A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning.

Amit Kishor; Chinmay Chakraborty; Wilson Jeberson

doi:10.9781/ijimai.2020.12.004

Authors

Amit Kishor Sam Higginbottom Institute of Agriculture
Chinmay Chakraborty Birla Institute of Technology, Mesra
Wilson Jeberson Sam Higginbottom Institute of Agriculture

DOI:

https://doi.org/10.9781/ijimai.2020.12.004

Keywords:

Cloud Computing, Fog Computing, Machine Learning, Health, Quality of Service, Data Segregation

Supporting Agencies

The authors would like to thanks to Department of Computer Science & Information Technology, Sam Higginbottom University of Agriculture, Technology and Sciences (SHUATS), Allahabad, U.P., India to give this platform to work. Thanks to all my seniors in the department.

Abstract

In the recent scenario, the most challenging requirements are to handle the massive generation of multimedia data from the Internet of Things (IoT) devices which becomes very difficult to handle only through the cloud. Fog computing technology emerges as an intelligent solution and uses a distributed environment to operate. The objective of the paper is latency minimization in e-healthcare through fog computing. Therefore, in IoT multimedia data transmission, the parameters such as transmission delay, network delay, and computation delay must be reduced as there is a high demand for healthcare multimedia analytics. Fog computing provides processing, storage, and analyze the data nearer to IoT and end-users to overcome the latency. In this paper, the novel Intelligent Multimedia Data Segregation (IMDS) scheme using Machine learning (k-fold random forest) is proposed in the fog computing environment that segregates the multimedia data and the model used to calculate total latency (transmission, computation, and network). With the simulated results, we achieved 92% as the classification accuracy of the model, an approximately 95% reduction in latency as compared with the pre-existing model, and improved the quality of services in e-healthcare.

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