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4. Using Edge and Cloud computing strategically to optimise the company’s IIoT operations will not only enhance productivity but will also add more layers of security to the operations all the while making data processing extremely fast and efficient.
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The Fourth Industrial Revolution has brought the Internet of Things (IoT) to the frontline of
manufacturing and technological advancement. Termed as “Industrial Internet of Things (IIoT)”
in this context, the system allows industrial microcosms to communicate and function as a whole
via the Internet. Using the IIoT, companies can now automate their entire business process, from
manufacturing to logistics, to enhance productivity and efficiency, and reduce expenditure across
several verticals. Some of the most popular technologies that constitute the IIoT are Cloud
Computing, Machine Learning, Artificial Intelligence, Deep Learning, and Smart Manufacturing.
Companies use Cloud, a network of remote servers hosted on the Internet to store, manage, and
process data, rather than a local server or a personal computer, to add the IoT functionality to
their industrial systems. IIoT devices generate a massive amount of data each day, which has
resulted in the rise in Cloud traffic and increased pressure on the Internet architecture. This
unintended consequence may make Cloud servers unmanageable in the future. Developers are
figuring out ways to create a better alternative to automate and connect the industry. One such
way or paradigm is Edge Computing.
It is important to note that the alternate is not a replacement for Cloud as a whole. Edge and
Cloud are designed for different niches, but the latter is being used more prominently due to the
lack of a viable alternative, until now. Edge is designed for handling the instantaneous decision
making IIoT needs, whereas Cloud is more suited for Big Data analytics and other high-volume
applications. Edge can also be seen as a way to optimise Cloud computing systems.
Difference between Edge & Cloud
Edge Computing is a distributed Information Technology (IT) architecture in which data is
processed at the periphery of the network, close to the source of origin. In other words, instead of
sending data to the Cloud server for processing, the Edge device does it through a local gateway
device. This results in faster analytics and reduced network pressure.
For IoT, the goal is to process near the device for immediate response and subsequent decision
making. And the goal becomes even more pronounced for applications using generated data in
algorithms that use machine learning to make autonomous decisions. This is where Edge comes
to the fore. Edge processes data at the near the device and retrieves responses quickly, and is
especially useful in applications that are sensitive to a timeout response.
Edge is useful especially for applications where network access is limited or irregular. For
instance, a manufacturing production line uses the IoT. Edge devices will capture real-time
information about the production process and process it internally to optimise production.
Cloud Computing is useful in applications that aren’t as sensitive to a timeout response or
applications that don’t require processing power. The computing model increases the efficiency
of everyday tasks and provides a pathway for the massive amounts of data to travel. Cloud
allows for large amounts of data to be stored and processed.
Edge Computing is more useful in tasks where the Edge device already has data processing
capabilities and only needs to quickly process smaller amounts of data in response to
manufacturing parameters whose limits may or may not be defined. However, Edge should not
be used for inventory control information as processing it at the periphery of the network would
result in an unsafe and uncontrollable disarray of data.
Balancing Cloud on the Edge
While deciding what system to use for automating their manufacturing or industrial unit, CIOs
need to strike a balance between Cloud and Edge. Although Edge isn’t a replacement for Cloud,
using them interchangeably in an interconnected environment can pose security hazards and
cause the lowering of productivity.
To use the two systems synergistically, the analytical algorithm may be produced via Cloud
Computing, and then pushed to the Edge device for processing and analysing in short bursts.
This is especially useful when the device itself is incapable of performing analysis.
To paraphrase, use Edge where time is important, and Cloud where data security and volume are.
To make the most of their interconnected and automated industrial ecosystems and optimise the
processing power of their IoT systems, it is recommended that CIOs use layers of strategically
stacked Edge and Cloud systems.