SOFTWARE & SYSTEMS
BENEFITS OF EDGE COMPUTING
NOVOTEK
HOW EDGE COMPUTING IS BECOMING
CENTRAL TO A NEW EFFICIENCY REVOLUTION
In the age of the industrial internet of
things (IIoT), the speed of data analysis is
key to effective operation. Edge computing
accelerates this process, allowing for
industrial data analysis to be performed at
the point of collection. Here, George Walker,
managing director of industrial control
and automation provider Novotek UK and
Ireland, explains the core benefits of edge
computing.
Edge computing is the term for when
process data is collected, processed and
analysed in a local device, as opposed to
being transmitted to a centralised system.
Supported by local cloud networks and
IIoT platforms like GE Digital’s Predix,
systems that support edge computing are
proving increasingly popular as a means
of streamlining the effectiveness of IIoT
networks.
For plant and utility managers, this presents
a range of opportunities to not only
improve the efficiency of operations, but
to also overcome some of the limitations of
centralised IIoT networks. In fact, there are
the three main ways that edge computing
drives value in businesses.
GREATER OPERATIONAL
EFFICIENCY
Traditional analysis is undergone by
transferring data externally, which can delay
decision-making as errors take longer to
be found. With edge computing capable
systems, large parts of the analysis can be
carried out by the devices collecting the
data.
The benefits of this are two-fold. For one, this
can allow plant managers to access partial
deep analysis in real time without waiting on
lengthy analysis to be carried out externally.
This means action can be taken earlier,
streamlining the decision-making process.
The second benefit is that the IIoT platform,
such as GE digitals Predix, can automatically
respond to operational data. The system will
be able to automatically adjust processes in
real-time. In effect, this would allow for a self-
correcting system that is able to maximise
uptime and reduce the need for manual
maintenance.
OVERCOMING NETWORK
LATENCY AND BOTTLENECKS
Traditionally, data analysis is carried out by
having smart sensors send all their data to
a remote location where it is analysed and
processed. This is data intensive and can
create problems if a network is not robust
enough.
Channelling large amounts can cause network
latency, which interrupts working within the
plant as there will be a delay with transferring
messages that run through the same network.
This is particularly problematic for
applications where a system needs to act
rapidly to a problem, such as in an industrial
oven control system in a food production
plant, where even a temporary dip in the
temperature can result in a batch being
unsuitable for market.
In addition to this, the sheer volume of raw
data that can be generated in an industrial
or utility plant is also likely to cause data
bottlenecks in the wider network.
By using edge computing systems and a
machine-learning IIoT platform, systems can
respond to changes in real-time to prevent
problems, while also having edge computers
in place to compress the data and reduce
network impact.
LOWER OPERATING COSTS
Due to the amount of information being
produced, the cost of data storage
is becoming a growing concern for
companies. Edge computing and its ability
to process data without transmitting it
lightens the load put on the network.
Processed data is also less substantial than
raw data as calculations can be made that
allow the raw data to be compressed,
thus reducing file sizes. As such, industrial
companies are able to make more
economical use of their cloud servers. By
minimising storage requirements and the
number of storage upgrades required, edge
computing can allow for a lower overall
operating cost.
It’s clear that there are many benefits to
edge computing, both from a financial and
operational perspective. Whether a business
is still considering adopting IIoT technology
or is already making use of such systems,
edge computing marks a step forward for
businesses looking to streamline processes
for efficiency and effectiveness.
www.novotek.com/uk/
Issue 36 PECM
135