AI on the Edge - Decisive Change in Data Processing

Jan Schäfer

Enterprises are now generating vast amounts of data so quickly that traditional data center infrastructures can no longer handle some of it. Gartner's forecast is therefore no surprise: By 2025, 75 % of the data generated by enterprises will originate outside central data centers.

This is where edge computing and artificial intelligence (AI) come into play. Briefly referred to as "AI on the Edge". This is a decentralized data processing architecture equipped with AI. Experts expect this to be available worldwide by 2028. However, the integration of edge computing is already in full swing. For example, 27 percent of companies are already using the technology. 54 percent would like to learn more about it (Gartner/Cisco, 2018).

We'll show you why you too should turn to edge computing now to relieve the burden on your IT infrastructure and make data processing more efficient.

The most Important in a Nutshell

  • Edge computing enables faster, real-time data processing, reduces latency, and enables efficient use of network bandwidth.
  • The technology results in faster decisions, reduced downtime and improved application performance.
  • The key challenges of edge computing include ensuring data privacy and security, and seamlessly integrating edge systems into existing infrastructures.
  • With Konfuzio's AI application, companies are transforming edge computing into AI on the edge, enabling faster, safer and more cost-effective data processing. Let one of our experts advise you now on how you can use Konfuzio in your company!
edge computing definition

What is Edge Computing?

Edge computing describes a decentralized data processing architecture in which data is processed close to the source of data production instead of being sent to central data centers or cloud servers. This technology makes it possible to process data in real time. This is especially important in situations where companies need to make fast, intelligent decisions - without the long latency that comes with sending data to remote servers.

Edge computing devices

To build an edge computing architecture, enterprises need these 3 pillars:

Edge computing devices

Physical edge computing devices collect data directly from their environment. These can be IoT (Internet of Things) devices, sensors, cameras or other data collection devices.

Edge server/gateways

These components are located in close proximity to the edge devices and are responsible for processing and analyzing the collected data. They are more powerful than the edge devices themselves and run algorithms to analyze data in real time.

Optional: Edge Cloud

This is an advanced form of edge computing where enterprises connect local edge devices and servers to cloud resources to enable increased processing capacity and storage. This type of architecture creates seamless integration between local processing and cloud-based services.

What is AI on the Edge?

AI on the Edge, also known as Edge AI or AI at the Edge, describes the use of artificial intelligence on local devices or "at the edge" of a network - rather than on central servers or in the cloud. The term "edge" here refers to the decentralized endpoints of a network, such as IoT devices, smartphones, sensors, or other embedded systems that collect and process data.

In practice, this means that when companies use AI on the Edge, they are able to run AI algorithms and models directly on these local devices without needing a permanent connection to the central server or the cloud.

edge computing vs.

Edge Computing vs. Fog Computing

Edge computing and fog computing are both decentralized data processing approaches that aim to bring data processing closer to the data source. This reduces latency and thus increases the efficiency of the data processing. While edge computing focuses on local processing of data close to the end devices, fog computing integrates an additional layer of computers or gateways between the end devices and the cloud. 

That is, edge computing performs data processing directly on the end devices, while fog computing enables processing on both the end devices and the gateways. In this way, Fog Computing creates an enhanced processing capability.

A key difference of Fog and Edge Computing is the coverage: Edge Computing is limited to local end devices, while Fog Computing covers a wider area by extending the processing capacity to a larger number of end devices via gateways. Both approaches aim to minimize latency and save bandwidth. 

Edge computing is particularly effective for applications that require very low latency, such as autonomous driving. Fog computing, on the other hand, is well suited for applications in distributed networks, such as smart grids, where more extensive data processing and analysis is required.

Edge Computing vs. Cloud Computing

Like edge computing cloud computing is an approach to data processing. While edge computing processes data locally on devices in the immediate vicinity of where they originate, cloud computing does so at remote locations or data centers. 

Edge computing analyzes data in real time, which enables low latency. In contrast, cloud computing requires data transmission via the Internet. This leads to higher latency times. 

In practice, cloud computing is particularly suitable for applications that need to store and analyze large amounts of data, such as Big Data analytics and complex calculations. However, cloud computing relies on secure connections and server management. Here, edge computing scores with more data protection because it processes data locally.

