Optimized Latency in IoT Healthcare: Edge-Driven System with Fog and Cloud Support

Kore, Manjula Shivaji (2024) Optimized Latency in IoT Healthcare: Edge-Driven System with Fog and Cloud Support. Masters thesis, Dublin, National College of Ireland.

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Abstract

It is necessary that IoT is incorporated in patients’ care, being essential in developing nations where access to healthcare facilities is limited. Non-invasive diagnostic devices, like pulse oximeters and ECGs, depend on cloud data processing to compute the vital parameters, which causes latency threatening to provide immediate medical attention in emergency conditions including the COVID-19 pandemic. This work integrates fog computing into a basic cloud IoT architecture model with the help of AWS IoT Greengrass for edge computing and AWS Lambda for cloud processing. The fact that such important indicators are filtered at the local level minimizes traffic and delays in sharing the necessary data. Through cohort studies, patient record files of 100 to 500 were used to determine the latency performance with less than 10–20% degradation in throughput latency when handling large files. These results support fog computing as a solution to latency problems and show promising signs for using it to make precise, real-time care decisions in areas of low bandwidth such as rural regions or ICUs. Further, the least reliance on ongoing cloud contact increases the system’s robustness in regions with limited internet access, guaranteeing regular surveillance and immediate exception recognition. Such an approach shows that edge computing can transform healthcare systems where technologies are lacking by filling the gap.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Lugones, Diego UNSPECIFIED
Uncontrolled Keywords:	Latency Reduction; Scalability; Accuracy; Cloud Services; AWS Greengrass Core
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing R Medicine > Healthcare Industry T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things
Divisions:	School of Computing > Master of Science in Cloud Computing
Depositing User:	Ciara O'Brien
Date Deposited:	15 Jul 2025 13:49
Last Modified:	15 Jul 2025 13:49
URI:	https://norma.ncirl.ie/id/eprint/8116

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