As the Industrial Internet of Things marches forever closer, companies are looking at tangible ways to their improve their workflow, productivity and efficiency.
However, unsurprisingly, businesses are not adopting radical internal re-engineering as a reaction to the IIoT – it’s simply too disruptive and expensive to throw out the old and embrace the new.
Companies actively engaged in the Industry 4.0 vision are taking a far more conservative and gradual approach.
While commentators evangelise the Brave New World that the IIoT will deliver, those in industry are asking “what steps can we take while we wait for this new reality to become commonplace?”
The Unconnected
Billions of industrial machines lay strewn all over the globe in factories and production facilities. These behemoths, never designed to “talk” to each other, have proudly stood alone for decades.
But the times they are a-changing. While our domestic world becomes more and more connected, the world of industry remains for the most part, fragmented and discrete. So what is to be done?
A tidal wave of IoT data threatens to swamp business operations
Connection of these standalone machines and equipment to the Cloud is the first priority and also it seems, the first problem.
The blanket connection of billions of industrial machines to the Cloud would generate a massive volume and variety of data. It is estimated that around 20 Billion devices will be online by 2020. These devices will generate several exabytes of data every single day. How can one business successfully manage and analyze this unprecedented amount of data with its existing resources?
Of course, moving this data from these previous standalone machines to the Cloud will also require vast amounts of bandwidth.
In this case, a way of regulating the amount of data would make this more manageable for the business in question. Additionally, evaluating which data is useful and which can be discarded would also help with operational practicalities.
Survival of the fittest defined by the quickest
The second problem is the agility of a business to respond this data. In any market, responsiveness is a key component to success. Faster response time can improve output, boost service levels, increase safety and reduce maintenance requirements, and urgent action can be taken for mission-critical events that need immediate attention to prevent costly downtime or catastrophic failure.
The traditional Cloud model would have this data sent to the Cloud and processing would occur minutes, perhaps hours later. This is far too late.
Enter a solution from the Cloud Edge.
But Clouds don’t have edges
Forget soft, fluffy clouds, the Industrial Internet of Things Cloud has a very definite edge and one where much activity is occurring. The term Cloud Edge merely indicates a near proximity to the Cloud rather than being within it.
The IoT Cloud Edge is also referred to as “the fog” with the processing of data at the edge of the Cloud often described as Fog Computing, Edge Computing or Edge Processing.
A middle man between machine and IoT Cloud
By placing an intermediary device between the industrial machine and the Cloud, one immediately solves the two problems outlined above.
The device has the capability perform calculations on the data it receives, and it is this ability to “do Math” that frees a business from data avalanche.
Functions can be written into the device so only vital and meaningful data is filtered to its final destination in the Cloud, thus solving our first problem.
Additionally the device can be programmed to respond to results of data immediately it falls outside normal parameters. Alarms can be triggered, pumps turned off, email alerts sent to tech staff and any other corrective action required. By siting the device close to the industrial machine, all this is enacted with minimal latency. A feature vital for almost any industrial business.
Beyond the event itself lies the ability to access and study the data in the lead up to the event, offering opportunities to fault detection, improve maintenance and service cycles, and other efficiencies of a reactive workflow. On an even longer timeline, there is also scope for identifying patterns within bigger data sets.
Another important benefit of locating the device near the machine is of security. Analyzing data close to where it is collected means sensitive data is kept inside the network. IT teams can monitor this as they would any other part of their IT environment and in line with existing company cyber-security policies.
First steps to connected ecosystem inside industrial businesses
All this makes Cloud Edge Processing a viable bridging step for almost all industrial companies. It is straightforward to implement, it is affordable and can be managed by an existing workforce. Additionally, it will provide good data and is scalable in the future.
In Summary
Processing data at the Cloud Edge (i.e. before it is transmitted) holds the most promise for industrial companies as they transition to becoming fully IIoT equipped.
IoT Gateways that support Cloud Edge Processing provide transitioning businesses with the ability to stem the flow of data to a manageable and useable amount.
In turn bandwidth costs would be reduced
Cloud Edge Processing has the following advantages:
Allows response to data at the Cloud edge (i.e. before it is transmitted to the Cloud)
Provides faster (often real time) response to this data
Can move specific data to other locations or systems
Sends only meaningful data to the Cloud
Reduces security issuesWhy Cloud Edge Processing is the future of the Industrial Internet of Things (IIoT)
