IIoT adoption: What's on the horizon?

Working with senior leadership teams across multiple market segments over the past six months, such as automotive, energy, transportation, manufacturing and government, has been nothing short of amazing to experience. It is easy to see why the global industrial IoT market is expected to reach a value of $922.62 billion by 2025 according to recent a Million Insights report. This growth is due to the worldwide rise in IoT technology development and implementation in the past few years.

I have been able to personally witness some of this explosive growth of software development and industrial IoT development over the past six months at Deasil Cognitive. With the core team being together for over 20 years and having extensive experience in collecting, moving, buffering, queueing and processing data, and building frameworks around the implementation of bleeding-edge technologies in artificial intelligence, machine learning and blockchain, the team is able to rapidly develop applications, quickly integrate new sensors and data acquisition devices, and implement game-changing sysetms for these market segments.

I have watched the Deasil team significantly improve productivity and ROI across multiple client projects, using Kubernetes, Docker, Golang, Cassandra, Kafka and Elastic, to name a few. The team is developing highly productive, stable, clean and faster applications and the results are beautiful and innovative, including IoT management systems, IoT implementations, mobile applications, business intelligence and data management platforms.

Some of the projects include figuring out how to use the massive amounts of data being collected on vehicles to provide more personalized services to drivers. These types of systems are designed to give customers a more personalized experience while also driving more customer loyalty and new revenue streams for the automotive industry. Applications for the energy sector have included streaming data from numerous sources to provide insights on where to allocate funds for potentially catastrophic risks, reduce product loss, property damage and even environmental risks.

As an authorized government software development partner, we have been able to identify ways to help the Department of Energy analyze Synchrophasor data. Synchrophasors are time-synchronized numbers that represent both the magnitude and phase angle of the sine waves found in electricity, and are time-synchronized for accuracy. They are measured by high-speed monitors called Phasor Measurement Units, or PMUs, that are 100 times faster than SCADA. These types of applications will help greatly mitigate risks, improve quality of service and reduce unforeseen downtime for utility companies.

Industrial IoT and software development technologies don’t always have to be so complicated, though. Figuring out ways to help companies digitize data that is being collected across the globe — where there are many mistakes and costly inefficiencies — is a great first step in helping companies begin their industrial IoT journey. In just being able to digitize the data, customers can begin to see a real benefit of reducing waste. The second step is where the magic happens. This is where they can begin to easily understand whether vendors are meeting the deadlines on delivery of thousands of components used in the field or whether or not customers are using their buying power across the many projects and vendors available to get the best pricing for the company. Data automation platforms, like Acuity, help enable companies to greatly improve operations, customer experience — with on-time delivery of projects — and notably improve their profits. Combining this kind of application with live operations is nirvana, especially where early warning signals can be tied to vendors to, for example, drop ship replacement parts, engines or pumps to reduce downtime.

What do I see coming down the pipe for industrial IoT and blockchain? For one, we are merging machine learning with blockchain. Machine learning relies on vast quantities of data to build models for accurate prediction. A great deal of the overhead incurred in securing this data lies in collecting, organizing and auditing it for accuracy. This is an area that can significantly be improved using blockchain. By using smart contracts, data can be directly and reliably transferred straight from its place of origin and improve the whole process significantly by using digital signatures.

I am looking forward to sharing more soon.

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