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

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 distribute...

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Bibliographic Details
Published inInternational journal of interactive multimedia and artificial intelligence Vol. 6; no. 7; pp. 7 - 17
Main Authors Kishor, Amit, Chakraborty, Chinmay, Jeberson, Wilson
Format Journal Article
LanguageEnglish
Published IMAI Software 01.09.2021
Universidad Internacional de La Rioja (UNIR)
Subjects
Analysis
Cloud Computing
data segregation
Data Segregation Scheme
Fog Computing
health
Health care industry
Health care reform
Information storage and retrieval
Latency
Machine Learning
Medical care
Multimedia Healthcare Data Analytics
Multimedia Transmission
Quality management
Quality Of Service
Iran
India
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Summary: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. KEYWORDS Cloud Computing, Data Segregation Scheme, Fog Computing, Latency, Machine Learning, Multimedia Healthcare Data Analytics, Multimedia Transmission, Quality Of Service.
ISSN:1989-1660
1989-1660
DOI:10.9781/ijimai.2020.12.004