Industrial Internet of Things (IIoT) is the application of IoT in an industrial setting. IIoT is sometimes referred to as Industry 4.0, although the latter focuses primarily on the manufacturing sector, using advanced technologies to reduce waste and increase value in the sector. Here are IIoT trends and challenges to watch.

IIoT covers all areas in which industrial equipment is used.

Like Industry 4.0, IIoT will revolutionize processes through connected machines that can optimize productivity and revenue. IIoT can be seen in a wide variety of industries, from transportation to public safety and from energy to, of course, construction.

There are new trends in this area and we need to see why the challenges leaders are trying to manage these trends.

Solutions for the influx of data from disparate systems

More and more data is coming to anyone using IoT, but this is especially true in the world of IIoT. Operators have been overwhelmed by the huge amount of data, making it difficult to harness their power to make decisions. One of the reasons data is so hard to understand is that it comes from so many different systems.

Ursalio’s COO/CPO Angie Stitcher, the only company to offer photorealistic 3D digital twins combined with live sensor data, asset data, maintenance data, notes, “different types of data streams from different systems that connect to each other.” and cannot give a realistic view of what is happening in a given environment.

To help manage the influx of data, the technology is being deployed and creating a workflow that moves between these systems that enables employees and managers to quickly resolve issues and tools for problem-solving. gives.

IIoT. manufacturing success in

While current trends do not indicate an uptick in US manufacturing, some in the IIoT industry think this may change. Joy Weiss, president and CEO of Tempo Automation, a smart factory startup for printed circuit board assembly (PCBA), sees the trend coming to light. “We have seen an increasing trend among companies preferring to switch to US-based manufacturing partners.

Using these partners instead of contracting overseas for a variety of reasons, including the recent global health crisis due to the coronavirus,” she said. “Some of these benefits include geographic proximity, additional IP and security certifications and standards. Simultaneous use is included. US-sourced, authentic components, and parts. ,

Christine Kyle-Remert, CEO and founder, LoneStarTracking, a company that provides telematics solutions that includes the latest Cat-M1 cellular technology and cellular-free LoRaWAN deployments in North America, explains that power consumption, transmission distance and price There are three factors. Play a role in the successful deployment of this technology.

“As technology advances, sensors are becoming smaller, more lightweight and more affordable. However, no one has the time to run out and replace the batteries. Just a few years ago, IoT sensors were only 1-2 years old. However, today, we are installing sensors that can last up to 10+ years on a single coin cell battery,” Kyle-Remert explains.

“Using technology like LoRaWAN, IoT sensors can now communicate 10+ km and beyond with very little power. If you can develop a low-cost sensor, there’s nothing stopping you from installing more sensors to get denser coverage.”

 skill gap in the world of
Like many new technologies, a skills gap is permeated through this industry. Ekaterina Lyapina, solutions architect and AI and IIoT consultant at Zyfra, a company that develops industrial digitization technologies for machinery, metallurgy, mining and oil and gas notes, “Qualifications required to install new smart robots in production lines Often not available most companies.

Facilities and factories lack free time and robotic technicians to update their ongoing production. This puts them behind AI and IIoT trends, as they are not able to use the latest robotics technology. They are losing skills in the integration, implementation and debugging of Artificial Intelligence Enhanced Systems.

So, the hindering factor in AI automation is the competency of the workers on the most important front. Digging into the treasury of AI, especially for training and optimizing neural networks, requires in-depth expert knowledge.”

Stitcher offers a potential solution to this skills gap, noting, “Virtualization is driving down costs for training in many areas. Digital twins and 3D models make it easier to train employees as they do in real-world environments.” reflect and shorten the learning curve.

Coupled with combining and scaling data from multiple systems, digital twins also provide a realistic and easily accessible information hub for the current state of the environment. ,

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