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Definition of Fog computing

What is fog computing in simple terms

Fog computing is like a smaller computer closer to your devices. It can handle some tasks itself, like processing data from sensors or making quick decisions, without relying on the faraway cloud all the time. This makes things faster and more efficient.

While initially associated with Cisco, fog computing is open to broader adoption. It strategically places intelligence and processing power within the local area network (LAN) for efficient data handling.

The fog metaphor refers to a cloud sitting low to the ground, mirroring how fog gathers around the network's edges.

What is a real-world example of fog computing?

Here are some examples of using fog computing:

Self-driving cars — Fog computing allows cars to process sensor data (like traffic lights and obstacles) locally, enabling quicker decision-making for autonomous driving features.

Smart traffic lights — Traffic cameras in busy intersections use fog computing to analyze video feeds. They can identify accidents or congestion immediately, allowing authorities to respond faster and keep the city running smoothly.

Factory robots — In industrial settings, fog computing allows for real-time analysis of sensor data (like temperature or vibration) from machines. This helps predict equipment failures before they happen, preventing costly downtime and promoting safety.

AR/VR gaming — Fog computing handles the processing of nearby objects, ensuring smooth and realistic interactions within the VR environment, even with a slight internet delay. 

Fog computing vs edge computing: what are the key differences?

Fog computing bridges the gap between cloud computing and edge devices. It and edge computing approaches enable data processing closer to its source, reducing the traffic burden on the cloud. But the key difference between edge and fog computing boils down to location.

Consider fog computing as a local processing center within your network. Sensors and devices send data to a fog gateway, which can independently handle some processing and decision-making. It then forwards more complex tasks or filtered data to the cloud for further analysis. This approach offers a good balance between real-time response and scalability.

In edge computing, the intelligence resides right on the devices themselves. These devices can perform basic tasks and send only the most crucial data to the cloud or a fog layer.  While this reduces reliance on external processing, it can limit the complexity of the tasks handled and create more potential points of failure if individual devices malfunction.

If real-time response and a centralized view are crucial, fog computing might be the better fit.  For simpler tasks and limited power/connectivity situations, edge computing could be sufficient.

Key Takeaways

  • Fog computing is like having a mini computer nearby your devices. It can handle some tasks on its own, making things faster without always relying on the distant cloud.
  • Examples of using fog computing are self-driving cars, smart traffics, AR/VR gaming and factory robots.
  • The key difference between fog and edge computing is where the intelligence lies. Fog computing operates like a local processing hub within the network, balancing real-time response and scalability. In contrast, edge computing places intelligence directly on devices, ideal for simpler tasks and limited connectivity situations.