Design and Implementation of a Fog Computing Architecture for IoT Data Analytics

Abstract

The number of Internet of Things (IoT) devices has exponentially increased, creating an explosion of data that requires sophisticated processing and analysis techniques. When it comes to meeting the demands of long duration and narrow band of the things' Internet applications, traditional cloud computing solutions may encounter difficulties. To overcome these issues, fog computing has developed into a workable concept for extending nube services all the way to the system's edge. The development of a fog computing architecture for the analysis of data from the internet of things is the topic of this study. Our system's three primary parts are fog nodes, edge devices, and a central cloud server. Sensors and edge devices of the Internet of Things (IoT) are in charge of local preprocessing and data collection. In between network edge devices and the cloud server, fog nodes act as intermediaries. Their actions have reduced the volume of raw data sent to the cloud for processing and archiving. One cloud server manages all aspects of data analysis, storage, and archiving. In order to show how effective and efficient our architecture is, Our approach was supported by data gathered from a variety of Internet-connected devices, and by lowering the amount of data transferred to the cloud, we were able to considerably lower lag and the use of black band. The network's core fog nodes also offered the processing capacity required to carry out analysis relatively instantly. The advantages of the board devices, given that a large number of Internet of Things applications require real-time or almost real-time data processing, this architecture stands out because to its capacity to lower latency, save bandwidth, and improve system efficiency.