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Got Coding Problems? Try a No-Code Solution

iiot, ai technology, iot projects

For many IoT projects, complexity can be a killer. Developers often need to work with a variety of legacy systems, each with its own programming language, toolset, protocols, and so forth. They need to interpret the data coming off these old systems and figure out how to get this newly acquired data to the right destination—and that can mean learning another set of APIs and protocols. For example, the data might need to go to a web dashboard, trigger SMS alerts, or be integrated into a variety of enterprise applications.

Just transporting the data can be a problem. Information often needs to flow over a variety of networks, including specialized real-time networks at the edge as well as enterprise IT networks in the back office. Here again developers need a diverse range of skills.

And of course more and more applications call for AI technologies like machine learning or predictive maintenance—yet another skill set that is difficult to acquire.

Up and Running? It’s Not Over Yet

Companies that manage to solve these problems and proceed to full deployment face a new hurdle: making use of the information that their IoT devices gather. IoT systems typically combine many disparate sources of information to extract useful insights. This kind of collaborative approach often does not mesh with the reality of how data is siloed within an organization.

Sometimes that siloing is physical and technical, like an old mission-critical control system that was never intended to interface with modern networking equipment. Sometimes, the difficulty is cultural or institutional. Some employees may not see the value of cross-silo analysis, particularly if required to work cooperatively with other business units they typically compete with.

A Fast-Track Solution

But in a way, that these problems are so common is actually good news. Because these issues threaten IoT deployments in many different industries, there’s been a great deal of attention focused on solutions like Webee that can streamline the entire IIoT deployment process, from earliest scale-out to penetrating data silos and performing analytics.

Webee describes its Machine Real-Time Monitoring Solution as “a no-coding end-to-end toolset powered by AI, IoT, and Computer Vision.” Its Intel® Core-based hardware platform integrates support for Intel® OpenVINO and uses some of the computer vision algorithms that ship with the toolkit.

For many IoT projects, complexity is a killer. A no-code approach from @webeelife can bring your efforts back to life.
Via @insightdottech

Rather than relying on specific coding skills or familiarity with one vendor’s software, Webee’s software offers an intuitive visual interface for configuring sensor data flows and hardware. As illustrated in Video 1, this approach enables a hardware- and platform-agnostic approach that focuses on functionality rather than architecture.


Video 1. Webee offers a no-coding toolset that simplifies construction of complex applications. (Source: Webee)


This novel approach can dramatically speed up development. For example, this no-code approach helped Panasonic launch its Cosmos indoor air quality monitoring system on a tight schedule. “From the beginning, Webee’s toolset really helps us efficiently deploy solutions at a fraction of the time,” says Jim French, President of Panasonic R&D.

Leaping the Silo and Scaling Hurdles

Of course, the first hurdle is getting devices to talk. “In most cases, customers already have the information they want to process,” explains Lucas Funes, CEO of Webee. “They either cannot access the data across data silos as easily as they need to, or they cannot analyze it in real time.”

That’s why Webee focuses on bringing disparate solutions into a unified whole. “We connect directly to PLCs or OPC UA servers to extract the data from configured machine tags,” says Funes, who notes that SCADA is also supported. From there, the Intel® NUC that is the backbone of this solution connects to the LAN production network through Ethernet or Wifi.

Once everything is connected, the Webee tools are invaluable for building a scalable solution. “The way we see it is that really the challenges start when you get everything uploaded and you get the data. What do you do with all the information you suddenly have access to? How do you scale from having a couple of sensors to integrating contextual data with third-party databases?” says Cecilia Flores, CMO and co-founder of Webee.

“Everyone connects sensors. Everyone extracts data. But then what to do with that when we already have it? How do we make real value out of the data that we are collecting? That’s where the complexities of IoT start.”

Stop Running Into the Same Problems

The explicit goal of Webee is to make it easier for company engineers who aren’t IoT specialists to extract useful information from easily programmed dataflows. If you’ve run into problems with an IoT deployment and are struggling with building a software stack that can extract useful information from the hardware you’ve put into service, there may be an easier way to solve the problem.

About the Author

Kenton Williston is an Editorial Consultant to and previously served as the Editor-in-Chief of the publication as well as the editor of its predecessor publication, the Embedded Innovator magazine. Kenton received his B.S. in Electrical Engineering in 2000 and has been writing about embedded computing and IoT ever since.

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