HomeARTIFICIAL INTELLIGENCEEdge Artificial Intelligence: When Is It Worthy For Your IoT Projects?

Edge Artificial Intelligence: When Is It Worthy For Your IoT Projects?

Artificial intelligence and the cloud: this combination has been established for a long time. While the cloud provides resources, for data training for IoT solutions, AI improves algorithms that make IoT systems autonomous.

The changing digital environment brings new challenges that the new Edge AI trend is much better at. That’s why large corporations like Google, Apple and Amazon have already invested millions in edge computing so that their solutions can respond quickly to incoming inquiries. The next article answers the following questions: How does edge computing work in conjunction with AI? What are the advantages of this combination for companies? And: when is the use of Edge AI even worthwhile?

What Does Edge Artificial Intelligence Mean?

Edge AI means that AI algorithms are executed locally – directly on the device or on the server near the machine. These AI algorithms use data captured now from the device. Appliances can make independent decisions within milliseconds without having to connect to the Internet or the cloud. In contrast to Cloud AI, data is processed locally on Edge AI devices. And only the results of this processing are sent to the cloud.

Edge AI: What Does It Include, And How Does It Work?

An Edge AI system includes the following components:

  • Sensors that collect data
  • Processors/chips that perform data analysis with AI
  • Communication interfaces to send the determined metadata to a server

A machine learning model is trained based on a data set to perform specific tasks. The model is programmed to recognize patterns in the training data set and then in several training models with similar properties. It can be used for inferences – deriving new facts from an existing database – in a particular context to make predictions.

As soon as the model works as intended, it can draw insights from absolute sensor data that can be used to improve business processes. Usually, the model works through an API. The model output is then transmitted to another software component or visualized on the app front end for the end-user.

Why Is The Cloud Alone No Longer Enough?

According to a report, the Edge AI software market alone will grow an average of over 25% per year over the period 2019-2024. Industry 4.0 requires real-time computing functions. “The need for real-time insights and immediate action, the current network constraints, the high volumes of data and the speed at which sensors and endpoints generate this data are forcing IT managers to use edge computing solutions to manage the Process data closer to the source of its origin,” 

The digital environment is changing rapidly. And the cloud alone can no longer meet the new demand, as the following prerequisites show:

  • Shorter latency times: With cloud computing, the data is sent to the cloud before being analyzed. This can lead to delays. These delays are, in some cases, unacceptable. For example, when connected cars need to recognize an obstacle and brake, fractions of a second can be decisive. Activating a robot too early or too late on the assembly line can result in a damaged product. If the error goes unnoticed, the defective product ends on the market or causes damage in later production phases.
  • Independence from connection problems: With Edge AI, intelligent devices are no longer dependent on communication with the cloud. Therefore there are no problems if the internet connection is not stable. Edge AI can be an advantage, especially in rural areas or for weather stations exposed to extreme conditions.
  • Scalability: According to forecasts by IDC, there will be 41.6 billion IoT devices worldwide by 2025, generating 79.4 zettabytes of data. This amount of data requires new ways of efficient data analysis and processing. When you need to process video data from hundreds or thousands of sources simultaneously, transferring data on a cloud service is not a viable solution. However, if most data processing occurs at the Edge, there will be no bottlenecks in data transfer.
  • Cost savings: Of course, Edge AI requires local computing power and investment in hardware. Even so, it is often the most cost-effective solution. Since the need for data transfer is reduced, Edge AI can save bandwidth. Edge AI also makes devices more energy efficient. The connection to the cloud does not have to be maintained permanently. This can significantly extend the runtime of the devices.
  • Data security: The less data is sent to the cloud, the fewer opportunities for online attacks. Edge makes information theft more difficult because the processing takes place in a closed network on site. After the analysis, data is deleted, with only selected metadata being forwarded to the cloud at specified intervals.

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Intelligent Algorithms With Edge AI: Successful Use Cases

Industrial Manufacturing: Prevent Errors And Simplify Production

Errors in production can lead to production downtimes and increased costs due to error elimination. They can easily be overlooked as they can be so small and inconspicuous that the human eye has no chance of detecting them. The theBlue.ai GmbH has developed an edge AI solution that enables complete automation of quality management processes. Using unique video cameras and sensors, she checks every product in real-time for deviations and errors. The system also classifies and assigns product types. As a result, the product is directed to a selected conveyor belt corresponding to the classification to be further processed there.

Automotive: More Security And Faster Reactions

Connected cars produce a lot of data while driving. With some decisions, you can’t wait for the data to be transferred to the cloud and back.

Every Formula 1 racing car is equipped with more than 200 sensors that produce 100 gigabytes of data on a racing weekend and transmit more than 100,000 data points per second. McLaren engineers and crew needed instant, real-time access to the captured data to decide when to change a tire and assess the track’s safety.

McLaren has combined edge computing with the edge core cloud strategy. While team members receive actionable information in real-time, the same data is sent to the central McLaren Technology Center. There they are analyzed and processed in an ML racing simulator to optimize the AI ​​algorithms.

Retail: Analyze customer flow

Shops can integrate Edge AI in video cameras to analyze customer frequency and buying behavior, for example, with a camera system from the Finnish company Adrian. The solution shows which areas of the shop the customers visit at what times, which routes they take, what they look at, where they stop, what they buy and even what mood they are in. These insights enable retailers to identify the areas of the store that are low in traffic or predict when the checkouts will queue. Plus, they can streamline the facility and deploy store staff where they can make the most impact.

Without Edge AI, this process would be too slow and expensive: you would have to send videos from several cameras to the cloud, edit them with software and then send the recommendations back. The amount of data would be too large, and centralized computers would be overwhelmed with processing this amount of data. In addition, data transmission would require more bandwidth than we will ever have. Edge AI can also minimize data protection risks – videos are analyzed locally, and only the necessary information is transferred to the cloud.

When Is Edge AI Worthwhile?

The use of Edge AI is associated with increased development costs, tradeoffs between price and performance, and the complexity of executing AI algorithms on edge devices. To assess whether Edge AI is worthwhile in your specific case, ask yourself the following questions. If your answer is “yes” several times, Edge AI is most likely worth it for you.

Do Sensors At The Edge Produce More Data That Can Be Sent For Processing In The Cloud?

There are three main reasons why data cannot be sent to the cloud:

  • The costs for data transfer to the cloud are too high.
  • The performance of the devices is insufficient for data transmission.
  • Connectivity does not support the transfer of the required amount of data.

Is A Stable Connection To The Cloud Ensured?

A stable internet connection is required when a device takes actions based on sensor data and the processing is running in the cloud. In this case, it is advisable to transfer part of the data processing for essential functions to the device.

Are Our Fast Response Times Critical To Your Solution?

The decisive factor is whether the period is acceptable for your application, during which the sensor data is sent to the cloud, processed and sent back to the device. Or does your solution have to make decisions in a split second? Will latency negatively impact user experience?

Do You Have Complete Data Sets For AI Training?

For AI algorithms to work well, they need large amounts of data to train the AI ​​model. This data must be complete and unbiased. Otherwise, you will get false and pointless results.

Is There A Stable And Flexible Solution Architecture In Place?

If you want to move to Edge AI, you need a stable solution architecture that uses edge computing in conjunction with a cloud backend. Only then can AI be integrated. Also, an Edge AI architecture requires some flexibility to implement and update models over time. The architecture must support additional devices, new versions of chips and operating systems, and migration to other cloud providers.

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