Problems and solutions for Industry 4.0

Problems and solutions for Industry 4.0

What happened to our fourth industrial revolution? The combination of IoT, cloud computing and big data analytics is bringing higher productivity, predictive maintenance and end-to-end process automation to all industrial operators. While these benefits have come and gone, we have yet to fully reimagine processes and systems—the ultimate promise of Industry 4.0.
One major reason Industry 4.0 got off to a rocky start: IoT solutions are still siled and monolithic, each trapped in its own narrow functional domain. Perhaps inevitable given the organic, ephemeral nature of the market, it is ironic that connectivity is one of the key values of IoT and Industry 4.0.
The truth is, siled IoT solutions are hindering innovation. Fortunately, new system architectures provide distributed data collection, storage, and processing—along with localized and secure central hubs. If Industry 4.0 is disrupted, these edge-to-cloud platforms are the solution.
How Edge Computing Can Unify the Industrial IoT
Most IIoT products are full-stack solutions: they include sensors or devices, data storage, cloud applications, and finally a dashboard or user interface. Users have no control over the data that flows through the stack; they don't even own the data that typically belongs to IoT manufacturers. Ethernet ввод-вывод
When every function of your business has a different full-stack IoT solution, IT architecture becomes a nightmare to use, let alone maintain: you have dozens of dashboards. You must update the device in the field. There is no communication between the systems. The pace of innovation has become too slow.
What's missing is a system with distributed storage and processing. Edge computing is just the beginning, but a far-edge platform is needed to fully realize this vision.
In an edge-computing architecture, data storage occurs close to the source (usually a sensor or device). Far-edge computing takes this distributed approach a step further by processing data where it is collected, that is, on or near the device where it is collected.
Far edge computing allows you to collect and process data that was previously unavailable. Or, if it was available before, it causes storage problems in the cloud - a problem that distributed local cloud storage can solve.
Make remote computing feasible
By itself, far edge computing alone is not enough. You also need a platform for creating a local cloud, combining multiple remote edge data "nodes" into one easy-to-use system. A platform like this can take distributed data storage and processing and make them available not only to users, but also to third-party applications—without creating yet another full-stack IoT system. Event-driven data logging makes such a platform possible.
An event-driven architecture creates an immutable stream of event data. This allows users to see the correlations between all the different data points and events, and add new applications, algorithms and functionality as needed. You retain ownership and control of all data. When you have access, you can innovate.
This distributed platform creates a local cloud, separate from the wider internet, but with full connectivity for every necessary application. This results in more secure integration with OT and control systems, a key consideration for critical infrastructure that you absolutely do not want exposed to external actors. We call such a solution an edge micro-cloud platform. This technology could be the key to breaking down the barriers that limit the effectiveness of IoT, finally realizing the true promise of Industry 4.0.
How Edge Micro-Cloud Platforms Work—and Realize the Promise of Industry 4.0
Innovative companies driving IoT implementations are the first to feel the sting of data silos, the dashed promise of Industry 4.0. Take aquaculture as an example: a fish farm might run 30+ separate IoT solutions, one monitoring water quality, another measuring feed waste, another counting lice, and so on. How helpful would it be if operators had to juggle 30 separate dashboards with no easy way to correlate data across systems?
Or imagine a cargo ship with 10 or 15 different digital systems (navigation, engine control, cargo monitoring, etc.). Each has its own vertical architecture and they cannot be connected to the internet for security reasons. When that ship came into port, technicians had to manually update each of these siled systems, costing shippers hundreds of thousands of dollars in downtime.
In these scenarios (and many more), the implementation of an edge micro-cloud platform starts with setting up a local cloud on an asset—whether it's a building, ship, production line, or coastal fish ring. A single application runs locally, in a micro-cloud with a private connection to an asset-based native cloud. For AI systems, you may need to occasionally connect to the wider commercial cloud to train algorithms - but once your model is ready to use, you close that connection for complete safety while the AI runs on your micro-cloud. Антенна и питание
An event-driven architecture maintains data integrity across applications and data sources. Crucially, the system separates the data collectors (sensors) from the computing layer (applications) from the user interface (dashboard). This provides full functionality across discrete operations within a single platform, reducing maintenance costs and simplifying the addition of new applications. Once you've set up your edge micro-cloud platform, you can easily customize the system without worrying about the security already built into the platform.
Edge computing platforms help every industrial operator achieve fact-based, cost-effective operations that maximize output and output while keeping downtime to an absolute minimum. In short, they deliver on the promise of Industry 4.0.

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Ebyte — национальное высокотехнологичное предприятие, специализирующееся на исследованиях и разработках беспроводных модулей и промышленных IoT-терминалов. Неза...
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