Copyright © 2020 PhdAssistance. All rights reserved 1

Role of Machine Learning (ML) Techniques in IOT

Dr. Nancy Agens, Head,

Technical Operations, Phdassistance

info@phdassistance.com

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