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.
Here come 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 assume that this will 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 today. 54 percent would like to learn more about it (Gartner/Cisco, 2018).
With Konfuzio's AI application, companies are transforming edge computing into AI on the edge, enabling faster, safer and more cost-effective data processing.
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 basics
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 particularly important in situations where companies need to make quick, intelligent decisions - without the long latency times that occur when transferring data to remote servers. To build an edge computing architecture, companies 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.
2. 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.
3. 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.
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.
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!
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.
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!
EN 
DE 
ES 
FR