discovering data, and visualizing it in smart
ways to make it easy for the decision-making
process to take place without delay.
Time series accuracy of data: Keeping the
level of confidence in data collected high with
high accuracy and integrity of data.
Predictive and advance analytics: Making
decisions based on data collected, discovered
and analyzed.
Real-Time geospatial and location
(logistical data): Maintaining the flow of data
smoothly and under control.
Standardization battle will continue
Standardization is one of the biggest
challenges facing growth of Io T—it’s a battle
among industry leaders who would like to
dominate the market at an early stage. Digital
assistant devices, including HomePod, Alexa,
and Google Assistant, are the future hubs
for the next phase of smart devices, and
companies are trying to establish “their hubs”
with consumers, to make it easier for them to
keep adding devices with less struggle and no
frustrations.
But what we have now is a case of
fragmentation, without a strong push by
organizations like IEEE or government
regulations to have common standards for Io T
devices.
One possible solution is to have a limited
number of devices dominating the market,
allowing customers to select one and stick to it
for any additional connected devices, similar to
the case of operating systems we have now have
with Windows, Mac and Linux for example,
where there are no cross-platform standards.
To understand the difficulty of
standardization, we need to deal with all
three categories in the standardization
process: Platform, Connectivity, and
Applications. In the case of platform, we
deal with UX/UI and analytic tools, while
connectivity deals with customer’s contact
points with devices, and last, applications are
the home of the applications which control,
collect and analyze data.
All three categories are inter-related and
we need them all, missing one will break that
model and stall the standardization process.
The need for more Io T skilled staff is rising,
including a growing need for those with AI,
big data analytics and blockchain skills.
Universities cannot keep up with the
demand, so to deal with such shortage,
companies have established internal training
programs to build their own teams, upgrading
the skills of their own engineering teams and
training new talents. This trend will continue,
representing an opportunity for new engineers
and a challenge for companies.
Ahmed Banafa has extensive experience
in research, operations and management,
with a focus on Io T, blockchain and AI. He
served as faculty at several at universities and
colleges, including the University of California,
Berkeley; California State University-East Bay;
San Jose State University; and University of
Massachusetts. He is the recipient of several
awards, including Distinguished Tenured Staff
Award of 2013, Instructor of the year for 2013,
2014 and Certificate of Honor for Instructor
from the city of San Francisco.
