Explanation, Advantages & Use Cases

What is Edge Computing?

Edge computing refers to the pre-processing of data close to the source. This allows calculations to be carried out in real-time and relieves the burden on local networks. In this article, we will explain why edge computing is playing an increasingly important role in industrial environments and other areas and which technologies are being used to implement it.


What is edge computing?

Edge computing refers to the processing of data directly at or near the source. The term “edge” refers to decentralized data processing at the “edge of the network”.

Edge computing can reduce the amount of data that needs to be transmitted to a more distant data center for processing. This not only conserves network bandwidth, but also avoids delays in data processing. This makes edge computing technology particularly relevant when real-time results are required.

The areas of application for edge computing are diverse: From industrial production management, smart homes and medical technology to autonomous driving. Edge computing also provides an important foundation for technologies such as augmented and virtual reality, where delays are unacceptable.

In many industrial environments, edge computing and cloud computing are combined strategically, with large volumes of data being collected from a wide variety of sources, pre-processed locally and then transmitted to a central cloud environment.


What are the advantages of edge computing?

In contrast to processing all data in a central data center, local pre-processing of data using edge computing technology offers a number of advantages:

Faster and more efficient data processing:
When data does not have to be transmitted over long distances, it can be analyzed and processed faster. This improves the performance and user experience of applications that require low latency, such as video games and augmented reality. Similarly, the real-time availability of plant data also plays a role in industrial environments in order to automate processes and respond to malfunctions as quickly as possible.

Lower network utilization:
When less data is sent over the network, bandwidth is conserved and network costs are reduced. This is particularly relevant when large amounts of data are generated by sensors and other devices.

Processing large amounts of data:
As part of local pre-processing, data can be filtered, aggregated or compressed before it is sent to central servers or cloud platforms. This makes it possible to transfer only the relevant data and not put unnecessary strain on storage and computing capacity.

Improved security and data privacy:
When data does not need to be transmitted over the internet, it is less vulnerable to attacks or data breaches. In special cases, edge computing allows data to be isolated and processed in accordance with local laws and regulations, making it easier to meet compliance requirements.

Use Cases

Where is edge computing used?

Edge computing is primarily used in cases where large volumes of data need to be processed quickly, securely and in a resource-efficient manner.

In many areas, including home and city automation, autonomous driving and security technology, it is impossible or impractical to send all data to remote data centers. This also applies to many everyday devices and smartphone functions such as door openers or automatic facial recognition.

The most important areas of application for edge computing include the following:

  • Home automation (Smart Home)
  • Autonomous driving
  • Health monitoring
  • Security technology
  • Industrial manufacturing
  • Logistics and transport

In industrial production, edge computing enables the real-time evaluation of large amounts of data from sensors, control systems and other sources. This data can be used to monitor the status of systems and implement predictive maintenance.

Digital production management in the cloud

Our cloud platform manubes combines cloud computing with edge computing to collect production data, visualize it in real-time and manage production processes using automated workflows.

The manubes platform offers worldwide access via web browser, easy operation and maximum security for production data.

Technical Implementation

How can edge computing be implemented?

Edge computing can be implemented in various ways. In many cases, smartphones, security cameras, routers or other devices are already equipped with the capacity for local data processing.

Especially for industrial applications, special hardware and software components are also available, allowing manufacturers to leverage the benefits of edge computing within existing structures.

Edge devices

Edge devices are hardware components that enable data processing close to the source. This requires them to have powerful processors and memory, on top of being able to collect data and transmit processed data to other systems. The latter is achieved with the help of various protocols and interface standards (see next section).

In order to deploy, run and manage applications on (Linux-based) edge devices, a container virtualization software such as Docker can be used.

Various manufacturers offer special edge devices for industrial use cases:

Protocols and interfaces

Suitable protocols and interfaces are needed to transport data between data sources, edge devices, data centers and cloud platforms.

Wireless transmission and mobile communications standards such as Bluetooth or 5G play an important role for many edge computing use cases.

At the application layer, MQTT is one of the most important network protocols for data transmission between IoT devices and other applications due to its resource-saving implementation. Within industrial environments, the OPC UA standard is also fused frequently.

Cloud computing

In cloud computing, (external) computer resources are utilized to centrally store and process large amounts of data. The most important advantages of cloud computing include the scalability of cloud environments and location-independent access to cloud resources.

In practice, edge computing and cloud computing complement each other very well: The decentralized data processing of edge computing reduces the amount of data that needs to be transported to the cloud. At the same time, cloud platforms serve as a central infrastructure for processed edge data and enable further storage, analysis and visualization – with all the advantages of cloud computing included.

Companies that combine edge computing and cloud computing benefit from a hybrid architecture that combines the advantages of local data processing with the scalability and performance of cloud environments.

Digital production management in the cloud

Our cloud platform manubes combines cloud computing with edge computing to collect production data, visualize it in real-time and manage production processes using automated workflows.

The manubes platform offers worldwide access via web browser, easy operation and maximum security for production data.

Future Outlook

Which role will edge computing play in the future?

Already, edge computing is being widely used in various areas, including the Internet of Things (IoT), industrial automation and healthcare.

With the ongoing development of technologies such as 5G and the emergence of increasingly powerful edge devices, the ability to process data locally is constantly improving. A further increase in the importance of edge computing is therefore predicted for the future. Due to advances in areas such as augmented and virtual reality, the number of use cases in which real-time data is required could grow even faster.

More and more manufacturers are also relying on the benefits of edge computing in industrial production. Companies with multiple sites in particular are using the technology to quickly gain insights from huge amounts of sensor and plant data without overloading their existing infrastructure.

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