Accelerating Performance with the Artificial Intelligence of Things
Figure 4: Industrial AI-driven IoT Applications
the data source as possible – to the
edge.
Bringing Advanced Analytics to the Edge
Where analysis of IoT data takes place
depends on issues of bandwidth and latency:
With AI-powered capabilities, IoT data can
be transformed, analyzed, visualized and
embedded across the entire ecosystem –
edge devices, gateways and data centers, in
the fog or in the cloud.
For applications that can tolerate
some delay or are not bandwidth-
intensive, such as collecting
summary data of a device’s
operation, the IoT device sends data
to the cloud or data center, which
analyzes it in light of historical
performance and other trends.
Insights gained from the analysis can
then be used to make decisions on
subsequent operation of the device,
including modifying the control
program on the device itself.
For cases where mobile or remote
assets churn out lots of data that
must be analyzed quickly – such as
self-driving vehicles or drones – or
where bandwidth is constrained,
data processing is moved as close to
IIC Journal of Innovation
AI O T IN A CTION
IoT Data with AI Reduces Downtime Helps
Truckers Keep on Trucking
Millions of trucks transport fuel, produce,
electronics and other essentials across
highways every day. But unplanned
downtime can exact a tremendous toll on
any fleet operator and their customers who
depend on timely deliveries. Volvo Trucks
and Mack Trucks, subsidiaries of the Swedish
Manufacturer AB Volvo, have met this
challenge through remote diagnostic and
preventative maintenance services based on
IoT technologies with advanced analytics
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