Cloud Computing Methods Based on IoT for Better Patient Data Planning: A Research

Pandey, Arvind Kumar; Maneria, Sumitra

doi:10.1109/smart55829.2022.10046668

Cited by 1 publication

(1 citation statement)

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“…Cloud-based health monitoring frameworks, leveraging smart technologies, showcase the integration of wearables and cloud computing into promising healthcare techniques [12]. The synergy of the cloud and the IoT significantly influences remote healthcare, enhancing patient outcomes and monitoring capabilities [13]. Improved analysis and monitoring of medical data, large storage capacities for electronic health records (EHRs), and instantaneous access to critical information are key advantages of cloud connectivity in healthcare.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Remote Patient Monitoring: A Review of Biosensors Integrated with Multi-Hop IoT Systems via Cloud Connectivity

Uddin,

Koo

2024

Applied Sciences

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This comprehensive review paper explores the intricate integration of biosensors with multi-hop Internet of Things (IoT) systems, representing a paradigm shift in healthcare through real-time remote patient monitoring. The strategic deployment of biosensors in different locations in medical facilities, intricately connected to multiple microcontrollers, serves as a cornerstone in the establishment of robust multi-hop IoT networks. This paper highlights the role of this multi-hop IoT network, which efficiently facilitates the seamless transmission of vital health data to a centralized server. Crucially, the utilization of cloud connectivity emerges as a linchpin in this integration, providing a secure and scalable platform for remote patient monitoring. This cloud-based approach not only improves the accessibility of critical health information but also transcends physical limitations, allowing healthcare providers to monitor patients in real-time from any location. This paper highlights the transformative potential of this integration in overcoming traditional healthcare limitations through real-time remote patient monitoring.

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Section: Introductionmentioning

confidence: 99%