Let us know about the Introduction to Fog Computing.
Abstract :
In the Field of Internet of Things (IOT) the devices by themselves can recognise the environment and conduct a certain functions by itself. IOT Devices majorly consist with sensors. Cloud Computing which is based in sensor networks manages huge amount of data which includes transferring and processing which takes delayed in service response time. As the growth of sensor network is increased, the demand to control and process the data on IOT devices is also increasing.
Brief : Introduction to the Fog Computing
Fog Computing is a model in which data is processed and applications which are concentrated in devices at
the network edge rather than existing almost entirely in the cloud. In general rather than sending the data directly
to the cloud from the smart devices it can be manage locally in the smart devices.
Fog Computing creates that extension of cloud functionality when you need it and brings that functionality
closer to where latency is very important.In the future 5G networks assure that latency will decrease as
low as possible like in sub-milliseconds so that the fastest network facility can be provided.
For that the introduction 5G networks must support Fog computing.
Fog Computing is a platform which is virtually generated that gives the functionality to process, manage and other
network services to the data between the cloud computing and a device.
Some Advantages of Fog Computing are as follows:
- It is reliable as it provides the mobility functionality to all the smart phones, laptops and many more physical or virtual with IOT applications.
- In the fog computing structure, devices provide context awareness relating to data created by the sensor with them. The device plays a role in combining the data at a sensor using position application context.
Architecture of Fog Computing
The Highest level is the Cloud Computing Layer which receives the information from the Middle Layer known as
Fog computing consists of Proxy Servers. The Highest level Layer is used to manage and mining the data. The Middle
Layer provides many services such as calculation, saving and networking at the edge in IOT devices.
The Last Layer or the Lowest Layer includes m-Proxy Servers with the limited amount of resources which are
setup at the store or public facility like cafe’s from the sensors so that IOT devices can collect information’s.
As the calculation is done at near edge on IOT devices so that it reduces the amount of data transferring
to the cloud and provide services to many devices with wireless connection.
For Example,
Traffic Control or reduces accident at the highly prone areas from the Cameras and Sensor set at a side of road
in Real-time. So that sensors can detect near by objects which are approaching to it closely that can includes
pedestrians and vehicles and measure the distance the speed of the objects.
So that the sensors can send the signal or information to the smart traffic lights to provide appropriate information.
Decentralized in Fog computing
Also, Fog Computing refers to decentrazlised computation at the edge of the network. By using the functionality of
distributing the data at the edge so that the result will be sent to the cloud not the raw data itself.
It helps to increase the bandwidth and computational power in the cloud. Companies using Cloud Computing for analytics often need it
fast. So that the most relevant and recent data can be accessed in real time. As the Company not need to depend
upon the globe analysing in the cloud and sent it back to them instead computation can be done closer to home.
Taking a scenario to analyze to what data do we actually need. So taking the example of Airplanes which are
connected with important sensors which are meant to prevent system failure. There are large amount of sensors
attach with Airplanes which produces up to 40TB of data per hour of flight.
If we calculate this data with and multiply with the numbers of flight hours per day that will result into astonish.
These sensors provides important information in Flight but the data not being used for analytics on fuel saving and other efficiencies would not be beneficial for being aggregated in the cloud.
So similarly the smart or self-driving cars are also connected with sensors which generates large amount of data which is to be monitor by the IOT devices constantly. If the data consistency in terms of Calculation is slow then
it will leads to failure and even more unimaginable painful. Situation for the passengers or the person. So to prevent these situations fog computing leads to manage and computation the data in the devices itself also.
Future of Fog Computing
Providing with more and better Hardware devices so that the market place can be evolved much faster like the iTune
App Stores or Google Play Store but with the focus on IOT devices. Their must be direction for expanding and
identifying the new areas of research and provides more real time cases. It will make a major impact in the IT
infrastructure and the network with emerging 5G and AI interactions.
Conclusions
In this discussion with the emerging technology and network handling called Fog Computing which handles the data from the IOT devices to process and computation so that the real time response is fast. Also it makes cloud computing concepts clear which helps to maintain the relevant and crucial data in the network. As the Fog Computing filters the data at the edge of the smart devices before sending to the Cloud. So that spaces can be utilise as much as possible for better responses and fast processes.
References:
[1] Kharb L. et al. A Comprehensive Study of Security in Cloud Computing, International Journal of Engineering
& Technology, 7 (4) (2018) 3897-3901. DOI: 10.14419/ijet. v7i4.20742
[2] Chahal D. et al. Technological Race Dependency on Cloud Computing, International Journal for Scientific
Research & Development, Volume 5 Issue 11 – NOVEMBER 2019.
[3] 3. Chahal D. et al. Data security in Cloud Computing, International Journal of Engineering Science Invention,
Volume 6 Issue 12, December 2017 (PP. 31-36).
