Edge computing and fog computing explained

By processing data at or near the source of the data, resources that may not be continuously connected to the network, such as laptops, smartphones, tablets and sensors, can be used to optimize cloud computing systems.

Cloud computing has changed computing in ways we couldn’t have imagined a decade ago. From voice control to automation, being able to process massive amounts of data off a device in seconds brings huge benefits. But what happens when you don’t have those seconds, or can’t make a connection? Welcome to the edge.

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As the number of connected devices surges, one of the main issues we have to deal with is pushing large amounts of data to the cloud for processing. Today, cloud connectivity is reliable enough, but it is not feasible to always rely on the cloud to handle everything.

This is where edge computing comes in. Simply put, the edge is where data is generated and initially gathered - static gateways, sensors, computers, smartphones, etc. It collects the data and processes it as much as possible, so only what really needs cloud capabilities is sent.

Fog under the clouds

Ground-based technology has been developing in parallel with the cloud. Today, devices are more capable of processing more complex data. And network technologies, such as Bluetooth Mesh networking, enable us to do more by using multiple devices at the same time.

Fog computing is a term coined by CISCO and refers to data processing, storage and networking technologies that reside beneath the cloud. The OpenFog Alliance exists to promote the development of this field.

Edge computing and fog computing are often used interchangeably, although they are slightly different. In edge computing, the processing is done by the devices themselves, while in fog computing, the collected data is pushed to a separate device for processing.

No internet? No problem

Self-driving cars generate a lot of data. They have hundreds of sensors that take millions of readings to ensure the vehicle is driving safely. You might think this is a perfect use case for the cloud. Collect the data, send it to the cloud for processing, and then decide whether to stop or keep going.

I’m sure you can see the main problem here. The amount of data they collect simply can’t be transmitted to the cloud fast enough to make a decision in time. We can’t have self-driving cars sitting still waiting for a response from a server.

Not all self-driving cars will be on public roads, either. In the mining industry, where automation offers huge benefits for worker safety, establishing reliable network connectivity hundreds of feet underground is a major achievement.

Instead of relying on cloud computing, you do most of the data processing on board the car, making it truly autonomous. Then, when necessary, you send only the most important data to the cloud that could be useful to other self-driving cars. For example, if there is an unexpected obstacle on the road, the cloud can notify other vehicles.

A more secure IoT?

Leveraging edge computing can help improve the security of IoT devices by minimizing network exposure and reducing attack surfaces and attack vectors.

If hackers were able to break into a self-driving car, it could cause chaos. If each sensor communicates directly with the cloud to determine what it needs to do, then each sensor needs to be protected. However, if the data is processed onboard and only small amounts of data are exchanged with the cloud, the potential for attack is much lower.

Helps reduce costs

Another major advantage of edge computing is reduced costs. Network bandwidth and cloud computing may be relatively cheap, but the cost of transferring megabytes of data to the cloud quickly adds up. The less you use, the less you pay, so if 90% of your data is processed locally, you can save 90% of the costs.

Having to equip each sensor with the ability to independently connect to the internet increases costs and consumes more power. Connecting devices together in a Bluetooth Mesh network and then connecting that network to the cloud will result in a more economical, low-power solution.

Edge computing also makes better use of your existing resources. If everyone in your company has a smartphone, you can use them to perform as many actions as possible, rather than relying on the cloud and leaving valuable processing power sitting idle.

New Possibilities

With the current increasing levels of integration with wireless systems on a chip and the resulting cost reductions, exciting new possibilities are emerging.

The Nordic Semiconductor nRF52840 is a multiprotocol device with 1MB of memory and a cortex M4F CPU running at 64MHz. This device has considerable capacity to perform localized calculations and run logic rules on the data it collects.

When such devices are multiplied on a mesh network, micro-distributed computing scenarios will become possible. In the future, devices may even be able to share each other’s computing resources when necessary.

The increasing use of edge and fog computing will bring huge benefits to everyone adopting IoT.