Decentralizing and processing data a few steps away from the source that produces it is one of the advantages of Edge Computing. Not only that, such devices can be equipped with Artificial Intelligence and learn from the processes they monitor. They are also fast and process data in real-time. All benefits that we can’t get through a cloud network alone. But let’s see what these benefits are and also the aspects to consider when adopting edge solutions.
A liquid infrastructure: the flexibility of Edge Computing
The well-known Polish sociologist Bauman has defined the current context as “liquid society,” in which structures are quickly composed and decomposed to adapt to changes. The social rigidity of the past becomes fluid, changeable, flexible. Let’s bring such a concept back to the factory. Today, thanks to new technologies, we can see that even infrastructures have abandoned rigidity in favor of flexibility that allows them to welcome and exploit the potential of new technologies. A liquid infrastructure is exactly what we need to implement Edge and IoT technologies in a flow that generates benefits from the bottom to the top.
In a previous article, we have illustrated what could be a typical technology stack for implementing edge architecture on the factory floor. The layers of which it is composed allow data to transform into useful information and get into the hands of the top management.
Data processing: the proximity advantage
When we define Edge Computing as a distributed computing architecture, we emphasize the advantage this technology offers to the devices that produce the data. There are no long data transmissions to the cloud that could delay communication. Data processing occurs close to the device that generates the data.
Also, dedicating a processor to a machine or process means that the data they produce won’t go to the cloud queue. But they will undergo an initial skimming and classification on site. The data, useful in real-time for the processes incorporated in the edge architecture, will be processed and fed back into the flow. Instead, strategic information will be sent to the reference cloud as it is needed for analysis and process evaluation activities. This decentralization allows a reduction in data latency.
The downside is that this fragmentation of decentralized devices makes their management more complex. Indeed, it will be necessary to strategically envisage specific objectives for each local device and verify their optimal functioning.
An Edge architecture allows data to be processed locally and fed back into the process quickly. Here's how to reduce latency times. Click To Tweet
Edge Computing and Cloud Computing: antagonists or collaborators?
And here’s a question that often causes confusion: what’s the difference between Edge Computing and Cloud Computing? Are they opposing technologies? In reality, although they follow opposite operating logics, they are not in the antithesis of each other: both contribute to proper data processing. The big difference is the infrastructure: decentralized for the Edge and centralized for the Cloud. So, the data recorded by the Edge are mainly processed locally, and access occurs when you are physically near the network. As far as the Cloud is concerned, on the other hand, access and processing of information can also take place from points outside the company network and the area where the company is located.
This is why we can’t see them as antagonists but rather as collaborators. Many solutions on the market offer both Edge and Cloud. In fact, with both technologies, operational and immediately functional data is maintained on the edge nodes, while data that needs to be processed, stored, or used for forecasting and predictive phases are sent to the Cloud. In particular, the more timely the data I take, the greater the efficiency I can bring to my process.
The risk of data attacks: is Edge Computing secure?
When designing an Edge infrastructure, the issue of security is a key piece. The cloud data center provides very high levels of protection from external attacks. However, when using peripheral devices to decentralize data processing activities, they must have secure access. For example, encryption of the data, or even a physical block that prevents its use by those who do not have the authentication codes.
Perimeter security can be enhanced with constant monitoring and controls:
- physical and virtual access to devices;
- logs of all perimeter activities;
- changes to the hosting of data and applications in the Edge.
An example of Edge Computing: video surveillance
The companies that will undoubtedly benefit from edge computing handle huge amounts of data, and for whom low latency and bandwidth make all the difference.
A perfect example of this is businesses that leverage video capture systems. In recent years, we have moved from simple video surveillance to server processing of images to identify something specific (fire risk, theft in progress, recognition of people).
Edge computing allows video processing to be carried out onboard the cameras. The savings and efficiency of this type of infrastructure are significant because the extrapolation of the information will be done locally. This means that if the camera detects an event that needs to be recorded, it will only send that information to the cloud and not all of the footage.
Basically, let’s say our goal is to recognize people entering a given area. By embedding the Edge in the cameras, I can process the information locally and only send the total number of entrances and the names of the people to the cloud. By relocating the intelligence, it saves on the entire infrastructure.
The Edge remains the best solution for industries that need to:
- analyze data and get answers in real-time;
- quickly feed information back into the process.
The initial investment of an Edge Computing infrastructure, in this case, will be amortized in a short time and will ensure an increase in efficiency that only this type of technology allows.
