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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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.  
PubNub Aids in McDonald's Malaysian Campaign
PubNub Aids in McDonald's Malaysian Campaign
McDonald’s Save the Sundae Cone campaign had a McDonald’s Sundae Cone on the digital billboard, which was slowly melting in the heat of the city. To “save” the sundae cone, the audience needed to spin a giant fan that would ‘cool’ the sundae cone and ‘un-melt’ it. They did this by spinning a mini-fan, which was accessed through their mobile device’s web browser. At the end, participants were given a voucher on their smartphone to be redeemed at a McDonald’s across the street for a free sundae cone. The realtime network needed to be able to handle hundreds of users simultaneously, and with such a large audience, 100% uptime was vital for the campaign.
Employing Intel Deep Learning SDK Toward Bettering Image Recognition Models
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: 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

 

Who is involved in purchasing decisions, and who are the primary system users?
Your Answer

Network Administrators: Network administrators manage and maintain fog computing infrastructure, ensuring its reliability, security, and performance. They deploy fog nodes, configure network settings, and monitor system health to ensure optimal operation.

Data Scientists: Data scientists leverage fog computing platforms to analyze data at the edge of the network, extract valuable insights, and develop predictive models. By processing data locally, data scientists can reduce latency and improve the accuracy of their analyses.

Which technologies are used in a system and what are the critical technology?
Your Answer

Edge Computing: Fog computing extends the principles of edge computing by distributing computing resources closer to data sources and endpoints. Edge computing technologies, such as edge servers, gateways, and edge analytics platforms, enable data processing and analytics at the edge of the network.

Networking: Fog computing relies on networking technologies, including wired and wireless networks, to connect fog nodes and devices and facilitate data communication and exchange. Networking technologies, such as Ethernet, Wi-Fi, Bluetooth, and Zigbee, enable seamless connectivity and data transfer between edge devices and fog nodes.

What data is obtained by the system and what are the critical data management decision points?
Your Answer

Edge Data Processing: Fog computing platforms process data locally at the edge of the network, allowing for real-time analysis and decision-making without the need to transmit data to centralized cloud servers. This reduces latency and bandwidth usage, making it ideal for applications that require low-latency responses.

Data Aggregation and Fusion: Fog nodes aggregate and fuse data from multiple sources, including sensors, devices, and IoT endpoints, to generate actionable insights and intelligence. By combining data from various sources, fog computing enables more comprehensive analysis and decision-making.

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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