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

• Published in: International 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
• Language: English
• 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
• DOI: 10.9781/ijimai.2020.12.004
• ISSN: 1989-1660

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.