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Health is on eof the main characteristic of quantity of life, hence the significance of having an appropriate healthcare monitoring system. The Internet of Things (IoT) is a state of the art technology that has found application in modern remote health monitoring systems. Fog computing is given to IoT by producing the local computing. Fog computing serves to overcome high latency and consumption of bandwidth in the cloud. Remote operation and connection is the heart of health monitoring application where real time information exchange is key. However, there is often delay due to the transfer of the information to the cloud and back to request which is intolerable. So a technique is heareby proposed to incoperate the fog computing techniques amongst cloud computing and the sensors to process information and to collect it efficiently. The sensor nodes are collected and detected by using the global monitoring capability to trace and detect the moving sensors which can be achieved by fog computing. With this design, there is minimal amount of data transfer between the sensor and node which increases the system efficiency. The task scheduling algorithm has the ability in which the main factor provide importance to perform the task exactly. Thus the algorithm is further augmented with a system called Task Classification and Virtual machines Categorization (TCVC) which applied a MAX-MIN concept to schedule the important tasks to optimize the cloud storage and reduce the latency and time on fog computing.
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