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The Next Top IoT Use Case

The Internet of Things (IoT) continues on it’s upward trajectory, leading to an ever increasing number of connected devices and data points being generated.

According to IoT Analytics, two of the top 3 use cases for IoT utilizes sensors and data to connect and monitor remote assets. Equipment previously only accessible by traveling physically to them can now be monitored in a central location thanks to the growth of IoT.

Further down the list is the use case of Predictive Maintenance (PdM). In many ways, PdM has become a buzz word and has taken on different meanings with a focus on reducing the cost of equipment maintenance and downtime.

To date, PdM has largely focused on monitoring machinery within manufacturing, and to a lesser extent heavy equipment within areas such as oil + gas, aviation, and rail.

However, as those areas mature in terms of new connected devices. IoT growth as a whole is still accelerating.

Predictive Maintenance for Remote Assets/Equipment

Looking at the expected increase in IoT connections, and coupling that with the top use cases, you can logically infer that the largest area of future growth will be in connecting old and new remote equipment.

So, this raises the question, is predictive maintenance for remote assets the next key use case and untapped market for IoT.

Remote Assets

First off, what is included in the definition of a remote asset? Remote assets can be assets on the move such as trucks for logistics and supply chain companies, or geographically dispersed such as water pumps located in a remote field, but it could also just mean equipment that is difficult to physically access or that's not readily in front of you to easily monitor such as an HVAC unit located on a roof. IoT is primed to connect these devices, generating data that AI is then primed to analyze.

AI-based Intelligent Monitoring

While AI-based PdM solutions offer the promise of being able to intelligently monitor remote equipment, a key challenge exists.

Current Challenge

Predictive Maintenance solutions that were early to the game were built by teams of data scientists, often manually, designed to be point solutions for industries noted above such as manufacturing. They are built for very specific machines. In addition, their AI-based approaches tend to require large amounts of historical data.

Focus on Simple, Fast, and Flexible

As you can see from the chart earlier, billions of devices are yet to be turned on meaning they have not yet generated a single data point of historical data. As a result, in order to turn PdM of remote assets into reality, we need a solution that is flexible enough to adjust to various types of equipment. It needs to be simple enough to use to puts the functionality in the operators hands without needing a team of data scientists to monitor a new machine. Finally, the AI approach needs to start with anomaly detection. Flip the problem on it's head and instead of learning to find patterns of problems instead learn normal to be able to then identify abnormal. This approach requires far less data and lead's to actionable insights in days not months or years.


The top IoT use cases show us where the value is today for the end user. Logically, to provide the next level of incremental value we can look across the existing use cases that have been proven and see which of them complement each other in a way that if combined they provide a new form of value. Predictive Maintenance and Remote Asset Monitoring do just that. So, is that the next big thing in IoT?

That appears to be where the market is going but like many innovations, road blocks exist. Elipsa is focused on providing plug-and-play predictive solutions to help turn the promise of this next way of innovation into reality to continue to provide higher levels of value to the Industrial IoT community.


Elipsa is a leader in delivering fully automated, scalable AI solutions for industrial IoT applications. Elipsa’s AI-based, Predictive Maintenance seamlessly deploys across any workflow on the edge or in the cloud, increasing the availability and output of critical equipment. Elipsa’s self-training AI models and bolt-on approach enable AI deployments that are simple, fast, and flexible. To learn more, please visit


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