{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T17:40:47Z","timestamp":1773855647838,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Khalid University","award":["RGP2\/312\/44"],"award-info":[{"award-number":["RGP2\/312\/44"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Addressing the increasing demand for remote patient monitoring, especially among the elderly and mobility-impaired, this study proposes the \u201cScalableDigitalHealth\u201d (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 \u201cSDH\u201d 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\u2019s proximity-based processing. This innovative fusion of technologies not only revolutionizes remote healthcare delivery but also enhances Kubernetes performance, preventing unresponsiveness during high usage.<\/jats:p>","DOI":"10.3390\/s24041346","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T10:40:15Z","timestamp":1708339215000},"page":"1346","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["ScalableDigitalHealth (SDH): An IoT-Based Scalable Framework for Remote Patient Monitoring"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6482-3968","authenticated-orcid":false,"given":"Hisham","family":"Alasmary","sequence":"first","affiliation":[{"name":"Department of Computer Science, College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.glohj.2019.07.001","article-title":"Smart healthcare: Making medical care more intelligent","volume":"3","author":"Tian","year":"2019","journal-title":"Glob. 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