How edge computing can ease IoT adoption

Much has been written about the IoT revolution and how the technology can revolutionize industries, transform productivity and unlock new insights. But for those intrigued by the possibilities and wanting to dip their toe in the water, the potential myths of high price, infrastructure and connectivity challenges, and required skills can present significant hurdles that seem insurmountable.

When addressing the reality of Industrial IoT (IIoT), many organizations must consider the cost, time, and disruption associated with a new facility. The prospect of having to dismantle and replace new infrastructure to support the IoT is not a viable option for many organizations.

Overcome the challenges of IoT implementation

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

1. Investment

The transformative potential of IoT across multiple industries is overwhelming, and its power to revolutionize business models has been much debated. But while the opportunities for market sectors are hugely exciting, the reality of many of these industrial IoT offerings is that they are designed for huge use cases – the setups are intricate and complex, with incredibly powerful networking capabilities that require significant investment and skill to execute.

Major players in the IoT space, including AWS and Microsoft, require huge upfront investments in IoT stacks and other hardware integrated into the data center, as well as manpower to code, write, and build the solution—potentially hundreds of thousands of dollars ahead a company receives potential data or insights.

ROI is something the IoT space is lacking, causing proof of concepts to fail. An early use case for IoT – smart meters – is one where it is easy to determine the ROI as companies do not have to ship meter readers to sites and there is an immediate cost benefit.

But with IIoT, it’s much more than that. Perhaps some savings will be seen as a result, and perhaps less machine maintenance will be required. Savings are harder to see at first; therefore, high upfront investments in this type of solution are difficult to justify in these circumstances.

2. Rip and replace

In many industrial cases, the existing machinery that needs to be monitored includes large, complex, and expensive structures. These machines are fit and built for the task at hand and should therefore be monitored non-invasively.

Many facilities have been designed and built for billions of dollars, and companies can’t start ripping and replacing components because cloud-enabled technology offers an as yet unquantified benefit.

Conversely, many of the IoT offerings on the market depend on IoT being built into infrastructure from the start – a concept that can result in significant business disruption and downtime.

3. Skills

The skills required to manage these types of complex setups also present a significant hurdle for many organizations. A high proportion of manufacturing IoT customers are not necessarily as IT savvy as traditional database users. With many vendors requiring someone who can effectively manage these platforms, this is an issue hampering the chances of adoption in this sector.

Businesses need a way to get data out of the IoT devices without the complex ecosystem that surrounds them, via a streamlined platform that only requires a browser to access. This means companies need to figure out if they can afford to hire a dedicated IoT expert and how that role can add value.

4. Infrastructure

Another stumbling block for many IoT projects is the infrastructure, which will not be developed if the site is in an awkward location with no reliable WiFi – the only clouds available are those floating in the sky. In this case, an IoT solution that collects all the data, analyzes it at the point of collection and allows a quick and reliable visibility of what is happening, can make the difference and is a much more pragmatic solution, both in large factories and remote locations. This is the difference between the original vision of IoT and what it is in practice.

5. IoT on the edge

The vision of IoT and reality differ significantly. A sensor’s yes or no answer is different from deciding whether a complex piece of machinery is operating properly and at optimal efficiency. It’s not just about the ability to collect data, but also the ability to modify that data collection and add additional sensors to augment the collected data even further.

For example, the setup might monitor temperature and RPM, but then need to measure vibration. This requires another sensor, so the platform needs to be adaptable and scalable. In the current industrial sector environment, IT teams must be flexible and ready to scale, both in terms of size and complexity of the data being collected.

As edge computing, which analyzes data at its point of origin, gains traction, companies are discovering how to access only the most valuable data that is proving to be mission-critical to their business, quickly and in real-time.

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. It’s still an investment, but the principle is simply to connect several homogeneous devices together. This is unlike today’s industrial environment, where there can be a handful or even tens of thousands of different devices, all doing 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 tear down and replace the machine’s internal electronics.

Edge enables data processing on the edge nodes before only the aggregated data is transferred to the central server. Instead of sending huge amounts of data every minute, this could be reduced to a few messages every five minutes, depending on the measurement use case.

This results in a massive reduction in bandwidth, making the cellular network cost-effective, which then lowers infrastructure costs and creates faster ROI and value.

For companies that decide to get started with IoT, edge computing eliminates the need for extremely complex and costly deployments. Edge computing can provide a way to get a project up and running, providing data points and insights into how a company with a data-driven strategy can further capitalize on IoT.

Conclusion

The tremendous possibilities of IoT deployments are well known. Many companies are unfamiliar with the availability of simple, 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-grade solutions like AWS and Microsoft have their place, but most organizations that don’t have the massive use cases to justify the dedicated attention and support of key players are left to their own devices. Instead, a small offering that integrates big data, edge, and IoT in a small space will have a significant impact that also scales easily without overhauling existing infrastructure.

About the author
Peter Ruffley is the founder of Zizo Software and has over 40 years of extensive IT industry experience including working with some of the largest data technologies such as Oracle, IBM and Ingres. With a strong interest in cloud analytics technologies, he understood that the shift to cloud analytics was underway and assembled a team to develop a new type of technology capable of enabling big data analytics and pattern database services at scale Deploy scale in the cloud.

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