Performance Improvement of healthcare IOT Applications through Smart Fog-Based Node

The increase in the number of IOT devices and the driven data has motivated users to switch to new computing environments. Nowadays, smart cities offer a variety of services, and IOT services produce high velocity, diverse, and large-sized data. With the rise of big data, the existence of cloud computing to process this amount of data in cloud data centers has become extremely necessary. Meanwhile, some smart city services need to receive real-time responses, such as hospital services, where, in the case of emergency, immediate responses with no delays are crucial. Since the cloud layer is located far from where the data is generated, data transfer to the cloud causes extendeddelays in IOT services. To address this issue, a fog layer or fog computing has been introduced in which the layer is located closer to the client and allows data processing at the edge of the network, and acts as an intermediate layer between the cloud layer and the client layer. For users who need online and real-time services, such as computer games and hospital emergency devices, etc., using a fog layer is far more suitable because data transmission and responses to the layer are way faster. In comparison with a cloud layer, a fog layer has more servers with less computational power (such as routers, modems, etc.), each with different resources. Consequently, these nodes overload faster, and some of them get under a lot of pressure. Therefore, load balancing in the fog layer is essential to improve the efficiency and to exploit the processing capacity of the fog nodes. In this paper, we try to balance the load in the fog computing layer through workload migration and improve some system metrics like the execution time and energy consumption. Furthermore, the research attempts to enhance the quality of the fog layer services in a smart hospital with the help of a modified genetic algorithm.

کلیدواژه ها:

Fog Computing ، Internet of Things ، Cloud Computing ، Big Data ، Smart City ، Smart Health ، Health Care