Enhancing Decision-Making in Healthcare with Fog Computing for Low-Latency Data Processing
Ameya Mahale, G. Geetha · 2024
The research aims to improve cloud computing efficiency using fog computing by optimising parameters such as response time and network utilisation. It also focuses on integrating Latency-Aware Modules into healthcare fog-cloud networks in order to reduce latency and to improve data security, for critical applications and improve patient outcomes.. We created a latency-aware framework that uses fog computing, deep learning algorithms, and edge tools to create a better healthcare system that better meets the needs of patients while also making healthcare more efficient, effective, and scalable.A cluster-based Fog Computing system capable of autonomously collecting and analyzing discrete data from distributed IoT devices before transmitting it to the cloud to minimize connection latency and network congestion. Moreover, our system endeavors to enhance the processing speed of encrypted data without compromising accuracy or privacy. By prioritizing secure and efficient data transmission and analysis within the ecosystem, we aim to establish a robust framework for IoT operations. Additionally, efforts will be made to minimize data duplication, thereby optimizing network bandwidth and storage utilization. We have compared fog computing, cloud computing, and their combination in three areas: latency, data security, and resource management. Fog computing provides superior latency performance, cloud computing provides improved data security, and the mixed approach balances latency, security, and resource management in a better way.