How are Edge Computing and IoT related?

Edge computing and IoT are closely linked because together they enable powerful and flexible data processing. IoT devices collect large amounts of data from their environment. This data often needs to be analyzed efficiently to enable fast decision making. This is where edge computing, or AI on the Edge, comes into play. Instead of sending all data to remote servers for processing, edge servers perform this task locally on or near the IoT devices.

This decentralized architecture makes it possible to minimize latency and increase the efficiency of data processing. For example, in smart cities, sensors on street lamps can collect traffic data and analyze it locally. Cities then use the results directly on site to optimize traffic light systems. 

Edge Computing Advantages

To implement the technology, companies must first create the appropriate infrastructure. However, this is worthwhile, as the following edge computing advantages show:

Lower latency

Edge computing enables lightning-fast processing of data, which is especially crucial in real-time applications. For example, doctors in telemedicine analyze patient data immediately and make diagnoses - without having to wait for slow network connections. This enables fast and life-saving decisions.

Efficient bandwidth utilization

Local data processing by edge devices reduces data transmission, optimizes network utilization and enables smooth communication between different devices and applications. In a networked factory, for example, manufacturers send only relevant amounts of data to central servers, which increases efficiency.

Improved data security

Edge computing processes sensitive data locally. This minimizes the risk of data leaks and cyber attacks. For example, a smart home processes biometric data such as fingerprints locally without sending them over the Internet. This protects privacy and ensures maximum security.

High scalability

Edge computing enables easy integration of new devices and resources to meet growing demands. In a smart traffic network, for example, cities add new sensors to monitor traffic flow and prevent congestion.

Robust applications

With edge computing, companies are able to run applications continuously even during network outages. Autonomous vehicles, for example, use edge computing to make decisions in real time. This ensures passenger safety under all conditions.

Optimized energy efficiency

Local data processing reduces energy consumption because companies need to transmit less data over the network. Smart streetlights, for example, analyze ambient data locally and regulate their brightness accordingly. This leads to significant energy savings and supports sustainable urban developments.

Better user experience

Applications interact in real time without causing delays. Online gaming platforms, for example, use this technology to ensure low latency. Players thus interact seamlessly with each other and enjoy a smooth gaming experience.

Cost efficiency

By processing data locally, companies significantly reduce the cost of data transfer and cloud storage. Companies are thus able to analyze large amounts of data locally instead of using expensive cloud servers. This leads to significant savings and makes edge computing an economical solution for companies of all sizes.

Edge Computing Challenges

So it's obvious: Edge computing is a potent solution for rapidly increasing data volumes. However, companies face the following challenges: 

Limited resources

Edge computing devices often have limited computing power as well as memory. Complex computations could quickly exhaust these resources. For example, an intelligent surveillance system with limited computing power has difficulty performing complex image analysis.

Connectivity

Edge devices require a stable Internet connection to exchange data with other systems. In rural areas or during natural disasters, this connectivity is at risk. For example, an autonomous vehicle in a remote area has difficulty obtaining real-time data for navigation.

Integration complexity

Integrating edge systems into existing infrastructures is a complex process. Companies have to synchronize different protocols and data formats in the process. One example is a smart home that needs to integrate different IoT devices to enable an Automation that works seamless.

Scalability

Although scalability is one of the benefits of the technology, it can also be a challenge depending on the application or industry. This is the case when companies want to integrate new edge devices and servers into an inflexible architecture. 

Data Management

The large amount of locally processed data requires efficient management solutions. A lack of data management leads to data loss or at least inefficient use. For example, a retailer has difficulty analyzing sales data effectively if it cannot consolidate data from different stores.

Energy consumption

Edge devices require energy to operate. In applications that are battery-powered, companies need to optimize energy consumption to ensure longer life. One example is IoT sensors in agricultural applications. Excessive energy consumption significantly shortens battery life, preventing companies from using the applications efficiently.

