The Biggest Advantages of IoT and AI Edge Computing

AI edge computing and Internet of Things (IoT) computing are changing the way businesses operate and delivering a wealth of benefits. Read on to find out what this means for businesses of all types.

Edge computing is the act of decentralizing data processing. Rather than relaying data back to a central processing hub, the processing takes place close to the source of data itself.

We can look to healthcare for good examples of how this can work in action. Healthcare facilities collect vital data from their patients, which is then used in monitoring, reporting, and deciding on diagnoses and prognoses. Traditionally, this data would have been collected via monitoring systems and then interpreted manually by medical professionals. Then, with the arrival of the digital age, data could be processed automatically, but only via a central server or processing unit.

With the advent of edge computing technology, this data can be processed via decentralized systems installed at the patient's location. This dramatically simplifies and streamlines the process.

The example of a patient in a medical facility is just one instance of how this can work. Across manufacturing, logistics, transportation, and a wealth of other applications, AI edge computing is the next phase in data-driven, artificially intelligent tech development.

Let's take a closer look at the key advantages of edge computing in practice.

The defining feature of edge computing is its proximity to the source of data itself. While data travels quickly through networks and can be transmitted vast distances at speeds that we humans feel are almost instantaneous, there is still a degree of latency. By handling processing at, or close to, the source, this latency is essentially eliminated.

This means decisions can be taken and acted upon in real time — without the drawn-out process of sending data, processing it, and then receiving a response. The AIR All-on-One System, for example, needs only three images for training and then can be deployed as an effective object recognition system. Recognition takes place right at the source, with no latency.

Data in transit is inherently vulnerable. While all sections of a network are potential targets for cybercriminals, it is relatively easy to protect processing units, data storage structures, and other network locations where data is stationary. This is why nefarious actors often target data pipelines and structures used to transport data.

With edge computing, these structures don't exist — or the distance the data needs to travel is much shorter. This improves security, as there are fewer points of weakness for hackers and criminals to target.

It's becoming increasingly difficult for businesses to avoid storing and handling data. From manufacturing to construction, most modern organizations rely on information resources to hone and develop their operations. This puts industrial projects within the purview of international data handling regulations.

By adopting edge computing, businesses are better equipped to meet these regulations. As data is not put at risk by extended periods of transfer and transportation, it becomes easier to protect. Compliance is simplified, even as regulatory requirements develop and evolve over time.

In order to grow and scale, businesses need to be able to leverage data in an effective way. Edge computing makes achieving this simple, as the processing takes place right at the source and is completed with minimal delay.

Implementing the infrastructure required to achieve this is also inherently scalable. Edge computing units — like the EPC-TGU compact computing system — can be added on a modular basis, achieving swift data processing precisely where it is required. Any data sources that need processing capabilities can be fitted with edge units, so businesses can build their data-driven systems as they go.

Edge systems are simpler than traditional systems. There is no need for vast, interconnected data pipelines carrying information to a centralized hub and then delivering it to where it needs to be. Instead, AI edge computing handles everything within close proximity to the source.

The practical upshot of this is a more budget-friendly setup. This, in turn, reduces the barrier to entry associated with AI and swift data processing, ensuring that more organizations have access to its benefits.

Edge computing not only makes data processing more simple — it can also make it more powerful. As organizations depend on increasing amounts of data processing power, this is going to become key in the future, and edge devices can give organizations the capability they need to do more with data.

While centralized servers are powerful, the sheer volume of calculations and actions they need to complete can reduce this power. Processing data at the edge of the network can result in far more power, if handled correctly. Products like the RIM-OOBM monitoring solution can be used to track performance, ensuring optimal operation and minimal downtime.

IoT computing systems collect data from within the network itself, and then edge computing devices process this data. While this is not quite happening in real time, it's pretty close — it's certainly much faster than it would be if data was processed in a more traditional way.

This gives organizations the chance to make decisions and enact initiatives in almost real time, dramatically improving operational agility and flexibility. With this in mind, all organizations need to adopt effective edge computing strategies — or risk getting left behind.

At Avalue, we offer a wide range of different products, covering IoT, AI, and edge computing, as well as much more besides. To find what you need, take a look at our extensive range today, or reach out to our team directly with any questions or queries you may have.