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Fog computing extends the concept of cloud computing to the edge of the network, making it ideal for the Internet of Things and other applications that require real-time interactions.

The concept of fog computing was developed to combat latency problems That plague a centralized cloud computing system. In fact, it is a decentralized computing infrastructure in which data, computation, storage and applications are located somewhere between the data source and the cloud.

Like edge computing, fog computing can optimize data analysis by Storing information closer to the data source. This works in tandem with edge computing. Many people use these two terms interchangeably, as they imply bringing intelligence and processing closer to where the data is created.

What is fog computing? How does it work? What are the advantages and disadvantages of it? Zoom on fog computing!


Table of contents

What is fog computing?

The term fog computing, coined by Cisco, refers to a alternative to cloud computing. This approach captures the twin problems of the proliferation of computing devices and the opportunity offered by the data generated.

In fact, by placing them closer to devices, rather than establishing channels in the cloud, users aggregate bandwidth at access points such as routers. This in turn reduces the overall need for bandwidth, as less data can be transmitted from data centers, through cloud channels.

Data storage is another important difference between cloud computing and fog computing: the decentralization and the flexibility.


In 2015, Cisco decided to partner with Microsoft, Intel, Dell, Arm and Princeton University to form the OpenFog consortium. Then, other organizations like Foxconn, General Electric, Hitachi also contributed to this project. The main objective of this association is to promote and standardize fog computing.

How does fog computing work?

Fog computing works by using local devices called nodes As well as edge devices. In this context, IoT beacons capture the raw data. The raw data is sent to a node near the data source. Then it is analyzed locally, filtered, and sent to the cloud for long-term storage if needed.

The difference between fog computing and edge computing

Fog computing and edge computing offer similar functionality in terms of delivering intelligence and data to devices. Both technologies harness the power of computing capabilities within a local network to perform computational tasks. In fact, the philosophy of fog and edge computing is to move processing activity closer to the edge of the network to speed up service.

Although they both intend to reduce network latency and congestion, they differ significantly in the way they actually process data. In this context, the main difference between edge computing and fog computing is where the intelligence and computing power resides:

  • In edge computing, data is processed directly on the data sources Such as sensors, IoT devices, or on the devices to which the sensors are connected.
  • On the other hand, fog computing transfers computing tasks to an IoT gateway Or nodes located in the LAN network.

What are the benefits of fog computing?

The main benefits of fog computing boil down to increasing the efficiency of an organization’s computing resources and IT structure.


Today’s business applications require a response time in the order of seconds or milliseconds, especially when corrective actions are required. In this context, fog computing eliminates the need to send data for processing to the cloud. As a result, computation occurs faster and the fog network can process large volumes of data with minimal delay. In other words, the latency between input and response is minimized. The goal is to provide millisecond-level responsiveness, enabling near real-time data processing.

Network bandwidth conservation

Moving data from the network edge to a cloud server requires a lot of bandwidth. Imagine a situation where each of your IoT devices generates 100 GB of data per day, while you have thousands of them. Transferring this large amount of data can take days. In fact, since fog computing processes data locally, it conserves network bandwidth to the maximum.

Online and offline access

Fog computing requires data to be stored primarily in the local network and sent to the cloud only when needed. This makes it ideal in scenarios where there is no internet connection to send data.

Real-time analysis

Fog computing reduces the inefficiency and latency that accompanies cloud services by real-time analytics. In fact, many organizations are leveraging the competitive advantage provided by real-time data analysis to stay ahead of the curve. For example, companies in the manufacturing sector need to be able to react to events as they occur; financial institutions need real-time data to inform business decisions or monitor fraud.

What are the drawbacks of fog computing?

Like any technology, fog computing applications also have drawbacks. Here are some of the limitations that you should consider before jumping in:

Security and Privacy Risks

Large companies use multiple devices and it is almost impossible to authenticate them all. This makes them vulnerable to various forms of cyber attacks if proper protections are not in place. In fact, malicious people can access your nodes by using your own devices against you.

Fog computing also raises concerns about end-user privacy. In fact, nodes collect sensitive information from devices.

Energy consumption

Nodes consume a lot of energy to operate. Therefore, the more nodes an organization has, the more energy is consumed. This can be a challenge to manage for some organizations.

Unsafe Physical Location

Location is also one of the weak points of fog computing. Indeed, due to the dispersed nature of the nodes, some of them may very likely be located in less secure environments. Therefore, malicious actors can easily access them, increasing the risk of attacks.

Network complexity

Fog computing is typically used in tandem with traditional networking and cloud computing resources. The combination of these technologies can become very complex to manage. This complex network architecture must be maintained and secured against cyber attacks. Therefore, the larger the organization, the more difficult the task becomes.

What industries use Fog Computing?

Fog computing is becoming increasingly popular with industries and organizations around the world. However, the main industries that take advantage of this technology are those that require data analysis close to the edge of the network and use advanced computing resources.

Healthcare is one of the main industries that rely heavily on fog computing. Other industries that need to collect large amounts of data also use it: agriculture, retail, government, oil and gas, etc.

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