A QUANTITATIVE STUDY ON ADOPTION OF EDGE COMPUTING FOR REAL-TIME DATA ANALYSIS IN INTERNET OF THINGS NETWORKS: A CASE OF KENYATTA NATIONAL HOSPITAL
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Date
2025-10
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Publisher
Gretsa University
Abstract
The increasing adoption of IoT technologies in healthcare led to the generation of large volumes of
real-time data that required fast and efficient processing. Traditional cloud computing approaches
often faced latency and bandwidth limitations, which made it difficult to support critical healthcare
operations. This study analyzed the role of edge computing in enhancing real-time data analysis
within IoT networks, focusing on Kenyatta National Hospital as the case study.
A quantitative research design was employed, and data was collected through structured
questionnaires and system performance records from ICT personnel, biomedical engineers, and
healthcare practitioners. The study examined variables such as system response time, reliability,
data security, and operational efficiency. To analyze data, descriptive and inferential statistics
methods were used. The findings indicated that adoption of edge computing improved data
processing speed, reduced latency, and enhanced network reliability in IoT-based healthcare
systems. The study concluded that the integration of edge computing into hospital infrastructure
strengthen real-time decision-making and patient care. It was recommended that healthcare
institutions adopt edge computing technologies to complement existing cloud systems to improve
service delivery.
Description
Research project
Keywords
SOCIAL SCIENCES::Statistics, computer and systems science::Informatics, computer and systems science