5 Fog Computing Examples from Foghorn
We recently wrote an article about “fog computing” and how it is a simple concept that involves moving your computers closer to the sensors they are talking to. That’s it really. Whereas before we had sensors that talked to “the cloud“, it turns out that we don’t have sufficient bandwidth and/or connectivity that enable the responsive type of communication we need to make our IoT concept fire on all cylinders. While “cloud computing” was all about centralization, “fog computing” is all about decentralization. While the whole thing reeks of a marketing spin, there is a startup specifically targeting fog computing called Foghorn.
Just a few weeks ago, Foghorn closed a $12 million Series A funding from investors that included General Electric. Foghorn put some of that funding to good use and had their marketing team create the following buzzword laden tagline to let everyone know what an exciting value proposition they have:
IIoT, Fog Computing & The Intelligent Edge
Update 02/26/2020: FogHorn has raised $25 million in Series C funding to invest in continued product innovation and deepen its portfolio of industry use cases. This brings the company’s total funding to $72.5 million to date.
Have you heard of IIoT? That’s IoT which an extra “I” in front of it and it stands for “Industrial Internet of Things”. Fog computing we already discussed. What about, “the intelligent edge”? That’s just edge computing which is the same as fog computing. So what is this tagline really saying? It says that you should move some computers closer to the sensors you are trying to communicate with so you have increased responsiveness, less bandwidth requirements, and less of a reliance on the cloud. However, it wouldn’t be fair to Foghorn’s marketing team if we didn’t obfuscate the simplicity of their solution with the following cryptic diagram:
In addition to creating cool diagrams like the one you see above, Foghorn has put together a pretty interesting set of “fog computing” use cases for their technology in IIoT. Here are 5 of their fog computing examples.
Mining: Today’s mining companies are moving more towards using autonomous trucks and trains as well as tunneling and boring machines to ensure worker safety and increase productivity. This sophisticated type of mining equipment can generate terabytes of data in a single day through the course of normal operations. When you’re working in harsh conditions, not to mention 100s of feet underground, reliable networks and cloud connectivity are typically unavailable. That’s where “the fog” comes into play. Do all that processing locally and then send a small upload up to the cloud at the end of the day.
Wind Farms: We talked about wind turbine blades before and realized that a wind turbine is a very sophisticated piece of equipment. Depending on the weather, you can make changes to your wind farm turbines in real-time to optimize electricity output. The problem is of course that most wind farms are located in remote areas. “Edge intelligence” lets you analyze data locally in real-time without relying on continuous wide area network availability.
Trains: As part of the rise in the Industrial Internet of Things or IIoT, trains and tracks are being equipped with a new generation of instruments and sensors with locomotives acting as the central hub for all the data gathered from these sensors. The problem is that trains move fast and it’s difficult to maintain a connection with “the cloud”. Install some fog computing nodes in your locomotive and you’re golden.
Pipelines: Did you know that a 100-mile long section of oil pipeline generates gigabytes of data during operation? This amounts to many terabytes of data over time, given that there are 2.5 million miles of oil and gas pipelines in the United States alone. This example of fog computing seems a bit tougher to visualize but one would imagine that you install a fog computing node at regular intervals and they speak to each other all the way down the line. In the end, the cloud only needs to know what’s important at a high level so the bandwidth needs are much less.
Oil Wells: The Electric Submersible Pump (ESP) lies at the center of an oil well extracting oil and pumping it to the surface. That far under the ground, there isn’t a whole lot of connectivity available to talk to “the cloud”. An ESP failure will close a well and can be very costly to replace. A real-time edge intelligence solution can monitor the operational data gathered from the ESP and apply advanced analytics in real-time to predict failures and increase uptime for oil wells.
These are all great fog computing examples from the world of IIoT, but we’re not really seeing barriers to entry here for this business model. Foghorn has built software that creates the fog computing layer while integrating with the HANA cloud platform which is built by SAP. They’ve partnered with big players in the industry like Microsoft and Cisco. You could say they have first-mover advantage but it doesn’t seem like anything is stopping any other startup from establishing the same partnerships and offering the same value proposition but perhaps with a different buzzword heavy tagline like “pushing cloud-ready fog intelligence solutions to the edge“. We’ll just have to wait and see what happens.
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