This post is in a series
Twelve unique characteristics of IoT based Predictive
analytics/machine learning .
I will be exploring these ideas in the Data
Science for IoT course /certification program when
Here, we discuss IoT devices and the nature of IoT data
Definitions and terminology
makes some bold predictions for IoT devices
The Internet of Things will be the largest device market in the
By 2019 it will be more than double the size of the smartphone,
PC, tablet, connected car, and the wearable market combined.
The IoT will result in $1.7 trillion in value added to the
global economy in 2019.
Device shipments will reach 6.7 billion in 2019 for a five-year
CAGR of 61%.
The enterprise sector will lead the IoT, accounting for 46% of
device shipments this year, but that share will decline as the
government and home sectors gain momentum.
The main benefit of growth in the IoT will be increased
efficiency and lower costs.
The IoT promises increased efficiency within the home, city, and
workplace by giving control to the user.
say internet things investment will run 140bn next five
Also, the term IoT has
many definitions – but it’s important to remember that IoT is
not the same as M2M (machine to machine). M2M is a telecoms term
which implies that there is a radio (cellular) at one or both ends
of the communication. On the other hand, IOT means simply
connecting to the Internet. When we are speaking of IoT(billions of
devices) – we are really referring to Smart objects. So, what makes
an Object Smart?
What makes an object smart?
Back in 2010, the then Chinese Premier
Wen Jiabo once said “Internet + Internet of things =
Wisdom of the earth”. Indeed the Internet of Things revolution
promises to transform many domains .. As the term Internet of
Things implies (IOT) – IOT is about Smart objects
For an object (say a chair) to be ‘smart’ it must have three
- An Identity (to be
uniquely identifiable – via iPv6)
- A communication
mechanism(i.e. a radio) and
- A set of sensors /
For example – the chair may have a pressure sensor indicating
that it is occupied
Now, if it is able to know who is sitting – it could co-relate
more data by connecting to the person’s profile
If it is in a cafe, whole new data sets can be co-related (about
the venue, about who else is there etc)
Thus, IOT is all about Data ..
How will Smart objects communicate?
How will billions of devices communicate? Primarily through the
ISM band and
Bluetooth 4.0 /
Bluetooth low energy. Certainly not through the cellular
network (Hence the above distinction between M2M and IoT is
important). Cellular will play a role in connectivity and there
will be many successful applications / connectivity models (ex
Jasper wireless). A more
likely scenario is
IoT specific networks like Sigfox (which could be deployed by
anyone including Telecom Operators). Sigfox currently uses
the most popular European ISM band on 868MHz (as defined by ETSI
and CEPT), along with 902MHz in the USA (as defined by the FCC),
depending on specific regional regulations.
Smart objects will generate a lot of Data ..
Understanding the nature of IoT data
In the ultimate vision of IoT, Things are identifiable,
autonomous, and self-configurable. Objects communicate among
themselves and interact with the environment. Objects can sense,
actuate and predictively react to events
Billions of devices will create massive volume of streaming and
geographically-dispersed data. This data will often need real-time
responses. There are primarily two modes of IoT data: periodic
observations/monitoring or abnormal event reporting. Periodic
observations present demands due to their high volumes and storage
overheads. Events on the other hand are one-off but need a rapid
reponse. If we consider video data(ex from survillance cameras) as
IoT Data, we have some additional characteristics.
Thus, our goal is to understand the implications of predictive
analytics to IoT data. This ultimately entails using IoT data to
make better decisions.
I will be exploring these ideas in the
Data Science for IoT course /certification program
when it’s launched. Comments welcome. In the next part of
this series, I will explore Time Series data