Industrial Data: In the Cloud and On the Edge
“Some say data is the new oil. If that’s the case, then data analytics is the new engine that propels the IIoT (Industrial Internet of Things) transformation,” concludes the Industrial Internet Consortium (IIC). Industries across the board—from machine building to automotive to infrastructure—are undergoing a massive digital transformation. Data is at the heart of this revolution.
For industrial contexts, this data’s genesis is the IIoT. This “refers to the billions of industrial devices -- anything from the machines in a factory to the engines inside an airplane -- that are filled with sensors, connected to wireless networks, and gathering and sharing data,” writes Steve Ranger for ZDNet. “Worldwide spending on the Internet of Things (IoT) is forecast to reach $745 billion in 2019 – up from the $646 billion spent in 2018 -- and likely to hit $1 trillion in 2022. The vast majority of that spending will be by businesses.”
This leads us to ask: what do we do with all this industrial data?
The Great Cloud Migration
The cloud requires no introduction. Within the last decade, enterprises of all sizes migrated large portions of their data systems to centralized clouds. Mordor Intelligence reports that “cloud computing technology is expected to generate a revenue of USD $411 billion by 2020,” and that “the average enterprise uses a staggering 1,427 distinct cloud services, a threefold increase from that in 2013.” The cloud enables organizations to access more sophisticated infrastructures at a fraction of the cost. We only need to pay for what we need, and we don’t have to worry about building and maintaining secure, redundant systems.
For all its capabilities, cloud computing has some major flaws in industrial applications. Certain IIoT devices, such as self-driving cars or mission-critical manufacturing equipment, cannot rely on the cloud for three main reasons. First, a failed connection can spell disaster, both for safety and revenue. Second, lag-time resulting from latency as data travels to and from the cloud reduces efficiency and operability. Third, the bandwidth requirements for transmitting all of this data drives up costs.
Edge Computing in Industry 4.0
The fourth industrial revolution, Industry 4.0, is on the horizon. TrendMicro explains that, in this context, “IIoT is integral to how cyber-physical systems and production processes are set to transform with the help of big data and analytics. Real-time data from sensors and other information sources helps industrial devices and infrastructures in their ‘decision-making,’ in coming up with insights and specific actions. Machines are further enabled to take on and automate tasks that previous industrial revolutions could not handle.”
These IIoT systems, however, cannot rely on cloud computing alone. They need real-time processing, they need full offline functionality, and they simply cannot rely on geographically distant processors. They need edge computing.
The Verge's Paul Miller provides a definition: “Edge computing is computing that’s done at or near the source of the data, instead of relying on the cloud at one of a dozen data centers to do all the work. It doesn’t mean the cloud will disappear. It means the cloud is coming to you.”
While the edge has its limitations—namely less processing power and new security concerns—this new type of computing opens new possibilities and offers a remedy to IIoT’s issues with the cloud.
The Edge-Cloud Continuum
The edge and the cloud sit at two poles of a single continuum. “The edge,” clarifies the IIC, “is a logical layer rather than a specific physical divide, so it is open to individual opinion and interpretation of ‘where’ the edge is…From the business perspective, the location of the edge depends on the business problem or “key objectives” to be addressed.”
The edge may be at a device, at a production floor, or even at a plant’s gateway to the cloud. Some marquee features of edge computing are localized processing capabilities, low-latency communications, and real-time control. IIoT devices can run basic decision-making analytics on the edge, and they can also communicate with other devices on the edge to synchronize their processes and work in harmony.
Furthermore, edge computing enables data caching, buffering, and streaming; data pre-processing, cleansing, filtering, and optimizing; and data aggregation. Edge devices can handle their day-to-day operations and then send regular, compiled reports to the cloud.
Since the cloud is much more powerful, it can then use data from the edge for complex analytics, big data mining, advanced visualizations, and machine learning rules. The cloud also provides a centralized area for long-term data storage.
In the edge-cloud continuum, neither pole can function without the other. Edge devices rely on analytics and ‘instruction’ from the cloud, while the cloud meanwhile requires aggerated data from the edge. Moreover, edge computing compensates for the cloud’s shortcomings: latency, bandwidth, and connectivity concerns.
That’s why the best approach is a hybrid model. Edge computing and cloud computing complement each other by compensating for the other’s weaknesses. Together, they form one integrated system that combines their respective advantages.
A Business Case for the Edge
As companies like Microsoft begin developing edge architecture for their Azure IoT systems, they need a strong business case to garner industry support and stakeholder buy-in. We need to demonstrate tangible benefits to justify an investment in this technology.
“The business case for edge computing,” concludes Open Automation Software, “is driven by cost savings in computing power and bandwidth as well as its ability to provide faster and more accurate access to automation data at its source. For many IIoT applications, edge computing is a reliable and cost-effective way to ensure data quality, freshness, accuracy and speed of delivery. Other factors such as the quantities of data transferred, and bandwidth costs will determine the ultimate mix of cloud and edge services.”
Conclusion
Imagine a self-driving car taking advantage of the edge-cloud continuum. Sensors placed throughout the engine can use edge computing to predict failures and schedule repairs. It then sends aggregate data to the cloud, which, after performing big data analytics, informs the engine’s processors about opportunities for optimization. The passengers remain safe because the car’s internal controls aren’t concerned about latency or connectivity. Meanwhile, the transportation service provider saves money on bandwidth and uses the cloud’s resources to maximize economy.
This is but one sector that IIoT and edge computing affects. As our world becomes increasingly connected, industries must develop new strategies to remain competitive. That’s why Phoenix Contact is committed to staying at the forefront of tomorrow’s innovations.
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