Opto 22 all products Sales Brochure

Building Industrial IoT from

Edge to Cloud

Next-generation distributed I/O and

control open new possibilities for

connected infrastructure

Opto 22

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Phone: [phone] or 951-695-3000

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BUILDING INDUSTRIAL IOT FROM EDGE TO CLOUD

Next-generation distributed I/O and control open new possibilities for
connected infrastructure

WHAT’S THE GOAL?

By now, most anyone working in a role involving industrial
automation has heard about digital transformation, the
internet of things (IoT), or the industrial IoT (IIoT). These
initiatives involve ever smarter devices communicating
progressively closer to the “edge,” perhaps connected to an
internet “cloud,” or even through some kind of
intermediate “fog.”

Even if we consolidate these terms under the umbrella of
IIoT, for most folks a simple question remains: what is the
goal of the IIoT?

Simply put, end users would like the IIoT to create a
cohesive system of devices and applications able to share
data seamlessly across machines, sites, and the enterprise
to help them optimize production and discover new
cost-saving opportunities.

Sharing process data has long been a goal of industrial
automation, but traditional operational technology (OT)
architectures are poor at scaling, priced prohibitively, and
demand complex configuration and support.

So what is changing to achieve this new, more ambitious
goal?

Much as consumer hardware and software technologies
have shifted to improve ease-of-use and connectivity,
industrial products and methods are following the same
trend. By adopting information technology (IT) capabilities,
they are making it easier to connect industrial equipment
with computer networks, software, and services, both on
premises and in the cloud.

This white paper discusses how a more distributed global
architecture is enabling connectivity from the field to the
cloud for sensors and actuators, and for the input/output
(I/O) systems and controllers linked to them.

UP AND DOWN THE ARCHITECTURE

Industrial automation architectures generally address data
processing from a hierarchical perspective, as with the

classic Purdue model. One good feature of this hierarchy is

the clarity it provides with regard to where data can
originate, be stored, undergo processing, and be delivered.

However, the task of transporting data and processing it in
context is often quite difficult, because so many layers of
equipment are required to connect devices and
applications.

For example, the illustration below shows a traditional
method of acquiring temperature data from facility

Traditional data acquisition methods require configuring and maintaining many

layers in a hierarchy of hardware and software.

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