Edge Computing

In a world where data volumes continue to grow and networks increasingly face real-time demands, the principle of decentralized processing is becoming ever more important. Edge computing refers to data processing that takes place not in a centralized cloud but directly at the “edge” of the network—such as within a sensor, a device, or in close proximity to it. This approach enables rapid responses, lower latency, and reduced load on transmission paths—especially in environments where large amounts of data are generated, quick decision-making is essential, and bandwidth or connectivity is limited. The following sections explain what edge computing actually entails, the application areas in which it already plays a role, and how it compares to other models such as cloud or fog computing, including their respective advantages and disadvantages.

What is Edge Computing?

Edge computing refers to a decentralized IT architecture in which data is processed directly at its source—for example, in sensors, machines, or other end devices at the edge of the network. Instead of sending all information to central data centers or cloud systems, local components such as edge devices and edge gateways take over the filtering, preprocessing, and analysis of the data.

This architecture enables low latency, robust operations, and more efficient use of central resources, making it an important foundation for data-intensive applications in IoT, industrial, and real-time environments.

Edge Computing vs Cloud Computing

Edge and cloud computing are both data processing models. One of the main differences is where the data is processed. With edge computing, this happens at or even in the end device, while with cloud computing it happens in a central IT structure, the cloud. Both work independently of each other, but can also be used together. For example, large amounts of data can be pre-filtered and reduced by edge computing before being transported to the cloud.

Applications such as complex data analysis, accessing data from anywhere, and long-term data storage are very feasible with cloud computing. However, the disadvantage here is the speed at which a decision is made, as the data must first be sent to the cloud, processed there, and then the decision sent back to the device. This can result in higher latency times. In addition, unfiltered data can lead to excessive data volumes and bandwidth overload for data transport. Edge computing can alleviate these problems.

Edge Computing vs Fog Computing

Edge and fog computing are often used as synonyms, although they describe different approaches. Fog computing is, in a sense, an intermediary for cloud infrastructure. If the cloud floats centrally above all end devices like a cloud, fog computing is like a fog that is closer to the end devices. This means that not all data is sent to the cloud, but is already processed in nearby mini data centers (the fog nodes). This reduces latency and processing times.

Fog nodes are able to communicate with each other, which is not possible with edge computing. This makes more complex analyses feasible with fog computing than with edge computing, which is limited to very simple analyses and, above all, to the filtering of data.

Fog, edge, and cloud computing work particularly well in combination with each other. Edge computing can be used to pre-filter and reduce data volumes, and initial analyses can be performed in the fog node. Complex and time-consuming tasks are then forwarded to the cloud. This allows the strengths of the different models to be exploited to the full.

Advantages and Disadvantages

Advantages of Edge Computing

• Real-time data processing with minimized latency
• Enables real-time monitoring and services
• Works even without an internet connection, which is particularly important in rural areas or in areas with poor reception
• Reduces transmission delays and service outages
• Bypasses bandwidth restrictions
• Sensitive customer and company data remains on site and does not have to be transported to the cloud

Disadvantages of Edge Computing

• Irregular computing or storage requirements
• Greater control and protection of end devices is necessary to prevent misuse and failures
• Constant availability of devices must be guaranteed
• Time-consuming initial setup

Examples and Use Cases

Edge Computing and IIoT

The abbreviation IIoT stands for Industrial Internet of Things. The term Industry 4.0 is also often used as a synonym. It refers to intelligent and digitally networked industrial machines or systems designed to create more efficient and self-organized production. Sensors and control devices generate enormous amounts of data. For example, the approximately 6,000 sensors on the Airbus A350 deliver around 2.5 TB of data every day. To avoid having to transport this amount of data unnecessarily to the cloud, it is filtered and evaluated on site, and only a fraction of it is sent to the cloud.

Edge computing also plays an important role in predictive maintenance, as the data collected here is also primarily important on site and enables short decision-making processes. For example, that a machine needs maintenance due to conspicuous measured values.

Edge Computing and IoT

The IoT (Internet of Things) describes networked and intelligent electronics, such as those used in smart homes. Similar to the IIoT, this also generates a lot of data that is primarily useful on site.

Edge Computing and Autonomous Driving

This uses a combination of edge and fog computing. Control units, sensors, and actuators also generate large amounts of data in autonomous driving. It is not uncommon for between 5 and 20 terabytes of data to be generated every day. Local data analysis (code to data) in a mobile mini data center, using fog computing, allows the data to be evaluated on site and only the results to be transmitted. This enables the required data to be processed in real time and quick decisions to be made. After all, delays can be life-threatening in ongoing road traffic. In addition, edge computing also works offline, which means that an autonomously controlled vehicle can easily drive through a dead zone or a tunnel and still be fully functional.

Edge Computing in Healthcare

Data records in healthcare increased by over 800 percent between 2016 and 2018. But not all of this data needs to be stored. Edge computing makes it possible to filter out the relevant data directly at the end device. For example, unremarkable heart rates can be detected and deleted. At the same time, abnormalities can be detected and forwarded without latency. This allows real-time response to emergency situations.

Conclusion

In summary, Edge Computing is not a replacement for the cloud but a valuable complement—especially in scenarios that demand speed, proximity to the data source, and architectures optimized for data and bandwidth efficiency. By processing, filtering, or compressing data directly “at the edge,” decision-making can be accelerated, resources conserved, and data volumes reduced. At the same time, centralized clouds remain essential for in-depth analytics, long-term storage, and global accessibility. Anyone designing modern data and AI architectures should therefore view Edge, Fog, and Cloud not as competing alternatives but as a coordinated ecosystem—one that enables the optimal blend of technologies based on specific requirements and conditions.