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Enterprise On-Premise IoT

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Enterprise On-Premise IoT

As the Internet of Thing solutions become more complex, require more physical devices and need more throughput, it becomes very costly to move all of that data from the physical world to the cloud without incurring massive data transfer and messaging costs. Your collected information is critically important, and you need to mine and process it to get that promised ROI on this new IoT product that you just implemented. Big Data is what IoT is all about, and we have all been informed that we need to collect as much data as we can. It’s just no one told us it was going to cost us an arm and a leg to get that massive amount of data to the cloud so we can see and ROI. In a world where IoT is set to change so much, how did we let this happen and how do we solve this critical issue?

Lets first look at some of the problems and scenarios that we might face with a data-heavy IoT solution.

RTLS and Location Services

Most of us are familiar with what RTLS (Real-Time Location Services) is but often we don’t understand the amount of data that an RTLS system can generate. In a recent study of an Echolo IoT Client that uses the location services to scan and look for Bluetooth Low Energy (BLE) devices that are attached to equipment; we looked at the amount of data transmitted per day, week and month on a single hub.

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. This Client had a single Hub that was sending on average 8.1 million messages a month, over the entire installation this single account can carry more than 261 million messages a month. Now, remember this client attaches end BLE beacons to equipment and has Hubs installed in fixed locations around the facility to look for the devices constantly. A single piece of equipment can cross multiple Hubs in a single day and the device attached to it will continuously send out a signal to let the Hubs know its still there.

When it comes to using location services, the more data, the better, so this customer is getting excellent results, but it’s a very high-cost situation. The Client has to evaluate every new end device introduced into the facility because it may create additional messaging and data transfer costs. Ultimately the client was not able to deploy additional devices and Hubs to complete full facility coverage and get the real value from the solution.

How did we solve this?

Echolo has developed a groundbreaking On-Premise IoT solution that can reduce this clients cost by over 60%. This unique utility is a physical device that is deployed at the location. Once in place all of the Hub information is routed to this local network device, allowing them to send communications through the local network to the On-Premise Edge solution. Once the Edge utility has the data, it can then make accurate real-time decisions and location calculations. The funny thing is that we all thought we where making real-time decisions in the cloud with IoT. Think again, welcome to on-premise decision making with sub-second response times!

Designed specifically for use with the Echolo IoT Platform, our on-premise utility will enable you to make decisions at the physical location without sending data to the cloud first. The On-Premises Edge utility allows for batching of data to the Echolo Cloud IoT Platform, giving your online applications access to the On-Premise data without having to send each point of data separately. The on-Premise Edge utility helps to reduce message and data costs effectively increasing the ROI by over 60%. Finally allowing the Client to deploy thoroughly and not have to worry about additional messaging and data costs associated with the more tracked equipment all while putting money back in the clients pocket!


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    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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