The Multifaceted Utility of the Intelligent Internet of Things

The Multifaceted Utility of the Intelligent Internet of Things

The Multifaceted Utility of the Intelligent Internet of Things

By Jelani Harper

17 July 2019


Manifestations of Artificial Intelligence have long been projected to
revolutionize the Internet of Things, culminating in an Intelligent Internet of
Things that maximizes business value for each of these prominent applications
of the data sphere.

Actually seeing the impact of the
IIoT in contemporary use cases
only confirms this fact, alluding to the
future of these deployments for the enterprise in general.

According to Cybera
Chief Marketing Officer Bethany Allee, “To remain competitive, retailers are
now required to integrate IoT into their storefronts. And that’s everything
from [gas] pumps…and things like outdoor payments, to also things like network
enabled coffee pots.”

The combination of different aspects of AI with the type of secure network
connectivity required for consistently capitalizing on the IoT results in
optimal cloud computing analytics, heightened regulatory compliance
capabilities, and decreased incidence and degree of fraud. In many of these instances
“those things have some element of expert systems and machine learning in those
apps,” remarked Cybera President Cliff Duffey.

Cloud Analytics

Whether at the edge or in centralized locations, cloud computing is the foundation of the benefits delivered by the IIoT. As such, the connectivity required for secure deployments of cloud-based AI applications is critical from both a safety and consistency standpoint. Moreover, that connectivity has become as distributed as the devices in the IoT themselves. Duffey mentioned that in the oil and gas industry, for example, “those devices used to have real simple connectivity, just to some in-store systems. Now, they have to be connected up to the internet as well.” The intricacy of that connectivity, and the ensuing security necessary to safeguard it, is exemplified in a Shell use case in which “that network is supporting all of the new devices that need to be connected to the internet like the fuel dispensers,” Duffey noted. “We also have to do the network connectivity for the dispensers to the in-store point of sale and then several internet destinations.”


Related: 2019 Trends In The Internet of Things


By spinning up the relevant data from such deployments to the cloud,
organizations can perform cognitive computing analytics for striking business
results including “tracking and monitoring fuel data, as well as cartels and so
on unique to fuel,” Duffey said. Thus, “one of the things that a couple of different
cloud companies do is they’ll gather the data on how much fuel has been sold
and then what are the fuel levels in the ground,” Duffey commented. “If there’s
a delta of 50 gallons, then something happened. It didn’t just disappear or
evaporate.”

Regulatory Realities

In this use case, deployments of cloud-based cognitive analytics are
instrumental to remaining regulatory compliant. In the preceding situation
Duffey referenced, these analytics are critical to determining “conclusions
like maybe there’s an underground leak in a tank, which for EPA and
environmental reasons is a really big problem,” Duffey noted. The reality is
that companies must monitor such data—and leverage it to prevent any wasteful,
noxious occurrences like those Duffey described—for regulations
at a variety of levels
spanning federal and statewide jurisdiction.

In the oil and gas industry, “a lot of the various states in the United States
require that there be daily or weekly checking of the fuel levels to determine
whether there’s an environmental leak,” Duffey revealed. Machine learning and
other AI analytics on this IIoT data underpin “cloud applications that can
monitor in real time to notice if there’s a potential leak quicker,” Duffey
said. Moreover, the Shell use case involves connecting IIoT devices with an
outdoor Europay, Mastercard, and Visa (EMV) network, which enables the company
to expedite adherence to 2020’s outdoor EMV chip card compliance deadline.

Fraud Prevention

Another defining attribute of the IIoT is the robust, reliable connectivity
required to feed intelligent algorithms in the cloud. The Shell use case
typifies this fact because the aforementioned outdoor EMV network it recently
implemented supports chip payments at fuel dispensers, which is still somewhat
novel within this industry. Chip payments were designed to prevent fraudulent
credit card use. Whereas the magnetic strips of these cards are easily copied
from one another for conventional credit card fraud (which is well suited for
use at unmanned fuel pumps), it’s much more exacting to copy chips—which
inherently reduces
fraudulent activity
.

The crux of this protection is “if you’re going to read the chip based
information, you have to be able to communicate that immediately,” Duffey
explained. “If the network is down, the dispenser simply can’t pump fuel
anymore.” Developments in wireless communication such as 5G or even 4G Long
Term Evolution (LTE) connectivity fortify such use cases so that “even if the
primary internet connection goes down, we can send the chip based payment over
a wireless connection through a partner like Verizon,” Duffey acknowledged.

Here to Stay

The Shell use case and other examples throughout the oil and gas industry evince the IIoT’s vitality to the enterprise. It’s a means of leveraging cloud-based analytics to produce business value, ensure regulatory adherence, and prevent common forms of risk like fraud. It requires stark, ongoing connectivity—and even a few timely backup plans to ensure it.


Jelani Harper is an editorial consultant servicing the information technology market, specializing in data-driven applications focused on semantic technologies, data governance and analytics.