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Why Edge computing and IoT got Married

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Why Edge computing and IoT got Married

We have seen a significant push from computing in the cloud to running applications on the “edge.” Why is this, and what does it have to do with the Internet of Things?


What is Edge computing and why did IoT wed them?

Wikipedia defines Edge Computing as a method of optimizing cloud computing systems “by taking control of computing applications, data, and services away from some central nodes to the other logical extreme of the Internet” which makes contact with the physical world. In other words, we are not using other people’s servers anymore we are buying our own and placing them in the location we are using this. If this sounds familiar to you, then you have been around this space for a while. Because back in the early 00’s there was not a “cloud” option.

You had two choices:

  1. You could buy or build a server and connect it to the internet.
  2. You could pay a monthly fee to use an already built computer at some facility.


Does it sound like we are moving backward? Yes, a little but the main difference here is that we are not building large personal data centers, we are deploying small bite size servers that host small applications that make sense for the facility it is in. Paired up with the Internet of Things and it becomes compelling.


Edge Computing with IoT

When pairing the power of Edge computing with an Internet of Things project the power starts to show itself. Edge computing on its own has massive benefits for the local facility using it but now images if the massive amount of IoT data was streaming to a local server and application vs. a cloud system. Imagine sub-second response times or actual real-time data. Companies often tout real-time data, but if you have to take information transmitted from a local facility to the cloud just to come back down to the facility again, there will be a delay. Moving data transfer all local changes the game in real-time applications.

What we get out of the Edge + IoT Marriage.

  • Extra Security.
  • True Real-Time data not “Real-Time data.”
  • Decisions made at the local facility. (No Lag time)
  • Larger ROI’s.
  • Reduced data transmission.
  • Big Data projects are nothing to be feared with batching.

This type of solution also changes the game when it comes to data security, and in an ever unsecured world having the extra security of knowing your data never left the facility is vital.

There are some drawbacks to going entirely private or 100% on premise. You lose the reason we have cloud computing and SaaS applications, to begin with! Updates to local security or application improvements have to be all a very manual process and require access to the physical hardware or at the minimum access to the local secure network. When using a “partial” online/offline edge device that offers some cloud or platform sync you now get the benefit of both worlds. This is why edge computing and IoT are holding hands and making a big entrance into not only Enterprise markets but small and medium-sized companies.


Edge + IoT = Big Data Love

If you recently have done any IoT Big Data project, you will quickly understand the benefits of pre-processing the data on-premise first before sending it to the cloud or some processing platform. If you have not worked with this type of project, I will briefly cover the struggle here.


See an example of a Big Data IoT Real-Time Location Services example here!

Our findings show that this client uses up to 5.6 million messages a month on a single Hub! That’s right, 5.6 million! As we did more digging, we see that this was not even the busiest Hub in the deployment. Read more…


Big Data IoT projects often rely on millions and millions of data points, so that they can be mined for patterns, habits, recurring themes and other information. Your data engineer wants more data to mine, but your wallet and ROI are suffering due to the high cost of sending that much data from the physical location to the cloud service you are using to process and sift the data. This is where Edge computing starts to get its stride, image not going to send all of the data to the cloud. Imagine you only sent the processed and finished data to be stored in the cloud for later use. This instantly cuts down on your spend per month! Now, I know there are other use cases here, what if you are already embedded with AWS or MS Azure and use a service they provide that can not be pulled to the edge? Edge computing again to the rescue. If you cant use something like AWS Greengrass and process data at the edge with an embedded service that is built to run on edge devices, then the solution becomes batching. Edge devices will allow you to batch the mounds of data up into a single message thus reducing your spend per month, while it’s not as good as processing the data on the edge it is still a cheap way to get some of the ROI back.


Have a happy marriage!

While edge computing continues to grow on its own, away from the edge + IoT marriage, we will see improvements made that will benefit the Internet of Things and possibly change the way we even work with IoT.

Regardless, we wish the two a happy and long-lasting marriage!


Take the next step!

Interested in bringing Edge computing to your Internet of Things project or product? Echolo has developed groundbreaking technology in the Edge and IoT space. Our On-Premise service gives you the power of an IoT Network and Platform on your personal and private network. You can view more here or feel free to reach out and have a quick chat with us!


Have a different opinion or an objection to this wedding? Let us know in the comments below!

Greg Winn

Greg is a highly qualified software engineer and expert in big data, with extensive experience in the development of guidance and avoidance systems for high-powered rockets. A veteran of the Air Force, he has spent over a decade at the National Association of Rocketry and has also worked in the online gaming industry. In 2002, Greg founded, a community site for the NovaLogic video game Delta Force, which was later acquired by Playnet Inc. He has since launched several web platforms and SaaS products, including Cignal, a big data Twitter sentiment analysis and predictive tool. Greg has worked with leading companies and organizations such as NASA, Ackerman & McQueen,, and the NRA, gaining a reputation as an authority on how to create and scale world-class software products with startup development teams. His expertise in software engineering, hardware engineering, and big data make him a valuable asset to Echolo's IoT products and roadmap.

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