Regulatory challenges

Different countries and regions have different data protection and security regulations. Companies must therefore ensure that their edge systems are compliant with the relevant regulations. For example, a company using edge computing in the healthcare industry must ensure that it complies with privacy regulations to avoid legal issues.

ai on the edge examples

Edge Computing and AI on the Edge Examples

In practice, edge computing and AI on the edge enable more efficient processes in almost all industries. To give you a better idea of how the technology is being used, we show 5 concrete edge computing examples that are already possible today or that we can expect in the near future:

AI on the Edge in the manufacturing industry - predictive maintenance

In the manufacturing industry, AI on the Edge is crucial for optimizing processes, especially in the area of predictive maintenance. Here, manufacturers integrate sensors directly into machines to continuously monitor various parameters such as vibrations and temperatures. These sensors generate large amounts of data that are traditionally sent to a central data center or the cloud. By using AI on the Edge, companies perform this data analysis directly on-site, at the "edge" of the network. This means that AI on the Edge analyzes the collected data locally on the machines or in the immediate environment without having to send it to external servers. 

By using AI algorithms locally, companies detect anomalies and wear patterns in real time. For example, if a sensor detects unusual vibrations or temperature fluctuations, local AI responds. It not only identifies the problem, but also predicts when the machine is likely to fail.

These predictions are extremely valuable because they enable manufacturers to perform maintenance work exactly when it is needed - rather than according to a rigid schedule. This significantly minimizes unplanned downtime. After all, manufacturers service machines before a breakdown occurs. This in turn increases productivity because the machines run longer without interruptions. In addition, manufacturers save costs because they avoid expensive emergency repairs. 

Integrate AI on the Edge into your manufacturing processes now! Our experts will advise you on your individual case!

Agriculture - Precision Agriculture

On farms, companies place sensors in fields that measure soil moisture, temperature and other environmental factors. The collected data is analyzed locally to calculate optimal irrigation and fertilizer use. This improves farming efficiency, saves resources and enables more accurate crop planning.

Healthcare - Mobile patient monitoring

Patients use wearable medical devices that continuously capture vital signs such as heart rate and blood pressure. Hospitals analyze this data locally to monitor health conditions in real time. In case of deviations, medical professionals can react immediately and treat patients in time, which increases patient safety.

AI on the Edge in transportation - autonomous vehicles

Autonomous vehicles are equipped with various sensors such as cameras, lidar and radar systems to detect their surroundings. Edge computing is used to analyze the data collected by these sensors in real time. This enables autonomous vehicles to make quick decisions, such as detecting obstacles and adjusting the route, which increases road safety.

Retail - Intelligent vending machines

Description: Smart vending machines are equipped with sensors that monitor inventory and collect data about product sales. Through local data analysis, the vending machine optimizes inventory levels by predicting demand for specific products. This leads to efficient inventory management, minimizes sellouts and improves customer satisfaction.

Telecommunications industry - Network optimization through AI on the Edge

In the telecommunications industry, IT managers and CTOs face the challenge of not only maximizing network performance, but also minimizing operational costs while responding to increasing demands for data security and ease of use. AI on the Edge embeds sensors and advanced algorithms directly into network components such as routers, switches and base stations. Telecommunications companies are thus able to optimize network performance in real time and proactively respond to potential bottlenecks.

A real-world example is intelligent monitoring of network traffic and utilization. Through edge-based AI, IT managers automatically detect patterns in traffic that indicate future bottlenecks. These insights enable preemptive optimization of network capacity even before performance is impacted. In addition, edge computing's local processing of data enables rapid response to security threats, with AI detecting and blocking suspicious activity directly at the point of origin - before it compromises the entire network.

Integrate AI on the Edge into your networks now. Talk to one of our AI experts now without obligation!

Konfuzio - Intelligent Solutions for AI on the Edge

Konfuzio has highly specialized AI solutions for companies that want to make complex data processing faster, safer and more cost-effective. To do this, the incumbent uses software that combines advanced technologies such as artificial intelligence, machine learning and deep learning. Konfuzio thus enables companies to integrate AI on the Edge into comprehensive data processing and analysis and thus benefit from the trend-setting technology.

Do you still have questions about using AI on the Edge in your business? Then get advice from one of our experts now and find out how you can make your data processing more efficient and cost-effective with Konfuzio!

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