Optimizing Exam Data Processing in Traditional Universities: A Case for Edge Computing Implementation with Smart Routers
Hoa. Doan Nguyen Thanh, Phu Nguyen Ngoc Thien, Nghia. Phan Duc, Lap. Hoang Van, Huy Nguyen Mai · 2024
This paper explores the challenges inherent in the traditional model of exam data processing at universities, where the centralized cloud server encounters issues such as bandwidth limitations, prolonged sending times, and data loss due to congestion. To address these shortcomings, we propose a novel approach leveraging smart router devices equipped with storage and computing units to deploy an edge computing model. In this model, the smart routers act as temporary storage locations, optimizing data processing by sending exam data to the cloud server during periods of reduced network traffic. Through a comprehensive demonstration, we validate the effectiveness of this edge computing model, showcasing its ability to enhance the speed of exam data submissions, streamline processing efficiency, and minimize data loss. Our findings advocate for the adoption of edge computing solutions, particularly through smart router integration, as a promising strategy to modernize exam data handling in traditional university settings.