edge computing, driven by dynamic latency thresholds and enhanced by the integration of custom metrics. This work ensures heightened accessibility, cost-efficiency, and rapid responsiveness to patient needs, marking a significant leap forward in the field. By dynamically adjusting pod numbers based on latency, the system optimizes system responsiveness, particularly in edge computing’s proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.https://www.mdpi.com/1424-8220/24/4/1346autoscalingAWS clouddigital healthedge computingInternet of Things (IoT)Kubernetes |
| spellingShingle | Hisham Alasmary ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring Sensors autoscaling AWS cloud digital health edge computing Internet of Things (IoT) Kubernetes |
| title | ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring |
| title_full | ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring |
| title_fullStr | ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring |
| title_full_unstemmed | ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring |
| title_short | ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring |
| title_sort | scalabledigitalhealth sdh an iot based scalable framework for remote patient monitoring |
| topic | autoscaling AWS cloud digital health edge computing Internet of Things (IoT) Kubernetes |
| url | https://www.mdpi.com/1424-8220/24/4/1346 |
| work_keys_str_mv | AT hishamalasmary scalabledigitalhealthsdhaniotbasedscalableframeworkforremotepatientmonitoring |