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Use Cases Fog Computing

Fog Computing

Fog computing refers to a decentralized computing structure, where resources, including the data and applications, get placed in logical locations between the data source and the cloud; it also is known by the terms ‘fogging’ and ‘fog networking.’

The goal of this is to bring basic analytic services to the network edge, improving performance by positioning computing resources closer to where they are needed, thereby reducing the distance that data needs to be transported on the network, improving overall network efficiency and performance. Fog computing can also be deployed for security reasons, as it has the ability to segment bandwidth traffic and introduce additional firewalls to a network for higher security. 

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Edge computing via local Edge controllers allows uniterrupted, high speed collection, filtering and processing of real time data line side. Initial fast reactions can be initiated with the full data sets also being sent to associated cloud systems for further analysis. This processing at two layers respects the data integrity but allows the collected data to be processed as live and historical instances. The challenge is how to collect the data from diverse sources without interrupting the existing processes but also being able to span "brown field" systems where there may not even be any existing communication or data capture functions. 

The edge analytics market is estimated to grow from USD 1.94 billion in 2016 to USD 7.96 billion by 2021, at a Compound Annual Growth Rate (CAGR) of 32.6%.

Source: Markets and Markets

 

What is the business value of this IoT use case and how is it measured?
Your Answer

What value do Fog Computing to companies?

By adding the capability to process data closer to where it is created, fog computing seeks to create a network with lower latency, and with fewer data to upload, it increases the efficiency at which it can be processed.

There is also the benefit that data can still be processed with fog computing in a situation of no bandwidth availability. It provides an intermediary between these IoT devices and the cloud computing infrastructure that they connect to, as it is able to analyze and process data closer to where it is coming from, filtering what gets uploaded up to the cloud.

What are the benefits of Fog Computing in real-time applications?

It is broadly used in IoT applications which involves real-time data. It acts as an extended version of cloud computing. It is an intermediate between the cloud and end users (closer to end users). It can be used in both the ways, that can be between machine and machine or between the human to machine.

- Mobile Big Data Analytics

- Water Pressure at Dams

- Smart Utility Service

- Health Data

 

What business, integration, or regulatory challenges could impact deployment?
Your Answer

What are the major challenges in Fog Computing?

Security challenges are predominant in fog computing. 

Fog computing considers the architecture of SOA. The network layer is established between the service layer and the application layer. Hence, Fog computing is designed ahead of traditional networking components, which are highly vulnerable security attacks.

 

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