What is Edge Computing? Explanation, advantages, disadvantages and examples!

If you deal with digital data processing, you will eventually come across the term “edge computing” in addition to “cloud computing”. According to the name, this describes data processing at the edge of a network. You can find out what this means in detail, what advantages and disadvantages there are with edge computing and what else you should know about the topic here.

Edge Computing – Here you will find the advantages and disadvantages of local data processing within a network. Examples include the smart home. There are also individual devices such as the iPhone as well as industrial device combinations as examples of edge computing.
Edge Computing – Here you will find the advantages and disadvantages of local data processing within a network. Examples include the smart home. There are also individual devices such as the iPhone as well as industrial device combinations as examples of edge computing.

The differences between cloud and edge computing

With cloud computing, the individual devices in a network (computers, sensors, measuring devices, etc.) send their requests and determined data to central servers, on which the results are calculated and output to the output devices. The Internet is used, which enables data exchange over long distances, but even with the most modern and fastest connections does not allow data processing and reactions in real time - especially when there is a large volume of data and devices.

And it is precisely this problem – along with issues such as data protection and broadband relief – that edge computing is intended to solve. The data processing or at least a preselection of the relevant data takes place locally, i.e. either directly within the device in question or within the same local device network. So instead of sending all raw data to the server and waiting for an evaluation, the data is interpreted on site, analyzed and outputs are calculated to get a real-time response. This should eliminate numerous hurdles, especially in the Internet of Things (IoT).

The benefits of edge computing

I have already mentioned a few advantages. But if you want to make a list of the most important advantages of edge computing, you can definitely find a few more points:

  • Low latency: One of the biggest benefits of edge computing is reducing latency. By processing data locally in close proximity to the data source, response times are significantly reduced. This is particularly important for applications such as autonomous driving or augmented reality (AR). Virtual reality headsets can only deliver a satisfactory user experience through edge computing.
  • Bandwidth relief: Edge computing reduces the need for network bandwidth because every record generated does not have to be sent to distant servers. Not only is this more cost efficient during operation, but it is also beneficial for regions with limited bandwidth.
  • Privacy and security: Local processing of sensitive data enables a higher level of data protection and security. Minimizing the transmission of data over external networks reduces the risk of data breaches. It also makes it more difficult for third parties to access data.
  • Scalability: Edge computing provides flexible scalability by distributing computing power across different edge devices. This enables efficient adaptation to increasing demands, especially in environments with a growing number of connected devices.
  • Offline capability: Another big advantage of edge computing is the ability to work without a constant internet connection. This is particularly relevant in environments with unreliable network connectivity and in applications that require continuous functionality. Of course, only if the entire necessary calculation takes place through edge computing and not just data preselection.

The disadvantages of edge computing

As with every system and every concept for bringing together various, complex individual factors, there are more than just advantages here. In the following list you will find a few edge computing disadvantages:

  • Complexity of administration: Distributing computing power to the edge of the network leads to increased complexity in its management. The administration and maintenance of distributed systems requires specialized knowledge and on-site resources. Instead of a cloud solution with administration close to the server that is convenient for people from outside the industry, if necessary, a specialist for edge computing needs to be hired for company applications.
  • Security risks at the local site: The decentralization of data processing brings with it its own security risks in local operations. Edge devices can theoretically be more vulnerable to physical attacks, requiring an additional layer of security monitoring.
  • Hardware upgrade costs: The use of edge computing may require investments in powerful edge devices. The need for hardware upgrades can result in higher costs, especially when extensive scaling is required. Various factors (acquisition costs, possible savings, etc.) must be compared to determine the profitability of the whole thing.

Edge computing using the example of the Apple iPhone

In addition to complex, industrial application areas that are difficult for laypeople to understand, everyday devices can also be used as examples of edge computing. The Apple iPhone, for example, processes a wide variety of inputs, sensor data, queries and the like locally without having to have an internet connection and external servers having to be contacted for the required result.

For example, fingerprints and faces for Touch ID and Face ID are compared locally with the stored data so that device access can be granted or denied offline. Siri has also been working directly on the device without an internet connection for a while now. Other functions such as text recognition, autocorrection, image recognition and the like can be used as examples. With iOS 18, Apple could also introduce generative AI for the iPhone that works locally.

Edge computing using the example of autonomous vehicles

Autonomous driving could be made safer on a somewhat more extensive scale, namely through the use of a local network. Certain sensor data and camera observations in self-driving vehicles are interpreted directly using the vehicle hardware. However, the data collected from the vehicle is not always enough to make it safe for everyone involved. With data sets collected from other vehicles as well as cameras and sensors on roads, the vehicle can react much better to approaching potential dangers.

An example would be the situation at an intersection. Here, a single vehicle cannot use its sensors to record all possible factors that are necessary to safely interpret the situation. It would therefore make sense to exchange information with other vehicles as well as with scanners and cameras that keep an eye on the entire intersection from above. In this way, people on the footpath, people speeding up on bicycles, emergency vehicles requiring space and the like can be identified and included in the decision on subsequent actions.

This would work through local exchange almost in real time, without the individual devices involved first having to communicate with a central cloud. Ultimately, this is only informed about the decisions and their consequences in order to create and interpret logs and enable improvements to the system. Contact with the central cloud does not have to be uninterrupted, but should be established from time to time in order to enable data evaluation, which promotes the further development of the offer.

Edge computing using the example of the smart home

Another application area of ​​edge computing that is tangible for many is smart home technology. In a smart home, various devices such as thermostats, cameras, lighting and household appliances are networked together to create an intelligent and automated environment. Edge computing plays a crucial role in optimizing the performance and response time of these systems. A cloud offer (or even a cloud requirement) can play a role with several providers. But there are also offers with local data processing and thus the possibility of secure offline use.

Speaking of safety: Sensitive systems and devices that can be affected by certain environmental influences or frequent material fatigue could be monitored using appropriate sensors. For example, heating systems or the electrical supply (e.g. mixed supply of local solar energy and connection to the power grid) could be monitored and problems could be detected using algorithms applied directly in the smart home. As in the industrial sector, where oil and gas pumps are monitored in this way and shut down in a timely manner to protect against possible failures or damage, a smart home could also be protected from damage and its consequences.

Summary and other sources of information

Overall, edge computing offers a promising solution for the current and future challenges of extensive data processing. By combining the advantages (low latency, bandwidth relief, data protection, scalability and offline capability), innovative and secure applications can be developed from smartphones to computers to industrially used device combinations. Of course, depending on the application, the disadvantages must also be taken into account so that alternatives can be considered if necessary.

Since this article is only intended to serve as a starting point for your research on the topic, here are a few other sources with more detailed, scientific and relevant information for IT professionals:

  • Edge Computing at Wikipedia: German / English
  • “What is edge computing? “Everything you need to know”: TechTarget
  • Video “Microsoft explains: Edge Computing”: YouTube

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