Copyright © 2020 PhdAssistance. All rights reserved 1
Role of Machine Learning (ML) Techniques in IOT
Dr. Nancy Agens, Head,
Technical Operations, Phdassistance
Keywords: Machine learning algorithm,
Internet of Thing (IoT), Artificial-
Intelligence Smart home, smart car, smart
coffee maker
I. INTRODUCTION
According to market Research firm
(Gartner, 2017), there will be 7.6 billion
internet connected devices in businesses by
2020. It does not include the ubiquitous
applications of IoT in the consumer space.
Clive Humby, chief data scientist at Tesco
claims that Industrial IoT could rake in US
$ 14 trillion by 2030 (Rhena Helmus, 2019).
The volume of data generated by IoT
devices is tempting more and more
companies to jump into the bandwagon. But
the idea is still in infancy as companies need
to figure out how to put the data to good use.
Companies are exploring ways of moving
IoT strategies from proof-of-concept to
deployment. Since the social consequences
of IoT have not been fully understood it is
crucial for corporates to implement IoT
strategies systematically. Machine
Learning is indispensable while trying to
incorporate IoT strategies.
Let us take the simple case of
creating a smart home. You return from
office everyday at a particular time and are
habituated to having coffee after reaching
home. Your smart coffee maker prepares
coffee at the time you normally return
everyday. The process would be upset if you
returned late on a given day (Grosch, 2018).
As further enhancement, the IoT connects
your car and smart coffee maker with the
internet. A delay due to traffic congestion is
relayed to the smart coffee maker which
suspends activity until you return. There
could be days when you did not want coffee
as you wanted to go to bed early due to
exhaustion. Your health watch could
transmit biological information to the coffee
maker giving it indications of your current
state. The Machine Learning (ML)
Algorithms then take a decision whether to
prepare coffee or not. Such Machine
Learning algorithms is applied to the
different variables scenario which get
trained and able to predict outcomes with
increasing accuracy. The iterations gets
better with time.
Machine Learning models interact
with their environment and learn from the
outcomes in what is known as reinforcement
learning. The importance of IoT and ML can
be understood from the fact that Uber would
rather be seen as a Technology company
than a transportation company (Kosaraju,
2020). It’s app connects the passenger
looking for a ride and the driver nearby who
is willing to take it. Uber’s business model
has been built around data enabled by IoT.
An area where IoT and ML is
projected to find increased acceptance is in
predictive maintenance. Siemens’ “internet
of trains” gathers data from sensors placed
on tracks and applies ML models to predict
the possibilities of failures. It has improved
reliability of rail vehicles by upto 99% in the
German railways. Technologies such as face
recognition are projected to turn cities smart.
Zhengzhou is the latest Chinese city to roll
out face recognition cameras in its airports,
malls and public places. Currently, this is
the most complex yet controversial
adaptation of IoT and ML due to privacy
reasons.
Copyright © 2020 PhdAssistance. All rights reserved 2
Industrial IoT, though tipped for a
big leap has not translated into good return
on investment for companies. American
farm equipment manufacturer, John Deere,
has implemented AI and ML technologies
such as computer vision to spray herbicides
only in areas with weeds. However, cost of
rolling out computation-heavy algorithms
will restrict its application in rural areas.
Also, most companies are still in the
exploratory phase and do not possess the in-
house capabilities needed to implement such
strategies.
IoT has enabled optimal utilization
of energy in malls by collecting data on
weather, energy consumption and energy
prices and then calculating the most cost-
effective solution to use energy generated in
the mall.
IoT is not always about cost
reduction. It finds application for
convenience as well. Switching on lights by
issuing voice commands may not be
efficient but it is convenient. Smart cities
use data collected on air quality and weather
to offer solutions in real-time such as
offering cheap public transport in places
predicted for traffic congestion.
According to IDC,(2019), video
surveillance will account for the largest
share of IoT data with industrial and
automotive category seeing the fastest data
growth rates. Many firms are still sitting on
huge volumes of data unable to turn them
into actionable Information. IoT metadata
is also a growing segment. Metadata is data
that is created based on other IoT data files.
They can be used for Developing
Intelligent systems, drive personalization or
bring context to random scenarios. You can
read about IDC’s forecast of the number of
IoT “things” that are connected as well as
data generated by them here.
In the long run, organizations must
be prepared to manage change in order to
incorporate and use IoT and ML for value
generation. To effect a digital transformation,
organizations must eveolve new methods of
hiring and training manpower that can
leverage this potential. Few companies
possess the scale, skill sets and tech
expertise for implementation of successful
IoT strategies and they will scout for
partners with complimentary capabilities.
REFERENCES
[1] Gartner. (2017). Newsroom: Gartner Says 8.4 Billion
Connected “Things” Will Be in Use in 2017, Up 31
Percent From 2016. Retrieved November 27, 2018,
from https://www.gartner.com/en/newsroom/press-
releases/2017-02-07-gartner-says-8-billion-
connected-things-will-be-in-use-in-2017-up-31-
percent-from-2016
[2] Grosch, K. (2018). John Deere – Bringing AI to
Agriculture. Retrieved from
https://digital.hbs.edu/platform-
rctom/submission/john-deere-bringing-ai-to-
agriculture/
[3] IDC. (2019). The Growth in Connected IoT Devices Is
Expected to Generate 79.4ZB of Data in 2025,
According to a New IDC Forecast. Retrieved
February 25, 2020, from
https://www.idc.com/getdoc.jsp?containerId=prUS4
5213219
[4] Kosaraju, R. (2020). IoT & Uber- Wait and Watch.
Retrieved from
https://www.theinternetofthings.eu/sites/default/files/
%5Buser-name%5D/IoT and Uber-Wait and
Watch.pdf
[5] Rhena Helmus. (2019). Turning The Internet Of Things
Into Reality. Siemens Iot Services. Retrieved from
https://assets.new.siemens.com/siemens/assets/publi
c.1556633115.131ac2f9-5e8b-4968-ba2f-
734eefccdb50.turning-iot-into-reality-whitepaper-
by-siemens-iot-services-fina.pdf
