3. AI and Cloud Computing:
This is the brain of IoT and provides the most value in the IoT technology stack.
Enterprises can now not only reduce IT costs by moving their IT infrastructure
to the cloud, but they can also leverage IoT analytics platforms from Microsoft,
Amazon, Google or IBM, to extract economic value out of the IoT data gath-
ered. For example, in the smart building arena, an intelligent IoT platform using
machine learning/AI can better manage heating, cooling and room-booking
systems by analyzing the usage data on the building.
AI, however, is still in its early adoption stage. We have solved the easier AI
problems, such as image pattern recognition, text-to-talk, etc., but the hard
problems related to semantics remain to be tackled. For example, having a
two-way meaningful dialogue with voice-enabled devices in a random context
is still a challenge.
Use Case: T-Mobile’s IoT Service – eSIM (Embedded
Subscriber Identity Module) for the Fleet
Management Industry
At T-Mobile I led a cross-functional team that implemented a large-scale,
global IoT service called eSIM for the fleet management industry. It works like
this. A trucking company, for example, that has hundreds of rigs traveling fre-
quently between the U.S. and Canada often needs to track their locations,
communicate with the drivers and perform vehicle diagnostics. A single truck
might easily use 100 MB of data per month while traveling in Canada. The
roaming cost for that truck alone could be as much as $200 per month, or
nearly $2,400 per year. T-Mobile’s eSIM card provides a powerful edge com-
puting capability, enabled by backend cloud computing, that can automatically
swap the default wireless data plan to a local carrier’s plan without incurring
roaming charges—as soon as the truck crosses the Canadian border.
Multiple technologies were used:
• Device layer: the IoT device has T-Mobile’s SIM card inside, provisioned
with international data plans.
• Network: T-Mobile’s LTE network and Canadian Wireless Carrier Rogers’
LTE network are used.
• Analytics and control: T-Mobile partners with backend/cloud software
providers that provide all the logic in detecting truck location and exe-
cuting the carrier profile swap. They can also provide machine learning
analytics as needed.
Companies can build eSIM into an assortment of connected product catego-
ries, including mobile health and wearables, smart connected machines, con-
nected cars and connected medical services among others.
52 | THE DOPPLER | FALL 2018