Scheduling IoT Healthcare Tasks in Fog Computing Based on their Importance

Abstract

The Internet-of-Things (IoT) is the future of the Internet and one of the most important technologies used in recent times in all areas, where everything will be connected to the Internet. One of them is using IoT in healthcare area such as mobile health and remote patient monitoring with various diseases such as heart disease, blood pressure, diabetes, and other chronic diseases. The real-time for remote health monitoring applications is very important; any delay caused by transferring data to the cloud and back to the application is unacceptable. So, we propose to use fog computing between sensors and cloud computing to collect and process data in an efficient manner, reduces the amount of data that is transported between the cloud and the sensors, and increase the entire system efficiency. Wireless sensors networks (WSNs) that used in the area of health monitoring send a huge number of tasks with different importance levels and lengths simultaneously to fog computing. Therefore, we need to implement an appropriate task scheduling algorithm with an ability to give priority for tasks accurately and make the main factor in giving priority to tasks is their importance regardless of their length. In this research, we attempt to improve static task scheduling algorithms performance by using a new method called Tasks Classification and Virtual Machines Categorization (TCVC) based on tasks importance. Tasks that received by IoT classified based on their importance into three classes: high importance tasks, medium importance tasks, and low importance tasks based on the patient’s health status. In order to measure the performance achieved by these methods, they will be applied on MAX-MIN scheduling algorithm. The cloudsim simulator has been used to measure their impact on algorithm complexity, resource availability, Total Execution Time (TET), Total Waiting Time (TWT), and Total Finish Time (TFT).