Three Reasons Edge Computing Is Key To Scalable IIoT

Edge computing has become vital in increasing the speed and efficiency of processing data. By having computing take place at the “edge” of corporate networks, IoT devices are provided with local computing which leads to faster response times, less latency and reduced overall traffic.

But how can edge computing be applied to industrial IoT in particular? It’s important to consider the differences between IoT and IIoT, as IIoT is a subcategory of IoT. Often, however, IoT devices are associated more often with individual customers in homes or offices, whereas IIoT devices are more specifically used in industrial settings in larger, more commercial areas, for example in manufacturing facilities. In this article, we list three reasons why edge computing is key to achieving scalable IIoT.

1. Edge Computing Increases Speed and Latency

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As with IoT devices, edge computing also increases speed and improves latency in IIoT devices. Maximized reduction in time is crucial when dealing with time-sensitive processes. For example, when monitoring the performance of equipment, data must be generated and analyzed immediately. Data traveling back and forth between the cloud is inefficient and takes far too much time.

A benefit of edge computing is that it drastically improves response times in order to allow for real-time IIoT applications. In this sense, edge computing helps with achieving scalability, as IIoT devices will need to be able to support an increasing number and variety of connections at fast speeds to compete in future markets.

2. Increased Security With Edge Computing

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In traditional cloud environments, data generated moves through a centralized architecture, which can be vulnerable to cyber threats. In industrial businesses reliant on data generated during operating processes, it is crucial that these issues are avoided. After all, scalable IIoT can only be achieved if processes are not interrupted or damaged.

Edge computing is the solution for this, as with it, security risks are split between edge devices and the cloud. So although cyber threats are still possible even with the use of edge computing, it makes security stronger because both edge and cloud infrastructure must be compromised at the same time before an attack can threaten the IIoT infrastructure. Also, when data is stored closer to devices, it is considered less susceptible to information theft.

3. Edge Computing Boosts Overall Performance Of IIoT Devices

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Edge devices can transmit information directly to a data communications infrastructure, which can track information about how a particular piece of machinery is performing. Additionally, the information gathered can be used in predictive maintenance. Predictive maintenance typically involves analyzing an asset to predict when it will fail, thus preventing it from doing so before it actually breaks down.

By scheduling routine preventative maintenance accordingly, one can identify potential risks. Predictive maintenance is important in creating scalable IIot, as it saves costs, improves uptime and reduces safety risks in IIoT devices. It also can lead to up to 20% longer machine lifespan.

So, from the reasons above, one can see the many benefits of using edge computing in IIoT devices, but that’s not all. It’s important to really consider the scalability of edge computing. What are its limits? Well, edge computing can really be tailored specifically to your company’s needs. For example, new edge nodes or devices can also be added later down the line if ever necessary. If you’re interested in learning more about this, check out our scalable IoT products and find out more information here.