Getting closer to the edge with edge computing
The Internet of Things (IoT) has created a host of far-ranging opportunities for industrial equipment manufacturers. When planning your next IoT project, edge computing could deliver a number of critical benefits. But what are these? And why might you choose edge computing over cloud computing? Let’s discuss.
What is edge computing?
Edge computing refers to a form of distributed computing, whereby data is stored and information is processed closer to a device, rather than a central location, which might be based many miles away.
Why, then, might we opt for edge computing? Originally, edge computing was solely viewed as the answer to reduce the bandwidth costs of moving raw data, which would have to be transported from where it was conceived to either an enterprise data centre or the cloud. However, it’s also needed to minimise latency for applications that rely on real-time data, as this might affect their performance.
As a result, edge computing is having a dramatic impact on how devices store, process, analyse and transmit data, and how IIoT applications are being specified.
How can edge computing lower bandwidth costs?
A key market driver for edge computing is the rise of real-time industrial IoT devices, whether it be predictive maintenance, remote monitoring or process automation applications.
One example might be maintaining industrial equipment, such as a compressed air system, with real-time data ensuring the health of the compressor is monitored and any issues quickly addressed, should a potential risk be found. Alternatively, it might be an automated tank monitoring solution for the oil and gas sector, with connected devices helping to better manage deliveries and servicing schedules.
Edge computing is a particularly key consideration for IoT devices when they are being deployed at scale. While a single device transmitting data will not be an issue, operators can encounter problems when hundreds or thousands of devices are having to transmit data at the same time. This can lead to potentially very expensive bandwidth costs as a result of transporting all this data, not to mention processing costs in the cloud due to CPU time, storage, and the associated environmental impact.
Reducing costs, then, can be a key reason to embrace edge computing. Industrial organisations that initially relied upon the cloud may want to consider the cost impact of developing IoT solutions that instead turn to edge computing. This can have a significant effect on the bottom line, depending on the scale of your business’ IoT operations.
Nevertheless, it’s important to remember that this is not a question of edge computing versus the cloud. Rather, for industrial scenarios where a vast amount of data is being generated, there’s a need to be mindful of this demand when deciding on the best computing framework for your project, and this will purely come down to the application in question.
This is an approach that Nordic Semiconductors, a fabless semiconductor company specialising in wireless communication technology powering the IoT, agrees with. Lorenzo Amicucci, Business Development Manager, explains: “You may find there are too many other devices competing for the WiFi bandwidth or the power demands restrict your ability to scale. There is always a trade-off to be made. For example, how much latency can you tolerate? If you are developing an asset tracker, it may be that you only need to send data if the asset is moving. If it isn’t moving, are regular updates necessary?”
By assessing the requirements that an IoT application must satisfy, as well as the demands placed on it, you can determine whether edge computing is right for you.
How can edge computing reduce latency issues?
Latency is the other critical issue here. More and more, the latest IoT applications for industrial environments require fast processing and response times. For many large-scale industrial operations, for instance, a key piece of equipment being out of action can be costly in terms of downtime and a brand’s reputation if deadlines fail to be met.
Depending on the location of cloud servers, communication can range from a few seconds to a few minutes. For many industrial businesses looking to maintain a competitive edge, this is simply too slow.
So, for time-sensitive and data-intensive applications, edge computing can offer unrivalled potential.
Furthermore, you may even find that leveraging a combination of the two technologies will deliver the best results. For example, the best option for automated industrial technology might be an on-site embedded computer, which operates the device in question while performing complex data calculations fast, and then sends selected information to the cloud.
What support do I need with edge computing?
With cloud computing, all that’s required is to request the resources and build the application frontend. In contrast, edge computing is a more involved process upfront, with the backend needing to be built and developed in line with the application’s requirements. However, many industrial businesses will reap the benefits of this approach in the long run.
Therefore, if you’re looking to build and develop an industrial IoT solution that takes advantage of edge computing, we would recommend seeking the advice and support of a highly skilled, proven and trusted partner to help you along this journey.
What other factors should I consider for IIoT solutions?
To further assist industrial businesses at the point of embarking on IoT projects, we have created a free new white paper, which also takes into account this and other vital considerations such as location, computational power, security and reliability, and visualising the data.
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