Fog computing is also known as “fogging“ or “fog networking“ is a decentralized computer architecture in which one or more end users or the edge devices store, compute the data and the applications are distributed in the logical and efficient place between the source and the cloud. Fog Computing extends cloud computing, bringing the advantages and the power of the cloud closer to the data where it is created and acted on.
[no_blockquote text=”The main purpose of fog computing is to improve the efficiency and reduce the amount of data that is transported to the cloud for the processing, analyzing and for storage. All this is done to improve the efficiency and it may also use for security reasons.” text_color=”” title_tag=”h2″ width=”” line_height=”” background_color=”” border_color=”” show_quote_icon=”yes” quote_icon_color=”RED” quote_icon_size=”2px”]
The OpenFog was founded by the members from Cisco, Dell, Intel, Microsoft, ARM and Princeton University in November 2015. The main mission of this is to develop an open reference infrastructure and to transfer the business value of fog computing.
A fog computing can have a variety of components and functions which include fog computing gateways that accepts data that the IoT devices have collected. This architecture may also include some of the routers and switching equipment.
The nodes in the fog network receives feed or the data from the IoT devices that are connected using any of the protocol in real time. The node runs the IoT enabled applications for the real-time control and the analytics with the respond time which is in milliseconds. They also provide transient storage, often 1-2 hours. The node in the fog network sends summarized data to the cloud at periodic times.
The data generated by the self-driving and the self-autonomous is increasing and already generated a large amount of data. The cars that runs independently requires a capability to analyze certain data in real time such as surroundings, driving conditions and the directions. There are other type of data that should be sent back to the manufacture to help in improving its maintenance or vehicle usage.
Like self-driving cars, smart cities also need to analyze the large amount of data in the real time. Sometimes the data is of remote areas needs to be process and large amount of data need to be aggregated from the large no. of sensors.