Survey: An IOT Based Approach for Hybrid Security Management in Health Care for Secure Cloud Storage

In some Emergency Situations, Patients must be monitored continuously and treated for. However, going to the hospital to perform such work is challenging due to time restraints. A virtual care monitoring system can help with this. With the rising use of cloud computing, the suggested system presents an efficient data science technique for IoT enabled healthcare monitoring systems that improve the productivity of data processing and the use of data in the cloud. IoT sensors in plenty are used to gather health care data. For the purposes of science processing, these data are stored in the cloud. An altered data science technique is initially introduced in the Healthcare Monitoring - Data Science Technique (HM-DST). The Upgraded Pigeon Optimization (IPO) technique is used to arrange the data that is kept in the cloud, which helps to increase prediction accuracy. The best method for selecting features is then illustrated for the removal and features selection. In order to analyze human healthcare, a backtracking search-based deep neural network (BS-DNN) is used. The performance of our system is then assessed using various real-time healthcare datasets, and changes are observed using the current smart healthcare monitoring systems. To assist the medical community in early detection of a patient’s primary organs’ health status, allowing for proper treatment, as well as to securely store patient health data in the cloud. The goal of the project is to demonstrate how the sensory system can be used to track a patient’s movement invisibly.