As for this type of computing, the data is processed on the device or sensor itself without being transferred anywhere.
The main difference between edge computing and fog computing entails where the processing of the data takes place.
With Fog computing, the data is processed within a fog node or IoT gateway which is situated within the LAN.
Compared to Cloud Computing where the data is processed in remote, far-off data centers, edge computing gives the devices the ability to process data right where data is collected, at the devices.
There are Billions of Edge Devices.
It pushes the intelligence, processing power, and communication capabilities of an edge gateway or appliance directly into devices like PLCs (programmable logic controllers), PACs (programmable automation controllers), and especially EPICs (edge programmable industrial controllers).
“Edge computing usually occurs directly on the devices to which the sensors are attached or a gateway device that is physically “close” to the sensors. Fog computing moves the edge computing activities to processors that are connected to the LAN or into the LAN hardware itself so they may be physically more distant from the sensors and actuators.” said Paul Butterworth, co-founder and CTO at Vantiq.
Advantages of Edge Computing.
- Can work without cloud or fog.
- Data isn’t transferred, and is hence more secure.
- Helps to keep costs low.
- The technology it uses saves time and resources in the maintenance of operations by collecting and analyzing data in real-time.
- Helps to optimize performance and increase uptime.
Disadvantages of Edge Computing.
- Complex issues would rather be managed at the Cloud level.
- High Infrastructure costs.
- It is less scalable than fog computing.
- Support and Monitoring are more difficult.
Edge Computing Examples
- On military and industrial facilities and equipment – tanks, weapon systems, surveillance systems, manufacturing apparatuses, and others
- City Surveillance using CCTV cameras help in identifying traffic mobile issues and criminal activities.
- Majorly applied in IoT (Internet of Things). Here data originated from IoT devices are analyzed at the edge of network before they are being send to a data center or cloud.
- Self-driving cars.
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