# A Fog Computing Architecture with Multi-Layer for Computing-Intensive IoT Applications
## 1 Introduction  
Recently, with the advent of high-performance and high-spec IoT devices, SNS data, e-mail, Internet stream data, multimedia data, etc. are collected and large-capacity transmission is possible. Big data refers to the large amounts of un-formalized, semi-formalized or formalized data that flows continuously through and around organizations [1]. Big data analysis provides the advantage of reducing costs when storing huge data while performing various analyzes in various and efficient ways [2]. The emerging 5G networks intend to accomplish new opportunities by introducing very high carrier frequencies. The cloud as the only solution for addressing these problems is not applicable in this random nature of internet. The Big data may be generated from different resources such as e-Healthcare, smart city, etc. Data mining and data analysis are a core methodology that assists investigate huge data to explore patterns, meaningful information in the data set and this information can be used for another analysis to help answer complex questions. In the form of wireless wearable accessories which are attached to a patient for retrieving the information and then analyzing of data performed [3]. That is the challenge to tackle these issues through coordinating these resources which will provide effective utilization of handling massive amount of distributed data. The fog computing nodes usually do not have enough storage and computing resources for dealing with that huge IoT data. The design uses local computing nodes, between the end points (e.g., sensors, cameras, etc.)and the cloud data center. Early big data applications were mostly deployed on-premises, whereas Hadoop was originally designed to work on clusters of small machines and edges. An increasing number of technologies such as CPI, storage, and networks facilitate processing data in the fog-cloud. For example, Cloudera [4] and Hortonworks [5] provide their distributions of the big data framework on the Amazon Web Services [6] and Microsoft Azure [7] clouds.  
In [8] research, they presented a health-care system to support autonomic analysis of Medical IoT data. A Medical IoT-enabled health care system [9] in which smart gateways used to provide warehouse and data processing. A prototype of smart-city surveillance was developed [10], which pre-process the necessary data and make decision. The machine and deep learning have been widely used in various fields, the use of machine and deep learning in health-care is discussed in [11] to high accurate predicting and classifying the data. In [12] a fog based IoT health-care system is proposed for the integration of cloud-services in interoperable health-care solutions upon traditional health-care framework.  
The main motivation of this paper is to use this multi-layer fog computing platform for enhancement of data analysis system based on IoT devices in real-time, as the current scenario of whole world due to COVID-19 has highlighted the importance of data and analysis system to avoid these circumstances in future with proper planning and management. We will suggest a multi-layered framework which many IoT devices will be used and connected to edge-fog gateways in collaborative manner to gather the optimal data needed. Through this study, we performed comparative experiments on delay time, network usage, and energy consumption when computing-intensive IoT applications are performed in cloud-only environments and in our framework, multi-layered fog architectures.  
The remainder of this article is organized as follows. In the next Section 2 provides a background and related work, which follows by the Section 3 provide the system overview of our idea, a fog computing real-time data analytics platform in detail. The Section 4 provide the use case and evaluation based on the performance and then the last section provides the conclusion and future directions.