Fog Computing and its IoT Applications
What is Fog computing and why?
Fog computing is also called as fogging which extends the cloud computing services to devices. It is a model where data processing can be done in the edges of networks instead of processing the entire data in cloud. Cloud computing has several advantages over few of its disadvantages like latency. Fog computing resolves this issue by reducing the amount of data going to be processed in the cloud or from the cloud.
Use Cases of Fog Computing in IoT:
The goal of IOT is to monitor, control and link everyday objects with electronic, software, sensors over the internet via mobile apps. Fog computing takes a vital role in IoT, by overcoming the drawbacks of cloud computing. It can act as a middleware between the edge devices and cloud data centers. Fog computing cannot replace cloud computing, but it will reduce the data traffic in cloud computing that can lead to better efficiency. Both cloud computing and fog computing manage, store and process the data resources. Fog local processes the computing, works, and sends its responses to the edge devices without the use of cloud.
Some of the Useful Applications in Fog computing:
- Smart Traffic lights: In Smart traffic lights fog controls the traffic by collecting data by the sensors which is locally interconnected with the sensors in other areas so that the smart lights can take decision and avoid road accidents.
- Health care: These days it’s essential to analyze and process the data in health care systems with in milliseconds. Fog computing will play a vital role in that. Patient details are gathered by sensors and will be analyzed in fog and revert the solutions to doctors without the involvement of cloud.
- Connected cars: Enables the interaction among cars, access points and traffic lights with each other. The connected car leads to safe drive.
Hence, fog computing has been introduced to reduce network traffic in cloud and make data processing easier with its characteristics such as low latency, mobility, heterogeneity and geographical distribution.
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