How edge computing can facilitate IoT adoption

How edge computing can facilitate IoT adoption

Much has been written about the IoT revolution and the ability of technology to revolutionize industries, transform productivity and unlock new levels of knowledge. But, for those intrigued by the possibilities and looking to dip their toes in the water, the potential myths of high price, infrastructure and connectivity challenges, and the skill set required, can be significant obstacles that seem insurmountable.

When it comes to the reality of Industrial IoT (IIoT), many organizations must consider the cost, time, and disruption of a new installation. The prospect of having to tear down and replace new infrastructure to support the IoT is not a viable option for many businesses.

Addressing IoT Implementation Challenges

Edge IoT and analytics can provide a powerful mechanism to translate complex data sources into a streamlined, lower-cost platform with faster ROI and higher value. However, businesses face five key challenges when considering an IoT implementation.

1. Investment

The transformative potential of IoT across multiple industries is staggering and much discussion has taken place about its power to revolutionize business models. But, while the possibilities for market sectors are hugely exciting, the reality of many of these industry IoT offerings is that they are designed for broad use cases – configurations are complex and complex, with incredibly powerful networking capabilities that require significant investment and skill. execute.

Major players in the IoT space, including AWS and Microsoft, require huge up-front investments in IoT stacks and other data center-integrated hardware, as well as personnel who can code the solution, write it and build it – that’s potentially hundreds of thousands of dollars before an organization even gets any potential data or insights.

ROI is something the IoT space lacks, leading to failed proof of concepts. One of the first use cases of IoT – smart meters – is a case where it is simple to calculate the return on investment, because organizations do not have to send meter readers to sites and it there is an immediate financial benefit.

But, with IIoT, it’s much more than that. Maybe it exposes some savings and maybe less machine maintenance is needed. Savings are more difficult to identify initially; therefore, a large initial investment in this type of solution is difficult to justify in these circumstances.

2. Tear and replace

In many industrial cases, the existing machines that require monitoring include large, complex and expensive structures. These machines are tailored and built for the task at hand, and for this reason they must be monitored non-invasively.

Many facilities have been designed and built at the cost of billions of dollars, and organizations cannot begin to extract and replace components because cloud-based technology provides an advantage that has yet to be quantified.

Conversely, many IoT offerings in the market depend on embedding IoT into the infrastructure from the outset, a concept that could lead to significant disruptions and downtime.

3. Skill sets

The skill set required to manage these types of complex configurations is also a significant barrier for many organizations. A high proportion of IoT customers in manufacturing are not necessarily computer savvy like traditional database users are. With many vendors needing someone who can effectively manage these platforms, this is an issue that hurts the chances of adoption in this industry.

Businesses need a way to extract data from IoT devices without the complex ecosystem around them through a simplified platform that only requires a browser to access. This means organizations need to consider whether they can afford to hire a dedicated IoT professional and how this role can provide value.

4. Infrastructure

Another stumbling block for many IoT projects is that the infrastructure is not developed if the location is in an inconvenient place without reliable Wi-Fi – the only clouds available are those floating in the sky . In this case, having an IoT solution that collects all the data, analyzes it at the point of collection and allows fast and reliable visibility of what is happening can make all the difference and is a much more pragmatic solution, both in large factories and remote locations. It’s the difference between the original vision of the IoT and what it is in practice.

5. IoT at the Edge

The vision of the IoT and the reality are significantly different. A sensor’s yes or no response is different from deciding whether a complex machine is performing as it should and at optimum levels of efficiency. It’s not just about the ability to collect data, but also having the ability to modify that data collection and add additional sensors to expand the collected data even further.

For example, the configuration might monitor temperature and speed, but then need to measure vibration. This requires another sensor, so the platform must be adaptable and scalable. In today’s industrial environment, IT teams must be flexible and ready to scale, both in terms of the size and complexity of the data collected.

As edge computing, which analyzes data at the point of creation, gains momentum, organizations are discovering how they can quickly access only the most valuable real-time data that is critical to their business.

Going back to the smart meter example, this type of IoT deployment involves millions of identical devices with the same data and a single purpose. This remains an investment, but the principle is simply to connect several homogeneous devices together. This is unlike today’s industrial environment, where there might be a handful or even tens of thousands of different devices, each performing slightly different tasks in different ways.

This specialized equipment therefore requires an IoT edge solution that can accurately translate, measure and analyze different data formats as the data arrives without having to extract and replace internal machine electronics.

Edge allows data processing to be performed on edge nodes before only transmitting aggregated data to the central server. Instead of transmitting huge volumes of data every minute, this could be reduced to a few messages every five minutes, depending on the metering use case.

This results in a massive reduction in bandwidth, so that the cellular network becomes profitable, which in turn reduces infrastructure costs and creates faster ROI and value.

For companies deciding to get started with IoT, edge computing eliminates the need for massively complex and expensive deployment. Edge computing can provide a way to set up a project, provide data points, and provide insight into how a business can get more out of IoT with a data-driven strategy.

Conclusion

The vast capabilities of IoT deployments are widely publicized. Many companies are unfamiliar with the availability of simple and affordable entry-level IoT capabilities to provide data analytics at the edge, where only the most valuable data collected is shared in real time, making the process more cost-effective .

Enterprise solutions like AWS and Microsoft have their place, but most companies that don’t have the huge use cases to warrant dedicated attention and support from major players are left to fend for themselves. Instead, a small-scale offering that integrates big data, edge, and IoT into a small footprint will have a significant impact, which is also easily scalable without the need to overhaul existing infrastructure.

About the Author
Peter Ruffley is the founder of Zizo Software and has over 40 years of experience in the IT industry, including working with some of the biggest data technologies, such as Oracle, IBM and Ingres. With a keen interest in cloud analytics technologies, he understood that the shift to cloud analytics was underway and assembled a team to create a new kind of technology, suitable for delivering big data analytics services. and large-scale model databases in the cloud.

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