Cloud Based Resource Provisioning method for IoT enabled Sensory Monitoring: A Healthcare Application

Abstract

Fog is a data management and analytics service. This paper introduces a novel approach for providing Fog computing is used to offer IoT-enabled services in healthcare applications. Data for this study were gathered from the Google Scholar, Science Direct, and Medline databases. To deliver quality services to customers, IoT-based Fog Computing solutions are presented. For an IoT client, an effective resource provisioning solution for boundary discovery, service level agreements, and administrative services has been developed. The deep Q residual information processing technology is used to link cloud data centers, and the computing paradigm approach is used to determine the reference depth of fog levels. The suggested optimum resource provisioning method analyses the dataset and simulates the environment using the tensor flow tool. Fog's computation layer is made up of IoT sensor data, cloud data centers, and modelling layers. The deep belief network is built using a 256 X 256 X 3-layer architecture with 5000 training data, with 1000 test data used for simulation. Using supervised and unsupervised learning approaches, each dataset simulation was recorded. According to the findings, fog computing data management and analytics systems. The proposed method has achieved 92% accuracy, further it shows better performance in terms of security and convenience compared with existing methods.