Parallel processing of IoT health care applications

Abstract

Health care is the hardest real time constrained domain. Queuing system, Delay in treatment, Difficult to treat rural people, Disability to do remote treatment are the major issues in current health care system. Internet of Things (IoT) allows caring of people from remote locations with the help of integration of wireless sensor network with internet. Different sensors are used to measure different health parameters. Processing of smart health care data in an efficient manner is necessary. Slight time variation causes severe effect such as loss of life. In IoT, delay occur in processing large volume of sensor data in real time. Energy spent by the sensors also affected by processing delay. Because sensors spent energy in idle state. To reduce delay in processing smart health care data, Multi core technology is included with IoT. SixLoWPAN is the technique used to connect low configured devices with internet. In this paper, Task Level Parallelism (TLP) is applied to process different health parameters in parallel. TLP utilizes the available resources in optimal way. It makes our system as more efficient. The proposed system improves performance upto 65.5%. Efficient system also reduces power consumption of the devices. This will increase the life time of the sensor network.