Fog Computing

Use Cases Equipment & Machinery Fog Computing

	Fog Computing

Overview	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. Read More
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Overview

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.

Equipment & Machinery

Discrete Manufacturing
Logistics & Warehousing
Case Studies (2)

	Engaging Fans at one of the Largest Stadiums in the USA Engaging and delighting fans has become the number one priority. However, the identity and behavior of fans within the stadium have been impossible to detect until Sirqul. Furthermore, standalone mobile apps historically have only seen a 5-30% penetration rate, thus leaving venues with poor data, insights and little interaction with the fan itself.This large stadium was looking for a set of recommendations to improve the fan experience, increase revenue and optimize operational efficiency based on this never seen before data.
	Koito - Reality AI: Next-generation Adaptive Driving Beam (ADB) smart headlight The current versions of Adaptive Driving Beams available in Japan and Europe are using traditional machine vision techniques like template matching. Those machine learning techniques perform very well in constrained environments but are prone to many false positive when used in the dynamic, real world, with a lot more variations in targets and backgrounds.
	Employing Intel Deep Learning SDK Toward Bettering Image Recognition Models In this case study, the challenge explored involves LeNet, one of the prominent image recognition topologies for handwritten digit recognition. In the case study, we dive into how the training tool can be used to visually set up, tune, and train the Mixed National Institute of Standards and Technology (MNIST) dataset on Caffe optimized for Intel® architecture. Data scientists are the intended audience.

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: M arkets and Markets Log in to view content

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: M arkets and Markets

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 Log in to view content

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 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. Log in to view content

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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Overview

Fog Computing

Applicable Industries

Applicable Functions

Case Studies

Market Size

Business Viewpoint

Deployment Challenges