A Novel Approach for Minimizing the Latency in Fog Computing

Abstract

When it comes to today's requirements, the most problematic ones are those that involve dealing with the vast generation of multimedia data from Internet of Things (IoT) devices, which becomes extremely tough to handle if done solely through the cloud. The technology of fog computing emerges as an intelligent solution that operates in a distributed environment, according to the researchers. Latency minimization in e-healthcare is the goal of this article, which is achieved with fog computing technology. Therefore, in IoT multimedia data transfer, it is necessary to lower the delay factors such as transmission time, network time, and computing time because of the rising demand for healthcare multimedia analytics. Fog computing brings processing, storage, and analysis of data closer to the Internet of Things and end-users, hence reducing latency. In this research, a novel Intelligent Multimedia Data Segregation (IMDS) strategy based on Machine Learning (k-fold random forest) is developed in a fog computing environment that segregates multimedia data, as well as a model for calculating total latency, is discussed.